Growth chamberMultimodalStereoRootStem / branchTrackingGrowth / development / phenology
Understanding plant behaviour requires the integration of multiple phenotypic and physiological signals measured over time under controlled conditions. However, different plant signals are typically studied using separate experimental setups, limiting temporal alignment and integrative analyses.We present Mind(the)Plant, a modular experimental facility designed for the synchronized, long-term acquisition of multimodal plant data, including three-dimensional shoot kinematics, above- and below-ground volatile organic compounds (VOCs) and root imaging. Its modular architecture is designed to accommodate additional acquisition modules, such as electrophysiological signalling, as future extensions. The platform integrates a controlled growth environment with stereovision imaging, high-resolution time-of-flight mass spectrometry and custom rhizocameras. These components are connected through a unified network infrastructure that ensures synchronized acquisition and centralized data handling.We validate the performance of each acquisition module through multi-week recordings, demonstrating high-temporal stability, reliable stereovision synchronization, effective isolation of VOCs signals and robust operation of below-ground imaging. We further illustrate the analytical potential of the platform using a one-day continuous multimodal acquisition combining shoot kinematics, above-ground VOC emissions, rhizocameras observations and environmental data.Mind(the)Plant provides a novel methodological framework for studying plant behaviour, signalling and phenotypic plasticity in ecological and evolutionary research. By enabling coordinated measurements of multiple plant response modalities, the platform supports investigations of dynamic plant-environment and plant-plant interactions from a behavioural perspective.
Why it matches plant phenotyping methods植物の複数の表現型・生理シグナルを同期取得する施設を開発し、各取得モジュールの性能を検証しているため、表現型計測プラットフォームが研究の中心です。
abstractWe present Mind(the)Plant, a modular experimental facility designed for the synchronized, long-term acquisition of multimodal plant data, including three-dimensional shoot kinematics, above- and below-ground volatile organic compounds (VOCs) and root imaging.
Reproduction assets foundThe paper's data availability statement explicitly deposits data, code and processing pipelines (supporting the multimodal plant phenotyping measurements and analysis) in a public Zenodo archive with an authors' URL matching an allowed URL.Code · publicf Interest Statement
The authors have no conflicts of interest to declare.
Peer Review
The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/2041-210x.70411 .
Data availability Statement
Data, code and processing pipelines supporting this study are available at https://doi.org/10.5281/zenodo.22095454 ( Simonetti & Castiello, 2026 ).
References
Avesani S, Bonato B, Simonetti V, Guerra S, Ravazzolo L, Gjinaj G, Dadda M, Castiello U. Comparing proton transfer reaction (PTR) and adduct ionization mechanism (AIM) for the study of volatile organic compounds. Molecules. 2026;31(3):402. doi: 10.3390/molecules31030402.
Baluška F, LeOpen asset ↗zenodo · 10.5281/zenodo.22095454lines:482-508Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 15 Sept 2026
Field / plotMultimodalNeRF / 3D Gaussian SplattingLiDAR / point cloudRGB / grayscaleStereoFruitWhole plant / canopy / plot / fieldMorphology / geometry measurementObject detection
Binocular stereo vision is a low-cost and scalable 3D perception technology that shows strong potential in agricultural phenotyping and smart agriculture. By estimating depth from multi-view RGB images, it enables non-contact, high-precision sensing of crop structure, canopy morphology, growth dynamics, and livestock traits, providing essential support for digital and intelligent agricultural production. With recent advances in deep learning-based stereo matching, multimodal sensor fusion, and 3D reconstruction, its robustness and accuracy in complex field environments have been significantly improved. This paper systematically reviews recent progress in agricultural applications of binocular stereo vision, covering system architectures, traditional and deep learning-based stereo matching methods, point cloud reconstruction techniques, and emerging supervision strategies such as 3D Gaussian splatting. It further summarizes key applications, including high-throughput phenotyping, fruit localization and robotic harvesting, weed detection and precision spraying, autonomous navigation, and livestock body condition assessment, highlighting its role in multi-task agricultural perception systems. Finally, the paper discusses major challenges, including low-texture matching difficulty, occlusions in complex environments, cross-domain generalization, real-time lightweight deployment, and limited dataset availability. Future directions are outlined in foundation model-based visual perception, self- and weakly supervised learning, multimodal fusion, and edge-efficient model design, aiming to support large-scale deployment in smart agriculture.
Why it matches plant phenotyping methods農業における双眼ステレオビジョンのシステム、ステレオマッチング、3D再構成を体系的にレビューし、作物構造・群落形態・生育動態の非接触計測とハイスループット表現型解析を主要対象としているため。
abstractThis paper systematically reviews recent progress in agricultural applications of binocular stereo vision, covering system architectures, traditional and deep learning-based stereo matching methods, point cloud reconstruction techniques
Three-dimensional surface reconstruction is essential for accurately acquiring the external quality parameters of watermelons, such as size, volume, and defect area. Binocular stereo vision provides a low-cost and easily deployable solution for the single-view 3D reconstruction of watermelons. However, watermelons present highly similar surface textures, and as typical spheroid-like objects, the excessive angle between surface normals of edge regions and the camera optical axis leads to insufficient feature representation. Consequently, directly applying existing stereo matching algorithms often introduces matching ambiguities, and lightweight networks struggle to balance real-time performance with matching accuracy. This study focuses on the high-precision single-view point cloud generation of Kirin watermelons. To address these issues, we first construct a cross-modal, high-precision Kirin watermelon stereo matching dataset. Building upon the Fast-ACVNet+ architecture, we then propose MI-ACVNet, a lightweight stereo matching network tailored for high-precision watermelon point cloud acquisition. In the feature extraction stage, a Multi-Scale Stereo Feature Extraction (MSFE) module is adapted. By incorporating the re-parameterized network MobileOne and Epipolar-Enhanced Coordinate Attention (E2CA), MSFE improves the discriminative capability for weak and similar textures without compromising inference speed. For cost computation, a Coarse-to-Fine Cascaded Residual Correction (C2F-CRC) strategy is incorporated to construct a fine-grained cost volume via sub-pixel interpolation, enhancing the network’s ability to capture subtle surface fluctuations. Furthermore, a Semantics-Guided Region-Aware Loss (SGRA-Loss) is formulated, leveraging semantic masks to apply differentiated supervision weights across edge, center, and background regions to significantly improve edge matching accuracy. Ablation studies validate the effectiveness of the MSFE, C2F-CRC, and SGRA-Loss components. Compared to the baseline model, the full MI-ACVNet reduces the End-Point Error (EPE) by 19.5% and the Bad-0.5 error rate by 34.5% in the watermelon region. Furthermore, when compared against five mainstream algorithms (StereoNet, AANet, HSMNet, LightStereo-L, and NMRF-swint), MI-ACVNet achieves state-of-the-art performance: EPE and Bad-0.5 are reduced to 0.091 pixels and 1.159%, respectively, with a single-frame inference time of only 46 ms. The average depth error of the reconstructed point clouds is merely 0.26 mm. By ensuring both real-time efficiency and high-precision depth estimation, this method demonstrates promising potential for deployment in industrial Kirin watermelon sorting lines, driving sorting equipment toward higher precision and intelligence.
Why it matches plant phenotyping methodsスイカのサイズ・体積・欠陥面積などの外部形質を取得するためのステレオ画像再構成手法を開発し、データセット構築、アブレーション、既存手法比較で検証しているため、植物フェノタイピング手法が中心である。
abstractThree-dimensional surface reconstruction is essential for accurately acquiring the external quality parameters of watermelons, such as size, volume, and defect area.
GrapevineField / plotStereoWhole plant / canopy / plot / field
• A stereovision-controlled variable-rate sprayer was tested over three growth stages • Spray volume, canopy deposit, coverage, airborne and ground drift were assessed • Spray volume was up to 79.6% less than constant-rate spray application • Higher canopy deposit and coverage was achieved at middle and late growth stages • Growth stage strongly influenced spray volume, canopy deposit, and spray losses Optimizing spray application efficiency in 3D crops remains a major challenge due to the spatial and temporal variability of canopy structure. Variable-rate spray (VRS) technologies have emerged as a promising solution to address this limitation by adapting spray output to canopy characteristics. Stereo vision systems have recently gained attention as a cost-effective real-time canopy detection sensor. Despite encouraging results, previous research has been conducted with prototype sprayers. In this study, a commercial airblast sprayer retrofitted with a stereo vision controlled spray system was evaluated and compared with a constant-rate spray (CRS) application across three grapevine growth stages (BBCH 53, 57, and 77). Spray volume, canopy deposit, spray coverage, airborne drift, and ground drift were assessed. The VRS application reduced spray volume by 79.6% and 43.0% at the early and middle growth stages, respectively, whereas a 22.0% increase was observed at the late growth stage compared with CRS application. Despite the spray volume reduction at the early growth stage, canopy deposit and spray coverage decreased to a lesser extent. At the middle and late growth stages, canopy deposit and spray coverage were higher with VRS application than with CRS application. Airborne and ground drift were reduced by 29.3% and 48.9%, respectively, at the early growth stage. At the middle growth stage, airborne drift increased by 44.0% while ground losses decreased by 23.0%. At the late growth stage, airborne and ground drift increased by 30.9% and 55.9%, respectively. Despite promising results, further optimization of spray dose according to canopy development is required.
Why it matches plant phenotyping methodsステレオビジョンでブドウ樹冠の構造を検出し、その情報に基づく可変散布システムを技術評価している。樹冠への散布量・付着・被覆など植物状態に関する取得性能が中心で、単なる農薬試験ではない。
abstractStereo vision systems have recently gained attention as a cost-effective real-time canopy detection sensor.
To address the low efficiency of manual inspection for corn sowing quality, which is labor-intensive and time-consuming, this study proposes an automatic seedling-stage plant-spacing measurement method based on three-dimensional machine vision. A high-clearance mobile platform equipped with a ZED 2i stereo camera and an industrial computer was developed to acquire RGB images and depth information of corn seedlings in the field in real time. Using the YOLOv11-Pose model, plant keypoints were detected and localized; combined with camera calibration and 3D reconstruction techniques, inter-plant distances were computed automatically. A sowing-quality evaluation framework was then established to enable automated analysis of the Quality of Feed Index (QFI), Multiple Index (MUL), Miss Index (MI), and coefficient of variation. Experimental results indicate that the system operates effectively under three preset plant spacings (15 cm, 20 cm, and 25 cm), achieving a keypoint-detection mAP@0.5 of 0.990 and an mAP@0.5:0.95 of 0.989. In sowing-quality evaluation, the qualified indices produced by the system were 77.83%, 80.36%, and 82.46%, respectively, and the Quality of Feed Index (QFI), Multiple Index (MUL), Miss Index (MI), and coefficient of variation showed trends consistent with manual measurements. The proposed method enables efficient, nondestructive detection of seedling-stage plant spacing and sowing quality, providing reliable technical support for precision sowing and field management.
Why it matches plant phenotyping methods3Dマシンビジョン、姿勢検出、校正、3D再構成を組み合わせ、トウモロコシ個体間距離を自動推定・検証する手法が研究の中心である。
abstractthis study proposes an automatic seedling-stage plant-spacing measurement method based on three-dimensional machine vision.
Dense ground-truth disparity maps are practically unobtainable in forestry environments, where thin overlapping branches and complex canopy geometry defeat conventional depth sensors -- a critical bottleneck for training supervised stereo matching networks for autonomous UAV-based pruning. We present UE5-Forest, a photorealistic synthetic stereo dataset built entirely in Unreal Engine 5 (UE5). One hundred and fifteen photogrammetry-scanned trees from the Quixel Megascans library are placed in virtual scenes and captured by a simulated stereo rig whose intrinsics -- 63 mm baseline, 2.8 mm focal length, 3.84 mm sensor width -- replicate the ZED Mini camera mounted on our drone. Orbiting each tree at up to 2 m across three elevation bands (horizontal, +45 degrees, -45 degrees) yields 5,520 rectified 1920 x 1080 stereo pairs with pixel-perfect disparity labels. We provide a statistical characterisation of the dataset -- covering disparity distributions, scene diversity, and visual fidelity -- and a qualitative comparison with real-world Canterbury Tree Branches imagery that confirms the photorealistic quality and geometric plausibility of the rendered data. The dataset will be publicly released to provide the community with a ready-to-use benchmark and training resource for stereo-based forestry depth estimation.
Why it matches plant phenotyping methods樹木の枝・樹冠形状を対象とするステレオ深度推定データセットを開発し、画素単位の視差ラベルと実画像との比較検証を提供しており、植物構造の取得方法が中心である。
abstractWe present UE5-Forest, a photorealistic synthetic stereo dataset built entirely in Unreal Engine 5 (UE5).
Controlled-environment agriculture (CEA) and circular production systems require coordinated monitoring of biological and physicochemical processes across trophic levels. This project report presents the implementation of a multi-trophic controlled-environment agriculture demonstrator that integrates computer-vision-based monitoring with established sensor infrastructure for aquaculture, poultry, plants, microalgae, duckweed, and insect modules. Stereo imaging and RGB-D systems are deployed for non-invasive quantification of fish biomass and plant growth, while continuous water-quality and environmental measurements (e.g., pH, dissolved oxygen, nitrate, ammonium, temperature, CO2) provide complementary process data. These data streams are synchronized within a shared database architecture to enable cross-module evaluation of nutrient dynamics, growth progression, and operational stability under real facility conditions. The implemented framework demonstrates how computer vision can extend conventional sensor-based monitoring by directly capturing biological performance indicators across aquatic, terrestrial, and microbial domains. While advanced predictive modeling and full digital twin simulation remain future development steps, the realized data-integration architecture establishes a structural foundation for the systematic evaluation of circular indoor food-production systems. The demonstrator illustrates how multimodal monitoring can support nutrient recirculation, transparency of biological variability, and data-driven assessment within controlled multi-trophic environments.
Why it matches plant phenotyping methodsステレオ画像およびRGB-Dによる植物生長の非破壊定量と、データ統合型モニタリング基盤の実装が中心的に記述されており、植物フェノタイピング基盤として収載対象です。
abstractStereo imaging and RGB-D systems are deployed for non-invasive quantification of fish biomass and plant growth
Accurate per-branch 3D reconstruction is a prerequisite for autonomous UAV-based tree pruning; however, dense disparity maps from modern stereo matchers often remain too noisy for individual branch analysis in complex forest canopies. This paper introduces a progressive pipeline integrating DEFOM-Stereo foundation-model disparity estimation, SAM3 instance segmentation, and multi-stage depth optimization to deliver robust per-branch point clouds. Starting from a naive baseline, we systematically identify and resolve three error families through successive refinements. Mask boundary contamination is first addressed through morphological erosion and subsequently refined via a skeleton-preserving variant to safeguard thin-branch topology. Segmentation inaccuracy is then mitigated using LAB-space Mahalanobis color validation coupled with cross-branch overlap arbitration. Finally, depth noise - the most persistent error source - is initially reduced by outlier removal and median filtering, before being superseded by a robust five-stage scheme comprising MAD global detection, spatial density consensus, local MAD filtering, RGB-guided filtering, and adaptive bilateral filtering. Evaluated on 1920x1080 stereo imagery of Radiata pine (Pinus radiata) acquired with a ZED Mini camera (63 mm baseline) from a UAV in Canterbury, New Zealand, the proposed pipeline reduces the average per-branch depth standard deviation by 82% while retaining edge fidelity. The result is geometrically coherent 3D point clouds suitable for autonomous pruning tool positioning. All code and processed data are publicly released to facilitate further UAV forestry research.
Why it matches plant phenotyping methods樹木の枝を対象に、ステレオ深度推定・セグメンテーション・深度最適化による枝単位の3D形状抽出手法を開発・評価しており、植物器官の表現型取得が中心である。
abstractThis paper introduces a progressive pipeline integrating DEFOM-Stereo foundation-model disparity estimation, SAM3 instance segmentation, and multi-stage depth optimization to deliver robust per-branch point clouds.
3D phenotyping of seedlings is crucial to tomato cultivation in greenhouse facilities. Current studies focus on high-quality point cloud reconstruction and artificial intelligence (AI) 3D segmentation to derive phenotypic traits like plant height and crown width, which heavily rely on manual annotation and possess high complexity in deployment. This study proposes a novel AI framework from pixels to points, for efficient 3D plant phenotyping of tomato seedlings. Through the integration of 2D-3D coordinate mapping and AI vision language models, the proposed method enables accurate reconstruction and analysis of 3D phenotypic traits from single-view data. Top-down RGB images and corresponding point clouds with spatial alignment are captured using a binocular camera. Vision language models are employed with the text prompt “plant” to automatically generate bounding boxes and masks, thereby minimizing manual annotation. These outputs are further transferred to a lightweight YOLO11-segment model. The core innovation is established in our 2D-3D mapping strategy, through which plant-specific 3D points are efficiently extracted using only 2D masks. Non-plant points within initial masks are repurposed to determine ground height for improved plant height estimation, while masks are refined using the Excess Green Index to enhance crown width measurement. An mAP₅₀ of 96.0% is achieved by the YOLO11-segment model. Concerning sparse canopy, highly accurate results are yielded by our phenotyping approach, with RMSE values of 1.7 cm for plant height and 1.0 cm for crown width, and R 2 values of 0.93 and 0.95 against manual measurements. For dense canopy, the usage of a reference chessboard improves the performance (RMSE was reduced from 9.57 cm to 2.07 cm). Annotation dependency is significantly reduced, computational complexity is decreased, edge deployment is supported, and efficient technology transfer is enabled by the presented method. Considerable potential is offered for high-throughput screening of elite tomato varieties with desirable agronomic traits. • Real-time low-cost 3D phenotyping of tomato plants is proposed. • Weak labels simplify the 3D plant segmentation. • Segment the 3D point cloud using 2D pixel-masks with spatial alignment. • Vision language models and knowledge transfer further simplify the AI application.
Why it matches plant phenotyping methodsトマト苗の3D表現型を抽出する画像・点群・AI統合手法を開発し、手動測定との精度検証も行っており、表現型取得法が研究の中心である。
abstractThis study proposes a novel AI framework from pixels to points, for efficient 3D plant phenotyping of tomato seedlings.
Controlled-environment agriculture (CEA) and circular production systems require coordinated monitoring of biological and physicochemical processes across trophic levels. This project report presents the implementation of a multi-trophic controlled-environment agriculture demonstrator that integrates computer-vision-based monitoring with established sensor infrastructure for aquaculture, poultry, plants, microalgae, duckweed, and insect modules. Stereo imaging and RGB-D systems are deployed for non-invasive quantification of fish biomass and plant growth, while continuous water-quality and environmental measurements (e.g., pH, dissolved oxygen, nitrate, ammonium, temperature, CO$_2$) provide complementary process data. These data streams are synchronized within a shared database architecture to enable cross-module evaluation of nutrient dynamics, growth progression, and operational stability under real facility conditions. The implemented framework demonstrates how computer vision can extend conventional sensor-based monitoring by directly capturing biological performance indicators across aquatic, terrestrial, and microbial domains. While advanced predictive modeling and full digital twin simulation remain future development steps, the realized data-integration architecture establishes a structural foundation for the systematic evaluation of circular indoor food-production systems. The demonstrator illustrates how multimodal monitoring can support nutrient recirculation, transparency of biological variability, and data-driven assessment within controlled multi-trophic environments.
Why it matches plant phenotyping methods植物成長をステレオ画像およびRGB-Dで非侵襲的に定量するコンピュータビジョン監視基盤を実装しており、植物フェノタイピングが統合監視システムの主要な技術要素である。
abstractStereo imaging and RGB-D systems are deployed for non-invasive quantification of fish biomass and plant growth
Automated three-dimensional plant phenotyping is an essential tool for non-destructive analysis of plant growth and structure. This paper presents a low-cost system based on stereo vision for depth estimation and morphological characterization of maize plants. The system incorporates an automatic detection stage for the object of interest using deep learning techniques to delimit the region of interest (ROI) corresponding to the plant. The Semi-Global Block Matching (SGBM) algorithm is applied to the detected region to compute the disparity map and generate a partial three-dimensional representation of the plant structure. The ROI delimitation restricts the disparity calculation to the plant area, reducing processing of the background and optimizing computational resource use. The deep learning-based detection stage maintains stable foliage identification even under varying lighting conditions and shadowing, ensuring consistent depth data across different experimental conditions. Overall, the proposed system integrates detection and disparity estimation into an efficient processing flow, providing an accessible alternative for automated three-dimensional phenotyping in agricultural environments.
Why it matches plant phenotyping methods植物の3次元形態を取得・特徴づけるステレオビジョンと深度推定システムの開発が中心であり、明確な植物フェノタイピング手法です。
abstractThis paper presents a low-cost system based on stereo vision for depth estimation and morphological characterization of maize plants.
Reproduction assets foundThe paper's Data Availability Statement openly deposits the original study data (the 544 stereo RGB maize images and related phenotyping data) on OSF at a DOI, which is a paper-specific, publicly actionable asset. No author analysis code repository is explicitly stated.Dataset · publicData Availability Statement: The original data presented in the study are openly available in OSF at
https://doi.org/10.17605/OSF.IO/MN6P9.Open asset ↗OSF · 10.17605/OSF.IO/MN6P9pdf-page:18 lines:1-59Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Drones are increasingly used in forestry to capture high-resolution remote sensing data, supporting enhanced monitoring, assessment, and decision-making processes. While operations above the forest canopy are already highly automated, flying inside forests remains challenging, primarily relying on manual piloting. In dense forests, relying on the Global Navigation Satellite System (GNSS) for localization is not feasible. In addition, the drone must autonomously adjust its flight path to avoid collisions. Recently, advancements in robotics have enabled autonomous drone flights in GNSS-denied obstacle-rich areas. In this article, a step towards autonomous forest data collection is taken by building a prototype of a robotic under-canopy drone utilizing state-of-the-art open source methods and validating its performance for data collection inside forests. Specifically, the study focused on camera-based autonomous flight under the forest canopy and photogrammetric post-processing of the data collected with the low-cost onboard stereo camera. The autonomous flight capability of the prototype was evaluated through multiple test flights in boreal forests. The tree parameter estimation capability was studied by performing diameter at breast height (DBH) estimation. The prototype successfully carried out flights in selected challenging forest environments, and the experiments showed promising performance in forest 3D modelling with a miniaturized stereoscopic photogrammetric system. The DBH estimation achieved a root mean square error (RMSE) of 3.33 - 3.97 cm (10.69 - 12.98 %) across all trees. For trees with a DBH less than 30 cm, the RMSE was 1.16 - 2.56 cm (5.74 - 12.47 %). The results provide valuable insights into autonomous under-canopy forest mapping and highlight the critical next steps for advancing lightweight robotic drone systems for mapping complex forest environments.
Why it matches plant phenotyping methods森林内ドローンとステレオ画像による3D計測・DBH推定を開発および性能評価しており、樹木形質の取得方法が研究の中心である。
abstractbuilding a prototype of a robotic under-canopy drone utilizing state-of-the-art open source methods and validating its performance for data collection inside forests
With the continuous progress in micro-optical machine technology, miniature spectral imaging devices have been rapidly developed; however, three-dimensional (3D) imaging measurement technology has become increasingly mature and widely used. The evolution of these technologies has established a robust foundation for the integration of three-dimensional imaging and spectral information. To achieve accurate alignment between 3D data and spectral information to obtain a more comprehensive spectral representation of objects in 3D space, we developed a binocular multispectral stereo imaging (BMSI) system. This system acquires images in synchrony with a binocular multispectral imager, thereby ensuring accurate alignment between 3D data and spectral data at the pixel level and facilitating the construction of a four-dimensional (4D) dataset. The segmentation of leaf regions from shadow backgrounds in two distinct plant species was achieved through optimal band fusion and hue-saturation value (HSV) color space transformation, significantly improving the segmentation accuracy, processing efficiency, and robustness across different plant species. A systematic evaluation was conducted to quantify the reconstruction precision and system stability at different measurement distances. The designed system acquired 4D image spectral data with plants as the objects to be tested. The distribution characteristics of chlorophyll (Chl) on the 3D surface of plants were obtained by first-order derivatives of the spectral data and the normalized difference red edge (NDRE) index. This technique provides a new means for plant phenotyping research and a more effective technical approach for the digitalization and precision monitoring of the agricultural industry.
Why it matches plant phenotyping methods植物の3D・マルチスペクトル画像取得、葉領域分割、再構成精度・安定性評価を中核とする新規フェノタイピングシステムの開発研究である。
abstractwe developed a binocular multispectral stereo imaging (BMSI) system.
Reproduction assets foundThe paper's data availability statement explicitly deposits raw data and essential source code for the BMSI plant phenotyping analysis on a public GitHub repository.Code · publicThe raw data and the essential parts of the source code have been uploaded to Github: https://github.com/wwxsoul1234/BMSI/tree/master.Open asset ↗wwxsoul1234/BMSI · BMSIhtml-lines:278-306Code / dataset availability confirmedEurope PMC · OpenAlex · checked 6 Sept 2026
TomatoMultimodalStereoFruitObject detectionVisualization / data managementGrowth / development / phenologyFruit / seed / panicle traitsYield / yield components
Introduction The advancement of smart agriculture has witnessed increasing applications of computer vision in crop monitoring and management. However, existing approaches remain challenged by high computational complexity, limited real-time capability, and poor multi-task coordination in tomato cultivation scenarios. Methods To address these limitations, an intelligent tomato management system is proposed based on the Ghost-based Adaptive Efficient You Only Look Once (GAE-YOLO) algorithm. The lightweight architecture of the GAE-YOLO framework is achieved through the replacement of standard convolutional layers with Ghost Convolution (GhostConv) modules, while detection accuracy is significantly improved by the integration of both AReLU activation functions and Effective Intersection over Union (E-IoU) loss optimization. The system, implemented on a Jetson TX2 embedded platform, also incorporates ZED stereo vision for 3D localization and a PyQt6-based visualization platform. Results When implemented on Jetson TX2, the system achieving 93.5% mean Average Precision at 50% intersection over union (mAP@50) at 10.2 frames per second (FPS), which can be optimized to 27 FPS by employing TensorRT acceleration and 720p resolution for scenarios demanding higher throughput. Furthermore, it establishes standardized assessment systems for tomato maturity and yield prediction, and offers integrated modules for disease diagnosis and agricultural large language model consultation. Discussion This work establishes a new paradigm for edge computing in agriculture while providing critical technical support for smart farming development.
Why it matches plant phenotyping methodsトマトの成熟度・収量予測および病害診断を含む画像・3Dビジョン基盤を開発し、エッジ環境で性能評価しているため、植物表現型取得が中心的な研究である。
abstractan intelligent tomato management system is proposed based on the Ghost-based Adaptive Efficient You Only Look Once (GAE-YOLO) algorithm
Reproduction assets foundThe paper's data availability statement explicitly states that the data and code supporting the study are publicly available on GitHub at the authors' repository (GAE-YOLO), which matches an allowed URL. This qualifies as a paper-specific public code asset for the tomato detection/phenotyping analysis.Code · publicThe data and code supporting this study are publicly available at GitHub under the following links: https://github.com/NSSCk/GAE-YOLO .Open asset ↗NSSCk/GAE-YOLOlines:756-834Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Abstract. Historical aerial images, captured by film cameras in the previous century, are valuable resources for quantifying Earth's surface and landscape changes over time. In the post-war period, these images were often acquired to create topographic maps, resulting in the acquisition of large-scale aerial photographs with stereo coverage. Photogrammetric techniques applied to these stereo images enable the extraction of 3D information to reconstruct digital surface models (DSMs) and orthoimages. Here, we present a highly automated photogrammetric approach for generating countrywide DSMs of Switzerland, at a 1 m resolution, from approximately 32 000 scanned aerial stereo images acquired between 1979 and 2006, with known exterior and interior orientation. We derived four countrywide DSMs for the epochs 1979–1985, 1985–1991, 1991–1998, and 1998–2006. From the DSMs, we generated corresponding countrywide vegetation height models (VHMs). We assessed the quality of the historical DSMs at the country scale and within six representative study sites, evaluating the vertical accuracy and the completeness of image matching across different land cover types. Mean completeness ranged from 64 % for “glacial and perpetual snow” to 98 % for “sealed surfaces”, with a value of 93 % for the “closed forest” class. Across Switzerland, the median elevation accuracy of the historical DSMs compared with a reference digital terrain model (DTM) on sealed surface points ranged from 0.08 to 0.16 m, with a normalized median absolute deviation (NMAD) of around 0.8 m and a maximum root mean square error (RMSE) of 1.20 m. Similar accuracies are obtained when comparing historical DSMs with measured geodetic points. The VHMs generated in this study enabled the detection of major changes in forest areas due to windstorm damage, forest dynamics, and growth. This work demonstrates the feasibility of generating accurate, very-high-resolution DSM time series (spanning three decades) and VHMs from historical aerial images of the entire surface of Switzerland in a highly automated manner. The VHMs are already being used to estimate countrywide biomass changes. The countrywide DSMs and VHMs for the four epochs, along with auxiliary data, are available online at https://doi.org/10.16904/envidat.528 (Marty et al., 2024) and can be used to quantify long-term elevation changes and related processes across different surfaces.
Why it matches plant phenotyping methods歴史的航空画像から植生高モデルを生成する自動写真測量法を開発・精度評価し、森林の高さ変化という植物キャノピー形質を抽出しているため、測定法が中心的である。
abstractFrom the DSMs, we generated corresponding countrywide vegetation height models (VHMs).
Reproduction assets foundThe paper's countrywide DSMs, VHMs, and auxiliary rasters (matching mask, vegetation mask, metadata shapefile) for four epochs are deposited publicly on EnviDat with an explicit DOI. These vegetation height models are the paper's plant/canopy phenotyping measurements. No author analysis code or trained models are namedDataset · publicDatasets can be accessed from EnviDat ( https://doi.org/10.16904/envidat.528 , Marty et al., 2024). The following files are available for the four epochs: countrywide digital surface model (DSM), hillshaded DSM, and vegetation height models (VHMs).Open asset ↗Envidat · 10.16904/envidat.528lines:249-256Plant phenotyping relevance match · UnverifiedOpenAlex · arXiv · checked 15 Sept 2026
NeRF / 3D Gaussian SplattingStereoLeafStem / branchObject detection2D/3D reconstructionSegmentation
Commercial plant phenotyping systems using fixed cameras cannot perceive many plant details due to leaf occlusion. In this paper, we present Botany-Bot, a system for building detailed "annotated digital twins" of living plants using two stereo cameras, a digital turntable inside a lightbox, an industrial robot arm, and 3D segmentated Gaussian Splat models. We also present robot algorithms for manipulating leaves to take high-resolution indexable images of occluded details such as stem buds and the underside/topside of leaves. Results from experiments suggest that Botany-Bot can segment leaves with 90.8% accuracy, detect leaves with 86.2% accuracy, lift/push leaves with 77.9% accuracy, and take detailed overside/underside images with 77.3% accuracy. Code, videos, and datasets are available at https://berkeleyautomation.github.io/Botany-Bot/.
Why it matches plant phenotyping methods植物の遮蔽部位を撮像・セグメンテーションし、3Dデジタルツインとして植物構造を取得するロボット型表現型計測システムの開発が中心である。
abstractwe present Botany-Bot, a system for building detailed "annotated digital twins" of living plants using two stereo cameras, a digital turntable inside a lightbox, an industrial robot arm, and 3D segmentated Gaussian Splat models.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Introduction: Accurate 3D reconstruction is essential for plant phenotyping. However, point clouds generated directly by binocular cameras using single-shot mode often suffer from distortion, while self-occlusion among plant organs complicates complete data acquisition. Methods: To address these challenges, this study proposes and validates an integrated, two-phase plant 3D reconstruction workflow. In the first phase, we bypass the integrated depth estimation module on camera and instead apply Structure from Motion (SfM) and Multi-View Stereo (MVS) techniques to the captured high-resolution images. It produces high-fidelity, single-view point clouds, effectively avoiding distortion and drift. In the second phase, to overcome self-occlusion, we register point clouds from six viewpoints into a complete plant model. This process involves a rapid coarse alignment using a marker-based Self-Registration (SR) method, followed by fine alignment with the Iterative Closest Point (ICP) algorithm. Results: The workflow was validated on two Ilex species (Ilex verticillata and Ilex salicina). The results demonstrate the high accuracy and reliability of the workflow. Furthermore, key phenotypic parameters extracted from the models show a strong correlation with manual measurements, with coefficients of determination (R²) exceeding 0.92 for plant height and crown width, and ranging from 0.72 to 0.89 for leaf parameters. Discussion: These findings validate our workflow as an accurate, reliable, and accessible tool for quantitative 3D plant phenotyping.
Why it matches plant phenotyping methods植物の3D再構成と形質抽出ワークフロー自体を開発・検証しており、植物形質計測が中心的な方法論的貢献である。
abstractthis study proposes and validates an integrated, two-phase plant 3D reconstruction workflow
Accurate non-destructive measurement of phenotypic data for leafy vegetable is critical for effective management of breeding, growth monitoring, and yield estimation. However, the scarcity of large-scale stereo matching datasets in agricultural scenarios and the high cost of depth-sensing devices tailored for small targets pose significant challenges to achieving accurate phenotyping of crops in complex growing environments. This study introduces a novel 3D Gaussian Splatting (3DGS)-supervised stereo matching network to enable accurate depth estimation of leafy vegetable. We enhance Gaussian rendering techniques to generate stereo training data from multi-view images. A multi-scale feature extraction network with deformable convolutions is proposed to adaptively capture diverse features, ranging from low-level details (edges and textures) to structural and semantic information. To address the multi-peak characteristics of disparity in leafy vegetable, a top- k algorithm is employed for disparity regression, significantly improving disparity estimation accuracy. To enhance the adaptability of cost aggregation, we construct a combined cost volume through a concatenation and group-wise correlations and leverage feature attention blocks to refine disparity prediction. Additionally, depthwise separable convolutions and optimized methods of the number of iterative blocks for leafy vegetable small targets are introduced to reduce computational complexity and accelerate training and inference. Experimental results demonstrate the effectiveness of our depth estimation method, achieving an Endpoint Error (EPE) of 2.32 pixels overall and 0.91 pixels specifically in leafy vegetable area compared to disparity ground truth. Furthermore, we developed a 3D point cloud phenotyping measurement method using instance segmentation and 3D reconstruction. Notably, the measured height, number of leaves, and surface area achieve R 2 values of 0.9946, 0.9577, and 0.9775 against manual measurements, respectively. These high-precision results underscore the practical applicability of our method and provide a theoretical foundation for deployment in agricultural production systems.
Why it matches plant phenotyping methods葉菜の深度推定と3D点群による形質測定法を開発し、手動測定との比較で高さ・葉数・表面積を検証しており、フェノタイピング手法が研究の中心である。
abstractThis study introduces a novel 3D Gaussian Splatting (3DGS)-supervised stereo matching network to enable accurate depth estimation of leafy vegetable.
This study proposes a real-time fruit volume estimation model based on YOLOv9 (RTFVE-YOLOv9) and binocular stereo vision technology to address the challenges of low automation and insufficient accuracy in fruit volume measurement in complex orchard environments, particularly in scenarios with diverse canopy structures and severe branch-leaf occlusion. The model achieves effective recognition of occluded fruits through the innovative design of a Dual-Scale and Global–Local Sequence (DSGLSeq) module while incorporating a Multi-Head and Multi-Scale Self-Interaction (MHMSI) module to improve the detection performance of small fruit targets. Systematic validation experiments conducted on major economic fruit tree varieties, including apples, pears, pomelos, and kiwifruit, demonstrate that RTFVE-YOLOv9 improved the mean Average Precision (mAP) by 2.1%, 1.6%, 4%, and 3.8% respectively on the four fruit datasets compared to the baseline YOLOv9-c model. The model’s internal working mechanisms were thoroughly revealed through multi-dimensional evaluation, including ablation experiments, Heatmap Analysis, and Effective Receptive Field (ERF) analysis, providing a theoretical foundation for subsequent optimization. The research findings enrich the application theory of computer vision in smart agriculture and provide reliable technical support for achieving precise orchard management.
Why it matches plant phenotyping methods果実の体積という植物器官形質を、YOLOv9と両眼ステレオビジョンで推定する手法を開発・検証しており、画像取得・計算による表現型推定が研究の中心である。
abstractThis study proposes a real-time fruit volume estimation model based on YOLOv9 (RTFVE-YOLOv9) and binocular stereo vision technology
Venus flytrap (Dionaea muscipula) leaves exhibit an exceptionally rapid closing motion that occurs within one second. The rapid closure of outwardly curved leaves is thought to be driven by snap-buckling instability-a rapid transition of an elastic system from one state to another. However, the ability of leaves that do not curve outward to also close suggests that the mechanics of leaf closure are complex and need to be understood using three-dimensional (3D) kinematics. We therefore developed a 3D reconstruction method to quantify the curvatures and displacements of leaf blades using two high-speed cameras. We then reconstructed a 3D surface mesh of the leaf, which revealed that the changes in curvature are spatiotemporally heterogeneous. We inferred the stretching and curvature elastic energies of the reconstructed surface, determining that the mechanical forces associated with in-plane deformation become significant in the peripheral regions of the leaf. This was true among different samples; however, the components of the energy profiles varied for each sample. The novelty of this study is that we could infer the elastic energy and the corresponding mechanical forces during closing motion. Our mechanical inference method will be useful for examining the deformation processes of various curved plant structures.
Why it matches plant phenotyping methods植物葉の3D画像再構成により、曲率・変位・弾性エネルギーを定量化する手法を開発しており、植物形態・運動の表現型取得が研究の中心である。
abstractWe therefore developed a 3D reconstruction method to quantify the curvatures and displacements of leaf blades using two high-speed cameras.
StrawberryStereoFlowerFruitObject detectionGrowth / development / phenology
Introduction To enhance the quality and yield of strawberries, it is essential to effectively supervise the entire growing process. Currently, the monitoring of strawberry growth primarily relies on manual identification and positioning methods. This approach presents several challenges, including low efficiency, high labor intensity, time consumption, elevated costs, and a lack of standardized monitoring protocols. On the basis of this, there was an urgent need in the market to automate the whole process of target recognition and localization in strawberry growing. Methods Aiming at the above problems, we innovatively constructed a model for target recognition and localization of strawberries based on the YOLOv8s benchmark model, named the WCS-YOLOv8s model. In this paper, the whole growth process of the strawberry was divided into four stages, namely, the bud, flower, fruit under-ripening, and fruit ripening stages, and a total of 1,957 images of these four stages were captured with a binocular depth camera. Using the constructed WCS-YOLOv8s model to process the images, the target recognition and localization of the whole growth process of the strawberry were accomplished. This model proposes a data enhancement strategy based on the Warmup learning rate to stabilize the initial training process. The self- developed SE-MSDWA module is integrated into the backbone network to improve the model's feature extraction capability while suppressing redundant information, thereby achieving efficient feature extraction. Additionally, the neck network is enhanced by incorporating the CGFM module, which employs a multi-head self-attention mechanism to fuse diverse feature information and improve the network's feature fusion performance. Results and discussion The model's Precision (P), Recall (R), HYPERLINK "mailto:mAP@0.5" mAP@0.5, and mAP@0.5:0.95 of detection were 83.4%, 86.7%, 87.53%, and 60.48%, respectively, and the detection speed was 45.9 FPS(21.8 ms/per image, which significantly improved on the detection accuracy and generalization ability of with the YOLOv8s benchmark model. This model can meet the demand for online real-time target identification and localization of strawberries and provide a new detection method for the automated monitoring and management of the whole growth process of strawberries.
Why it matches plant phenotyping methodsイチゴの生育段階（芽、花、未熟果、成熟果）を画像から認識・定位するYOLOモデルを開発し、検出性能も評価しており、植物状態の取得手法が研究の中心である。
abstractwe innovatively constructed a model for target recognition and localization of strawberries based on the YOLOv8s benchmark model, named the WCS-YOLOv8s model.
Plant phenotyping relevance match · UnverifiedbioRxiv · OpenAlex · Europe PMC · checked 13 Sept 2026
O_LILight drones provide a cheap and effective tool to monitor forest canopy, especially in tropical and equatorial contexts, where infrastructure and resources are limiting. In these regions, good quality optical satellite images are rare, yet the stakes are maximal to characterize forest function, dynamics, diversity, and phenology, and more generally the vegetation-climate interplay. C_LIO_LIWe describe a complete processing chain based on photogrammetric tools that seeks to optimize the spatial and spectral coherence between repeat image mosaics at centimetric resolution. Our target is to allow individual tree-level monitoring over tens to hundreds of hectare scales with consumer grade equipment (i.e., quadcopter with stabilized RGB camera, standard GNSS positioning). C_LIO_LIWe demonstrate the increase in spatial precision achieved using Time-SIFT and Arosics algorithms, which allow (individually and synergistically) to reduce global and local spatial misalignment between mosaics from several meters to a few centimeters. Time-SIFT provides the advantage of increased robustness in initial image alignment and 3D reconstruction, and hence reduces occasional distortions or data gaps. Using Agisofts color and white balance corrections combined with the use of vegetation indices provides meaningful quantitative signal despite considerable changes in acquisition conditions. C_LIO_LIIn particular, indices that are less sensitive to illumination changes, like the green chromatic coordinate (GCC), allowed evidencing a seasonal signal over four years of monitoring in the evergreen moist forest at Paracou in French Guiana. The signal was decorrelated from obvious geometrical effect (sun height), and provided information on the vegetative stage at tree, species, and stand levels. C_LI Data/Code for peer reviewThe complete processing chain, as well as the data and scripts used for producing the analyses presented here, are available for review on Zenodo: https://zenodo.org/records/15449377?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6IjdjNWExMzIzLThkNDMtNDllNy1iYWY4LTY2MGZlZjkyZmQ3OCIsImRhdGEiOnt9LCJyYW5kb20iOiI5MGQ4NTE2YTE4OGViNDQ3YTFiZmMyYTFkZDlhZTZmMiJ9.CsJ0VRuQ90A1qzO1VJC1Q9eXFSp1N5UpeJlyr6otgXRPlf-I-jcwBJ6ytiBbbu8enCNJ2Ke6-oxNV8aeJ_AWIw
Why it matches plant phenotyping methodsドローン画像のステレオフォトグラメトリ処理、画像整合化、色補正、植生指数を組み合わせた処理チェーンを開発・実証し、個体樹木から林分レベルの植生状態・季節性を定量化しているため、植物フェノタイピング手法が中心である。
abstractWe describe a complete processing chain based on photogrammetric tools that seeks to optimize the spatial and spectral coherence between repeat image mosaics at centimetric resolution.
Abstract Purpose Accurate measurement of corn stalk diameter is important for assessing plant robustness, lodging resistance, and harvest performance, but automated measurement across changing crop conditions remains challenging. This study evaluated whether a ground-based stereo-vision system could reliably estimate stalk diameter throughout the growing season, from mid-summer through senescence. Methods A stereo-vision pipeline using dual AR0234 global-shutter cameras was deployed on an autonomous ground robot. YOLOv8 provided stalk localization and pose correction, BoT-SORT enabled multi-frame tracking, and U-Net segmentation with an edge-mask strategy extracted stalk boundaries under partial occlusion. Repeated per-frame width estimates were filtered and converted to physical dimensions using disparity-based calibration. Vision estimates were compared with perpendicular caliper measurements. Results Under mid-summer conditions with minimal leaf interference, the system achieved a mean absolute error (MAE) of 1.1–1.5 mm and r² of 0.90. During late-season senescence, leaf-sheath expansion and occlusion caused systematic diameter overestimation and reduced accuracy. Applying a seasonally derived offset of approximately 3.8 mm reduced MAE to approximately 1.3 mm, although correlation remained modest (r² ≈ 0.41). Independent human measurements also exhibited variability, with inter-rater r² ≈ 0.77 and mean disagreement of approximately 1.0 mm. Conclusion Stereo vision can provide accurate, non-contact corn stalk diameter measurements under favorable canopy conditions and remains viable across the crop lifecycle. However, robust late-season phenotyping will require improved modeling of leaf sheaths and occlusions together with more reliable ground-truth measurement procedures.
Why it matches plant phenotyping methodsステレオビジョンと深層学習によるトウモロコシ茎径の自動推定・校正・検証が研究の中心であり、明確な植物表現型測定法である。
titleCorn stalk diameter estimation using deep learning
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Field / plotNeRF / 3D Gaussian SplattingLiDAR / point cloudStereoWhole plant / canopy / plot / fieldObject detection2D/3D reconstructionStress / disease detectionGrowth / development / phenology
In the context of the booming development of precision agriculture and plant phenotyping, plant 3D reconstruction technology has become a research hotspot, with widespread applications in plant growth monitoring, pest and disease detection, and smart agricultural equipment. Given the complex geometric and textural characteristics of plants, traditional 2D image analysis methods are difficult to meet the modeling requirements, highlighting the growing importance of 3D reconstruction technology. This paper reviews active vision techniques (such as structured light, time-of-flight, and laser scanning methods), passive vision techniques (such as stereo vision and structure from motion), and deep learning-based 3D reconstruction methods (such as NeRF, CNN, and 3DGS). These technologies enhance crop analysis accuracy from multiple perspectives, provide strong support for agricultural production, and significantly promote the development of the field of plant research.
Why it matches plant phenotyping methods植物フェノタイピング向けの3次元再構築技術を体系的にレビューしており、画像ベースの形質取得手法が中心的です。
titleA Review of Optical-Based Three-Dimensional Reconstruction and Multi-Source Fusion for Plant Phenotyping.
Deep learning has transformed computer vision for precision agriculture, yet apple orchard monitoring remains limited by dataset constraints. The lack of diverse, realistic datasets and the difficulty of annotating dense, heterogeneous scenes. Existing datasets overlook different growth stages and stereo imagery, both essential for realistic 3D modeling of orchards and tasks like fruit localization, yield estimation, and structural analysis. To address these gaps, we present AppleGrowthVision, a large-scale dataset comprising two subsets. The first includes 9,317 high resolution stereo images collected from a farm in Brandenburg (Germany), covering six agriculturally validated growth stages over a full growth cycle. The second subset consists of 1,125 densely annotated images from the same farm in Brandenburg and one in Pillnitz (Germany), containing a total of 31,084 apple labels. AppleGrowthVision provides stereo-image data with agriculturally validated growth stages, enabling precise phenological analysis and 3D reconstructions. Extending MinneApple with our data improves YOLOv8 performance by 7.69 % in terms of F1-score, while adding it to MinneApple and MAD boosts Faster R-CNN F1-score by 31.06 %. Additionally, six BBCH stages were predicted with over 95 % accuracy using VGG16, ResNet152, DenseNet201, and MobileNetv2. AppleGrowthVision bridges the gap between agricultural science and computer vision, by enabling the development of robust models for fruit detection, growth modeling, and 3D analysis in precision agriculture. Future work includes improving annotation, enhancing 3D reconstruction, and extending multimodal analysis across all growth stages.
Why it matches plant phenotyping methodsリンゴの生育段階・果実・樹体構造を対象とする大規模ステレオ画像データセットを構築し、果実検出、フェノロジー分析、3D再構成モデルの評価に用いており、表現型取得・解析基盤が中心である。
titleAppleGrowthVision: A large-scale stereo dataset for phenological analysis, fruit detection, and 3D reconstruction in apple orchards
Corn is an important staple crop. Obtaining the pose and dimensions of corn is key for automating corn cultivation, mainly using robotic arms or similar devices to perform precise operations, such as pesticide spraying, measurement, or precise picking. In this study, the Stereo-Corn-Pose Detection (SCPD) algorithm was proposed, which used three dimensional (3D) object detection to obtain the pose and dimensions of corn with stereo images. This algorithm includes pitch angle detection, which is absent in traditional 3D object detection. The SCPD algorithm consists of two models: the union box detection model, FCOS-Stereo, based on the anchor-free network FCOSNet, and the 3D bounding box regression model, Cross-Stereo-EfficientFormer. This regression model incorporates a cross-attention mechanism into EfficientFormer to extract and fuse features effectively. This work constructed a dataset comprising 2,700 samples for training and 300 samples for testing. In the test set, this work achieved a union bounding box mAP of 85.3%, representing a 3.5% improvement over the original FCOSNet model. It also achieved 88.3% AP3D and 85.6% APBEV for 3D bounding box regression, making increases of 5% and 4.7%, respectively, compared to the traditional 3D object detection method, IDA-3D. The results indicate an accuracy of approximately 91% in detecting corn dimensions and pose. Therefore, the SCPD algorithm offers a novel framework for obtaining the 3D dimensions and pose of corn and promotes precision and smart agriculture for corn cultivation.
Why it matches plant phenotyping methodsトウモロコシの姿勢・寸法という植物形態形質をステレオ画像から推定する3D手法を開発し、データセットと精度評価も行っており、フェノタイピング手法が中心である。
abstractthe Stereo-Corn-Pose Detection (SCPD) algorithm was proposed, which used three dimensional (3D) object detection to obtain the pose and dimensions of corn with stereo images.
Multispectral imaging plays a key role in crop monitoring. A major challenge, however, is spectral band misalignment, which can hinder accurate plant health assessment by distorting the calculation of vegetation indices. This study presents a novel approach for short-range calibration of a multispectral camera, utilizing stereo vision for precise geometric correction of acquired images. By using multispectral camera lenses as binocular pairs, the sensor acquisition distance was estimated, and an alignment model was developed for distances ranging from 500 mm to 1500 mm. The approach relied on selecting the red band image as a reference, while the remaining bands were treated as moving images. The stereo camera calibration algorithm estimated the target distance, enabling the correction of band misalignment through previously developed models. The alignment models were applied to assess the health status of baby leaf crops (Lactuca sativa cv. Maverik) by analyzing spectral indices correlated with chlorophyll content. The results showed that the stereo vision approach used for distance estimation achieved high accuracy, with average reprojection errors of approximately 0.013 pixels (4.485 × 10−5 mm). Additionally, the proposed linear model was able to explain reasonably the effect of distance on alignment offsets. The overall performance of the proposed experimental alignment models was satisfactory, with offset errors on the bands less than 3 pixels. Despite the results being not yet sufficiently robust for a fully predictive model of chlorophyll content in plants, the analysis of vegetation indices demonstrated a clear distinction between healthy and unhealthy plants.
Why it matches plant phenotyping methods植物の健康状態・クロロフィル関連形質を推定するマルチスペクトル画像の幾何補正・校正法を開発し、精度検証と作物への適用を行っており、フェノタイピング手法が中心である。
abstractThis study presents a novel approach for short-range calibration of a multispectral camera, utilizing stereo vision for precise geometric correction of acquired images.
Abstract. Historical aerial images, captured by film cameras in the previous century, are valuable resources for quantifying Earth’s surface and landscape changes over time. In the post-war period, these images were often acquired to create topographic maps, resulting in the acquisition of large-scale aerial photographs with stereo coverage. Photogrammetric techniques applied to these stereo images enable the extraction of 3D information to reconstruct digital surface models (DSMs) and orthoimages. Here, we present a highly automated photogrammetric approach for generating countrywide DSMs of Switzerland, at a 1 m resolution, from approximately 40,000 scanned aerial stereo images acquired between 1979 and 2006, with known exterior and interior orientation. We derived four countrywide DSMs for the epochs 1979–1985, 1985–1991, 1991–1998, and 1998–2006. From the DSMs, we generated corresponding countrywide vegetation height models (VHMs). We assessed the quality of the historical DSMs at the country scale and within six representative study sites, evaluating the vertical accuracy and the completeness of image-matching across different land cover types. Mean completeness ranged from 64 % for ‘glacial and perpetual snow’ to 98 % for ‘sealed surfaces’, with a value of 93 % for the ‘closed forest’ class. Across Switzerland, the median elevation accuracy of the historical DSMs compared with a reference digital terrain model (DTM) on sealed surface points ranged from 0.28 to 0.53 m, with a normalised median absolute deviation (NMAD) of around 1 m and a maximum root mean square error (RMSE) of 3.90 m. The same analysis between geodetic points and historical DSMs showed higher accuracies, with median values of ≤ 0.05 m and an NMAD < 1 m. The VHMs generated in this study enabled the detection of major changes in forest areas due to windstorm damage, forest dynamics, and growth. This work demonstrates the feasibility of generating accurate, very high-resolution DSM time series (spanning three decades) and VHMs from historical aerial images of the entire surface of Switzerland in a highly automated manner. The VHMs are already being used to estimate countrywide biomass changes. The countrywide DSMs and VHMs for the four epochs, along with auxiliary data, are available online at https://doi.org/10.16904/envidat.528 (Marty et al., 2024) and can be used to quantify long-term elevation changes and related processes across different surfaces.
Why it matches plant phenotyping methods歴史航空画像から植生高モデルを自動生成するフォトグラメトリ手法を開発・精度評価し、森林の高さ変化という植物状態を測定するデータセットも提供しているため、植物フェノタイピング手法が中心である。
abstractFrom the DSMs, we generated corresponding countrywide vegetation height models (VHMs).
Reproduction assets foundThe paper's own countrywide DSM and vegetation height model (VHM) rasters, plus masks and metadata, are publicly deposited on EnviDat with an explicit DOI, directly reproducing the paper's vegetation height measurements.Dataset · public22
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Datasets can be accessed from EnviDat (https://doi.org/10.16904/envidat.528, Marty et al., 2024). The following files are
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available for the four epochs: countrywide digital surface model (DSM), hillshaded DSM, and vegetation height models
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(VHMs). A metadata shapefile is provided with information about the acquisition year of the photographs used here; the
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geometry corresponds to the 1:25,00Open asset ↗EnviDat · 10.16904/envidat.528pdf-raw-page:22 lines:1-63Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
Developing accurate, non-destructive, and automated methods for monitoring the phenotypic traits of rapeseed is crucial for improving yield and quality in modern agriculture. We used a line laser binocular stereo vision technology system to obtain the three-dimensional (3D) point cloud data of different rapeseed varieties (namely Qinyou 7, Zheyouza 108, and Huyou 039) at the seedling stage, and the phenotypic traits of rapeseed were extracted from those point clouds. After pre-processing the rapeseed point clouds with denoising and segmentation, the plant height, leaf length, leaf width, and leaf area of the rapeseed in the seedling stage were extracted by a series of algorithms and were evaluated for accuracy with the manually measured values. The following results were obtained: the R2 values for plant height data between the extracted values of the 3D point cloud and the manually measured values reached 0.934, and the RMSE was 0.351 cm. Similarly, the R2 values for leaf length of the three kinds of rapeseed were all greater than 0.95, and the RMSEs for Qinyou 7, Zheyouza 108, and Huyou 039 were 0.134 cm, 0.131 cm, and 0.139 cm, respectively. Regarding leaf width, R2 was greater than 0.92, and the RMSEs were 0.151 cm, 0.189 cm, and 0.150 cm, respectively. Further, the R2 values for leaf area were all greater than 0.98 with RMSEs of 0.296 cm2, 0.231 cm2 and 0.259 cm2, respectively. The results extracted from the 3D point cloud are reliable and have high accuracy. These results demonstrate the potential of 3D point cloud technology for automated, non-destructive phenotypic analysis in rapeseed breeding programs, which can accelerate the development of improved varieties.
Why it matches plant phenotyping methods3D点群と分割・抽出アルゴリズムを用いて rapeseed の形態形質を自動推定し、手測定値で精度検証しており、フェノタイピング手法が研究の中心である。
abstractWe used a line laser binocular stereo vision technology system to obtain the three-dimensional (3D) point cloud data of different rapeseed varieties
Drones are increasingly used in forestry to capture high-resolution remote sensing data, supporting enhanced monitoring, assessment, and decision-making processes. While operations above the forest canopy are already highly automated, flying inside forests remains challenging, primarily relying on manual piloting. In dense forests, relying on the Global Navigation Satellite System (GNSS) for localization is not feasible. In addition, the drone must autonomously adjust its flight path to avoid collisions. Recently, advancements in robotics have enabled autonomous drone flights in GNSS-denied obstacle-rich areas. In this article, a step towards autonomous forest data collection is taken by building a prototype of a robotic under-canopy drone utilizing state-of-the-art open source methods and validating its performance for data collection inside forests. Specifically, the study focused on camera-based autonomous flight under the forest canopy and photogrammetric post-processing of the data collected with the low-cost onboard stereo camera. The autonomous flight capability of the prototype was evaluated through multiple test flights in boreal forests. The tree parameter estimation capability was studied by performing diameter at breast height (DBH) estimation. The prototype successfully carried out flights in selected challenging forest environments, and the experiments showed promising performance in forest 3D modeling with a miniaturized stereoscopic photogrammetric system. The DBH estimation achieved a root mean square error (RMSE) of 3.33 - 3.97 cm (10.69 - 12.98 %) across all trees. For trees with a DBH less than 30 cm, the RMSE was 1.16 - 2.56 cm (5.74 - 12.47 %). The results provide valuable insights into autonomous under-canopy forest mapping and highlight the critical next steps for advancing lightweight robotic drone systems for mapping complex forest environments.
Why it matches plant phenotyping methods自律型ドローンとステレオ画像による森林3D計測・DBH推定が研究の中心であり、植物個体の形態形質を技術的に評価している。
abstractthe study focused on camera-based autonomous flight under the forest canopy and photogrammetric post-processing of the data collected with the low-cost onboard stereo camera.
Rapeseed / canolaLiDAR / point cloudStereoLeafClassification2D/3D reconstructionSegmentationLeaf traits
Point cloud segmentation is necessary for obtaining highly precise morphological traits in plant phenotyping. Although a huge development has occurred in point cloud segmentation, the segmentation of point clouds from complex plant leaves still remains challenging. Rapeseed leaves are critical in cultivation and breeding, yet traditional two-dimensional imaging is susceptible to reduced segmentation accuracy due to occlusions between plants. The current study proposes the use of binocular stereo-vision technology to obtain three-dimensional (3D) point clouds of rapeseed leaves at the seedling and bolting stages. The point clouds were colorized based on elevation values in order to better process the 3D point cloud data and extract rapeseed phenotypic parameters. Denoising methods were selected based on the source and classification of point cloud noise. However, for ground point clouds, we combined plane fitting with pass-through filtering for denoising, while statistical filtering was used for denoising outliers generated during scanning. We found that, during the seedling stage of rapeseed, a region-growing segmentation method was helpful in finding suitable parameter thresholds for leaf segmentation, and the Locally Convex Connected Patches (LCCP) clustering method was used for leaf segmentation at the bolting stage. Furthermore, the study results show that combining plane fitting with pass-through filtering effectively removes the ground point cloud noise, while statistical filtering successfully denoises outlier noise points generated during scanning. Finally, using the region-growing algorithm during the seedling stage with a normal angle threshold set at 5.0/180.0* M_PI and a curvature threshold set at 1.5 helps to avoid the under-segmentation and over-segmentation issues, achieving complete segmentation of rapeseed seedling leaves, while the LCCP clustering method fully segments rapeseed leaves at the bolting stage. The proposed method provides insights to improve the accuracy of subsequent point cloud phenotypic parameter extraction, such as rapeseed leaf area, and is beneficial for the 3D reconstruction of rapeseed.
Why it matches plant phenotyping methods二眼ステレオビジョンによる3D点群取得、ノイズ除去、葉セグメンテーションを開発・評価し、葉面積などの表現型形質抽出を目的とするため、植物フェノタイピング手法が研究の中心である。
abstractPoint cloud segmentation is necessary for obtaining highly precise morphological traits in plant phenotyping.
In agriculture, the plant leaf angle influences light use efficiency and photosynthesis and, consequently, the overall crop performance. Leaf angle measurements are used in plant phenotyping, plant breeding, and remote sensing to study plant function and structure. Traditional manual leaf angle measurements have limited precision as they are labor- and time-intensive due to challenging environmental conditions and highly dynamic plant processes. To enable more detailed studies on leaf angles, we modified a well-established automated farming robot to obtain high-resolution 3D point clouds at customizable intervals of individual plants using stereo vision. We demonstrate the system's accuracy and reliability, with minimal deviation from reference values. The method can be utilized by other researchers to gather data on leaf angles and other structural plant traits at regular intervals to access the dynamics of leaves, plants, and canopies. The system's low cost and adaptability can enhance the efficiency of crop monitoring in plant breeding and phenotyping experiments. Detailed documentation and code are available on GitHub.•An open-source farming robot is retrofitted to function as an automatic data collection platform•Hard to access leaf angles can be retrieved with high accuracy•Leaf angle dynamics can be observed with high temporal resolution.
Why it matches plant phenotyping methodsステレオビジョンを用いて葉角度を高精度・高頻度に測定するロボット基盤を開発・改良し、精度と信頼性を検証しているため、植物フェノタイピング手法が中心である。
abstractwe modified a well-established automated farming robot to obtain high-resolution 3D point clouds at customizable intervals of individual plants using stereo vision.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicAll used codes and recorded data are available at: https://github.com/FrederikHennecke/PointCloudHarvest .Open asset ↗FrederikHennecke/PointCloudHarvestlines:218-236Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
The dataset consists of stereo images of maize plants captured daily from August 20 to September 6, aimed at constructing a comprehensive dataset for phenotypic analysis and the development of stereo vision models. The images were acquired at two resolutions: 1280×640 and 640×360 pixels. For each resolution, six individual plants were recorded, organized into 20 stereo pairs per plant, resulting in a total of 240 images per resolution. Each stereo pair comprises one image from the left camera and one from the right camera, enabling three-dimensional reconstructions and comparative growth analyses. The 640×360 images correspond to consecutive captures of the 1280×640 images, intended to facilitate future work on devices with lower computational capacity while preserving essential scene information.
Why it matches plant phenotyping methodsトウモロコシの3Dフェノタイピング用ステレオ画像データセットを構築し、ステレオビジョンモデル開発と成長解析を目的とするため、表現型取得基盤が中心です。
titleStereo-Matching Deep Learning Dataset for Maize 3D Phenotyping
Plant height is an important phenotypic trait used to estimate crop height, biomass, and yield for various crops, including cotton. Traditionally, plant heights were manually measured using rulers and tapes, a tedious and time-consuming task. To address such limitations, several studies have developed and applied different techniques for estimating plant height, including stereo-vision camera systems. However, the lack of standardized camera mounting positions across different studies raises concerns about consistency in plant height measurement as the plant height increases, but the camera position does not. To address these limitations, this study investigates how the mounting position of a stereo-vision camera, specifically the ZED2 stereo-camera from StereoLabs, influences the accuracy of cotton plant height estimation. Furthermore, the study proposes an optimal mounting position for the ZED2 camera for accurate cotton height measurements. In our experiment, we used ten potted cotton plants grown and measured weekly for over six weeks. We mounted the ZED2 camera at five predefined positions (H1, H2, H3, H4, H5) directly above the plant. We ensured that the camera was positioned perpendicular to the plant canopy when measuring and recording the plant heights. The manual measurements served as the ground truth for comparative analysis with camera measurements, using both a mixed-effects model and a simple regression model. Experimental results indicated that there were no statistically significant differences between camera measurements and manual measurements when the camera was 38 to 47 inches above the plant canopy. This observation was also demonstrated by a strong linear correlation between the manual and the camera measurement when we ran a simple linear regression model (R 2 = 0.987). The findings from this study emphasize the importance of mounting cameras at the proposed positions to obtain an accurate estimate of plant height, thereby enhancing agronomic field decisions.
Why it matches plant phenotyping methodsステレオカメラによる綿花の草丈という植物形質の推定について、カメラ取付位置を最適化し、手測定を基準に精度を検証しているため、フェノタイピング手法が中心である。
abstractthis study investigates how the mounting position of a stereo-vision camera, specifically the ZED2 stereo-camera from StereoLabs, influences the accuracy of cotton plant height estimation.
Abstract Forests are essential for regulating the climate, enhancement of air quality, and the preservation of biodiversity. However, tree falls pose significant risks to infrastructure, particularly powerlines, leading to widespread blackouts and substantial damage. Traditional methods for monitoring tree fall risks, such as field surveys, are often costly, time-consuming, and lack real-time capabilities. While airborne Light Detection and Ranging (LiDAR) provides precise data for monitoring tree fall risks, it still faces challenges related to frequency of data acquisition and high costs. In response to the European Space Agency's call for more cost-effective monitoring approaches, this study investigates the potential of using very high-resolution optical satellite data, specifically from Pléiades satellite imagery, for assessing tree fall risks to powerlines. Key forest structure metrics such as canopy complexity using the Rumple Index, canopy height, as well as distance to powerlines were analyzed across four study sites in Finland and Switzerland. Sites with simpler canopy structures exhibited stronger correlations between stereo and LiDAR height measurements (R 2 values up to 0.64). Stereo-based measurements can overall provide acceptable accuracy (ca. 96.57%) in detecting trees compared with LiDAR data. The results demonstrated that the Rumple Index can identify areas with simpler canopy structures, where stereo-based height measurements yield high accuracy. These findings suggest the potential of hybrid approaches that integrate both stereo imagery and airborne LiDAR data, tailored to site-specific characteristics, for accurate risk assessments. This study contributes to the ongoing efforts in developing an understanding of vegetation management along powerlines, to inform decision-makers in their endeavors to identify and mitigate risks associated with tree falls.
Why it matches plant phenotyping methods衛星ステレオ画像から樹冠構造・樹高を抽出し、LiDARと比較検証して倒木リスク評価に用いる植物形態計測が中心であるため。
abstractKey forest structure metrics such as canopy complexity using the Rumple Index, canopy height, as well as distance to powerlines were analyzed
Published14 Dec 2024The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 1 · OpenAlex ↗
Abstract. Phenotyping, the measurement of plants physical traits, plays a pivotal role in advancing sustainable agricultural practices. Therefore, developing efficient, low-cost, means to generate such measures is vital. Though image based 2D-driven methods are commonly applied for that purpose due to their processing simplicity, it is clear that only 3D information can offer the necessary plant details. Notwithstanding, the generation of such data is challenged by the requirement to acquire a large set of images or the use of active sensors, which exhibit sensitivity to illumination and require lengthy acquisition campaigns. Consequently, 3D plant phenotyping is presently limited to controlled laboratory conditions and is hardly applied in actual growth setups. To address this shortcoming, this paper argues that by focusing on relevant plant traits, modeling can be simplified and the need for detailed plant reconstruction can be relieved. Accordingly, only a minimal set of images, specifically a stereo pair, can suffice for the reconstruction, thereby providing a low-cost sensing solution. To facilitate the reconstruction, we adapt an anchor-free detection deep neural network and integrate low- and high-level features to accurately detect our plant traits of interest. As the paper demonstrates our adapted network facilitates a robust 3D reconstruction of the entities of interest. Performance analysis demonstrates how our detection is reliable and accurate compared to standard anchor-free frameworks, translating to accurate reconstruction, as we validate against 3D plant scans.
Why it matches plant phenotyping methods植物形質の3D再構成を目的に、ステレオ画像と適応型深層検出ネットワークを開発し、3Dスキャンで精度検証しており、表現型取得手法が研究の中心である。
abstractAccordingly, only a minimal set of images, specifically a stereo pair, can suffice for the reconstruction, thereby providing a low-cost sensing solution.
The breeding of plants with superior traits and the improvement of cultivation means are two essential ways to achieve yield growth and quality promotion. Phenotype, which is the result of the interaction between genes and the environment, plays a key role in understanding plant geometry, growth and development. However, inefficient manual phenotypic measurement has become the main bottleneck restricting the advancement of related technologies. The monocular stereo vision system based on an RGB camera is considered as a promising approach for achieving high-throughput three-dimensional phenotypic data acquisition. This approach is cost-effective, highly efficient, and accurate. This work presents a comprehensive summary of the eight commonly used three-dimensional reconstruction methods in monocular stereo vision, along with three common image acquisition methods (circular, fixed, and straight) applied in plant phenotyping. Through a systematic review of the literature published in the past decade, this paper highlights the application of these systems and matching methods in three-dimensional plant phenotypic research. Additionally, this paper provides a discussion on the advantages and disadvantages of different approaches. At present, monocular stereo vision systems based on a single RGB camera are widely utilized to acquire diverse plant traits due to their affordability and convenience. Different application scenarios have corresponding mechanical structure and data processing methods. Deep learning-based three-dimensional reconstruction methods have demonstrated promising results and significant potential across all three common image acquisition methods. However, the current effectiveness of deep learning in reconstruction requires further validation in the absence of datasets. Moreover, limitations exist in utilizing the results of 3D reconstruction and in the selection of experimental subjects, such as vertical farming. To advance modern breeding and intelligent cultivation, it is imperative to promote dataset collection, diversify the range of research subjects (such as edible fungi and diseased plants), and develop a novel, automated, high-throughput, four-dimensional phenotype platform. As such, monocular stereo vision systems based on an RGB camera, coupled with expanded applications and the development of more efficient reconstruction algorithms, will undoubtedly emerge as a focal point for future researches.
Why it matches plant phenotyping methods植物表現型取得を目的としたRGB単眼ステレオビジョンの3D再構成手法と画像取得法を体系的にレビューしており、方法論が中心である。
abstractThis work presents a comprehensive summary of the eight commonly used three-dimensional reconstruction methods in monocular stereo vision, along with three common image acquisition methods (circular, fixed, and straight) applied in plant phenotyping.
Monitoring tree growth helps operators better understand the growth mechanism of trees and the health status of trees and to formulate more effective management measures. Computer vision technology can quickly restore the three-dimensional geometric structure of trees from two-dimensional images of trees, playing a huge role in planning and managing tree growth. This study used binocular reconstruction technology to measure the height, canopy width, and ground diameter of Castanopsis hystrix and compared the growth differences under different nitrogen levels. In this research, we proposed a wavelet exponential decay thresholding method for image denoising. At the same time, based on the traditional semi-global matching (SGM) algorithm, a cost search direction is added, and a multi-line scanning semi-global matching (MLC-SGM) algorithm for stereo matching is proposed. The results show that the wavelet exponential attenuation threshold method can effectively remove random noise in red cone images, and the denoising effect is better than the traditional hard-threshold and soft-threshold denoising methods. The disparity images produced by the MLC-SGM algorithm have better disparity continuity and noise suppression than those produced by the SGM algorithm, with more minor measurement errors for C. hystrix growth factors. Medium nitrogen fertilization significantly promotes the height, canopy width, and ground diameter growth of C. hystrix. However, excessive fertilization can diminish this effect. Compared to tree height, excessive fertilization has a more pronounced impact on canopy width and ground diameter growth.
Why it matches plant phenotyping methods樹木の高さ・樹冠幅・地際直径を画像から推定する三次元画像計測法を開発・比較検証し、窒素処理への適用も行っており、植物表現型取得法が中心である。
abstractThis study used binocular reconstruction technology to measure the height, canopy width, and ground diameter of Castanopsis hystrix
Accurate pre-harvest yield estimation facilitates the more rational allocation of resources. Previous methods of estimating fruit position, either directly from hardware or through Structure from Motion (SFM), encounter challenges such as high hardware costs, extensive computational resources, and repetitive counting. In this paper, we present a graph optimization-based system that tightly couples dual-frequency GNSS raw measurements and visual-inertial data to estimate pitaya state, including its position and radius. Firstly, the system utilizes a coarse-to-fine approach to estimate the transformations on three axes and align three complementary sensors to a unified coordinate system, thus reducing the computational complexity of pure visual navigation. Secondly, for the first time, this system analytically calculates the pitaya state using the single-view measurement output from the binocular camera, replacing expensive lidar. It employs this calculated state as the initial value, then optimizes the pitaya state and eliminates outliers by multiple reprojection residuals from multi-view measurements. Next, for the first time, we integrate ionospheric-free pseudorange and Doppler residuals into the visual-inertial factor graph, thereby mitigating multipath interference in double-layer film greenhouse and providing a unique ID and position for each pitaya. Finally, we calculate the weight of each pitaya using a cubic polynomial based on its radius and estimate the overall greenhouse yield by summing all individual weights. In greenhouse experiments, our system achieved an Root Mean Square Error (RMSE) of 7 cm for position estimation, 7 mm for radius estimation, and a weight Mean Absolute Percentage Error (MAPE) of about 16% for individual pitaya. Overall, our system can provide cost-effective, real-time, non-repetitive, and low-missing pitaya yield estimation to assist greenhouse managers in decision-making.
Why it matches plant phenotyping methodsGNSS・双眼カメラ・慣性センサを統合し、ピタヤの位置・半径・重量を推定する計測システムを開発し、誤差評価まで実施しているため、植物器官の表現型取得が中心である。
abstractwe present a graph optimization-based system that tightly couples dual-frequency GNSS raw measurements and visual-inertial data to estimate pitaya state, including its position and radius.
Sugarcane tip cutting is essential to reducing the rate of impurities in the harvest. To achieve adaptive regulation of the tip-cutting position by a sugarcane harvester, we propose a method for estimating the height of the tip-cutting position of sugarcane. The RGB and Binocular depth cameras are aligned to process the sugarcane tip region image. This involves threshold segmentation, morphological operations, and contour detection to identify the tip-cutting position and upper boundary contours. The depth image is segmented using contour pixel information and merged to form a colour depth image of the sugarcane's tip. This image is then transformed using depth data and triangular parallax principles to determine the height of the sugarcane tip-cutting position. The proposed method was evaluated in various sugarcane plantation environments. Comparative analysis between the proposed method and manual measurements of actual cutting position heights revealed that the RMSE ranged from 1.22 cm to 1.78 cm, and R² varied between 0.79 and 0.86. These results demonstrate the effectiveness of the proposed method in accurately extracting the height information of sugarcane tip-cutting positions, which has a specific application value for the adaptive adjustment of the tip-cutting device of the sugarcane harvester.
Why it matches plant phenotyping methodsRGB・深度画像の融合、画像処理、三角測量によりサトウキビ先端の切断位置高さを推定し、実測値と比較検証しているため、植物形質取得法が研究の中心です。
abstractwe propose a method for estimating the height of the tip-cutting position of sugarcane
The Normalized Difference Vegetation Index (NDVI) is a valuable indicator of plant vigor that is frequently used in agronomic practices to make timely and targeted decisions with the aim of increasing the productivity of the system. NDVI measurements of large-scale fields are typically performed using remote sensing from satellite and aerial imaging devices. However, due to their low spatial and temporal resolution, these technologies may have limitations in precision viticulture. This paper investigates the potential of a proximal sensing system to characterize the vine foliage that makes use of data collected by a farmer robot equipped with an Intel RealSense D435 camera. The camera includes two infrared (IR) sensors in stereoscopic configuration and one RGB sensor, which provide, for each observation, both infrared and visible red channel information, thus making possible pixel-per-pixel NDVI calculation. Solutions to IR filtering and radiometric calibration issues are proposed that significantly improve measurement accuracy and reliability. Since the camera also provides stereo-based 3D scene reconstruction, depth information can be used to separate the plant canopy from the background before NDVI measurement. At the same time, range data can be employed to extract geometric properties of the crop, such as plant height and/or volume. The system is validated in the field in a commercial vineyard at different phenological stages, from the formation of the berries to leaf discoloration and fall. Experimental results show good agreement compared with ground truth provided by a GreenSeeker, with an average percentage error in the NDVI estimation of 4.6% and a R2 of 0.87 tested over the whole grapevine cycle. Therefore, the proposed sensing system could be a feasible solution to automated NDVI estimation at plant-scale.
Why it matches plant phenotyping methods植物スケールのNDVI・樹冠形状を取得する近接センシングシステムを開発し、放射補正やIRフィルタリングを提案して圃場検証しているため、植物フェノタイピング手法が中心である。
abstractThis paper investigates the potential of a proximal sensing system to characterize the vine foliage that makes use of data collected by a farmer robot equipped with an Intel RealSense D435 camera.
Field / plotMultispectral / hyperspectralStereoFruitClassificationFruit / seed / panicle traits
Fruit ripeness estimation models have for decades depended on spectral index features or colour-based features, such as mean, standard deviation, skewness, colour moments, and/or histograms for learning traits of fruit ripeness. Recently, few studies have explored the use of deep learning techniques to extract features from images of fruits with visible ripeness cues. However, the blackberry (Rubus fruticosus) fruit does not show obvious and reliable visible traits of ripeness when mature and therefore poses great difficulty to fruit pickers. The mature blackberry, to the human eye, is black before, during, and post-ripening. To address this problem, this paper proposes a novel multi-input convolutional neural network (CNN) ensemble classifier (MCE) for detecting subtle traits of ripeness in blackberry fruits. The multi-input CNN was created from a pre-trained visual geometry group 16-layer deep convolutional network (VGG16) model trained on the ImageNet dataset. The fully connected layers were optimized for learning traits of ripeness of mature blackberry fruits. The resulting model of about 600 K trainable parameters served as the base for building homogeneous ensemble learners t ensembled using the stack generalization ensemble (SGE) framework. The input to the network are images acquired with a stereo sensor using visible and near-infrared (Vis-NIR) spectral filters at wavelengths of 700 nm and 770 nm. Through experiments, the proposed model achieved 95.1 % accuracy on unseen sets and 90.2 % accuracy with in-field conditions. Further experiments reveal that machine sensory is highly and positively correlated to human sensory over blackberry fruit skin texture
Why it matches plant phenotyping methodsブラックベリー果実の成熟度という植物器官の状態を、Vis-NIRステレオ画像とCNNアンサンブルで推定する手法を開発・評価しており、表現型取得・抽出が研究の中心である。
abstractthis paper proposes a novel multi-input convolutional neural network (CNN) ensemble classifier (MCE) for detecting subtle traits of ripeness in blackberry fruits.
Field / plotRGB / grayscaleStereoObject detectionSegmentation
LITERAL is a lightweight, portable high-throughput phenotyping tool. It meets the need for low-cost, easy-to-use yet accurate measuring equipment for monitoring small plot trials or a network of agricultural plots. In practical terms, it integrates a set of sensors, including three high-resolution cameras, connected to an acquisition box that triggers acquisitions, stores data, and communicates with a tablet PC that enables measurement scenarios to be defined via a user-friendly graphic interface. The measurement scenario describes the configuration of each sensor, the test plan, and the number of measurements in each plot. This makes it easy to use in the field and ensures that each image is correctly referenced. Once downloaded, the data are analyzed by a modular processing chain, implementing generic processing algorithms: semantic segmentation, object detection by deep learning, colorimetric analysis, stereovision. These algorithms can be parameterized by culture to achieve high precision. The quality of the images acquired, and the many possible configurations mean that LITERAL can be used for a wide range of uses: monitoring the growth of field crops and trees, characterizing mixed crops, quantifying the symptoms of leaf diseases, measuring the density of plants or fruits, etc. Ergonomic and scalable, LITERAL has been developed as part of a CASDAR project led by ARVALIS and involving INRAE1, GEVES2, Terres Inovia3, ITB4, CTIFL5 and HIPHEN. It is currently used by technical teams in France, Portugal, USA and Australia. Wider distribution is planned from 2024.
Why it matches plant phenotyping methods植物形質の取得を目的とした携帯型ハイスループット・フェノタイピングシステムの開発と構成、画像解析ワークフローを中心に記述しており、方法が研究の中核です。
abstractLITERAL is a lightweight, portable high-throughput phenotyping tool.
This article addresses the challenges of measuring the 3D architecture traits, such as height and volume, of fruit tree canopies, constituting information that is essential for assessing tree growth and informing orchard management. The traditional methods are time-consuming, prompting the need for efficient alternatives. Recent advancements in unmanned aerial vehicle (UAV) technology, particularly using Light Detection and Ranging (LiDAR) and RGB cameras, have emerged as promising solutions. LiDAR offers precise 3D data but is costly and computationally intensive. RGB and photogrammetry techniques like Structure from Motion and Multi-View Stereo (SfM-MVS) can be a cost-effective alternative to LiDAR, but the computational demands still exist. This paper introduces an innovative approach using UAV-based single-lens stereoscopic photography to overcome these limitations. This method utilizes color variations in canopies and a dual-image-input network to generate a detailed canopy height map (CHM). Additionally, a block structure similarity method is presented to enhance height estimation accuracy in single-lens UAV photography. As a result, the average rates of growth in canopy height (CH), canopy volume (CV), canopy width (CW), and canopy project area (CPA) were 3.296%, 9.067%, 2.772%, and 5.541%, respectively. The r2 values of CH, CV, CW, and CPA were 0.9039, 0.9081, 0.9228, and 0.9303, respectively. In addition, compared to the commonly used SFM-MVS approach, the proposed method reduces the time cost of canopy reconstruction by 95.2% and of the cost of images needed for canopy reconstruction by 88.2%. This approach allows growers and researchers to utilize UAV-based approaches in actual orchard environments without incurring high computation costs.
Why it matches plant phenotyping methodsUAV単眼ステレオ画像から樹冠の高さ・体積・幅・投影面積を抽出する手法を開発し、精度と既存法との性能を検証しており、植物表現型取得が研究の中心である。
abstractThis paper introduces an innovative approach using UAV-based single-lens stereoscopic photography to overcome these limitations.
Accurate extraction of three-dimensional (3D) vegetation is essential for monitoring urban ecological environments and carbon sinks. Two-dimensional vegetation data in cities has been widely researched. However, large-scale urban vegetation height inventories are lacking. This study proposes a novel framework for 3D extraction of urban vegetation, which can be widely applied based on remote sensing approaches. A multi-task convolutional neural network is established to extract the urban vegetation cover and estimate the vegetation height at the pixel level. The results indicate that this method can derive the complete urban vegetation cover and height from stereo satellite data. Compared with the traditional stereo-photogrammetry method, this method enables rapid inference of vegetation height in urban areas with a root mean square error (RMSE) of 3.16 m. This model is capable of accurately separating vegetation in complex urban environments and performs well despite shadow effects. Furthermore, in this study, the first vegetation height map with 1-m spatial resolution has been produced, covering six urban districts in Beijing (approximately 1,378 km2). It only takes 2–3 min to process the imagery of the whole study area. The high-resolution map can display more urban vegetation details over the existing 10 m/30 m resolution vegetation height maps. Furthermore, the established framework and benchmark for urban vegetation 3D information offer unique insights and provide a basis for further research.
Why it matches plant phenotyping methods都市植生の被覆と高さという植物キャノピー形質を、深層学習とステレオ衛星データから抽出・推定する手法を開発し、従来法と比較検証しているため、方法が研究の中心である。
abstractThis study proposes a novel framework for 3D extraction of urban vegetation
MaizeGrowth chamberStereoRoot2D/3D reconstructionGrowth / time-series analysisRoot system architecture
Background The root of a plant is a fundamental organ for the multisensory perception of the environment. Investigating root growth dynamics as a mean of their interaction with the environment is of key importance for improving knowledge in plant behaviour, plant biology and agriculture. To date, it is difficult to study roots movements from a dynamic perspective given that available technologies for root imaging focus mostly on static characterizations, lacking temporal and three-dimensional (3D) spatial information. This paper describes a new system based on time-lapse for the 3D reconstruction and analysis of roots growing in hydroponics. Results The system is based on infrared stereo-cameras acquiring time-lapse images of the roots for 3D reconstruction. The acquisition protocol guarantees the root growth in complete dark while the upper part of the plant grows in normal light conditions. The system extracts the 3D trajectory of the root tip and a set of descriptive features in both the temporal and frequency domains. The system has been used on Zea mays L. (B73) during the first week of growth and shows good inter-reliability between operators with an Intra Class Correlation Coefficient (ICC) > 0.9 for all features extracted. It also showed measurement accuracy with a median difference of Conclusions The system and the protocol presented in this study enable accurate 3D analysis of primary root growth in hydroponics. It can serve as a valuable tool for analysing real-time root responses to environmental stimuli thus improving knowledge on the processes contributing to roots physiological and phenotypic plasticity.
Why it matches plant phenotyping methods根の3D動態を取得・解析する画像計測システムを開発し、特徴量の信頼性と精度を検証しており、植物表現型取得が中心である。
abstractThis paper describes a new system based on time-lapse for the 3D reconstruction and analysis of roots growing in hydroponics.
Reproduction assets foundThe paper's 3D root tip trajectory data (phenotyping measurements from maize root imaging) are publicly deposited on Zenodo. The analysis software and scripts are only available upon request, so they qualify as request_only.Dataset · publicData describing 3D trajectories used in this paper are available here: https://zenodo.org/record/8422242 . Software and scripts are available for research purposes upon request through the email address: mindtheplantlab@gmail.com.Open asset ↗Zenodo · 8422242lines:141-172Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Crop leaf length, perimeter, and area serve as vital phenotypic indicators of crop growth status, the measurement of which is important for crop monitoring and yield estimation. However, processing a leaf point cloud is often challenging due to cluttered, fluctuating, and uncertain points, which culminate in inaccurate measurements of leaf phenotypic parameters. To tackle this issue, the RKM-D point cloud method for measuring leaf phenotypic parameters is proposed, which is based on the fusion of improved Random Sample Consensus with a ground point removal (R) algorithm, the K-means clustering (K) algorithm, the Moving Least Squares (M) method, and the Euclidean distance (D) algorithm. Pepper leaves were obtained from three growth periods on the 14th, 28th, and 42nd days as experimental subjects, and a stereo camera was employed to capture point clouds. The experimental results reveal that the RKM-D point cloud method delivers high precision in measuring leaf phenotypic parameters. (i) For leaf length, the coefficient of determination (R2) surpasses 0.81, the mean absolute error (MAE) is less than 3.50 mm, the mean relative error (MRE) is less than 5.93%, and the root mean square error (RMSE) is less than 3.73 mm. (ii) For leaf perimeter, the R2 surpasses 0.82, the MAE is less than 7.30 mm, the MRE is less than 4.50%, and the RMSE is less than 8.37 mm. (iii) For leaf area, the R2 surpasses 0.97, the MAE is less than 64.66 mm2, the MRE is less than 4.96%, and the RMSE is less than 73.06 mm2. The results show that the proposed RKM-D point cloud method offers a robust solution for the precise measurement of crop leaf phenotypic parameters.
Why it matches plant phenotyping methods葉の形態形質を点群から抽出するRKM-D法を開発し、精度を検証した研究であり、植物フェノタイピング手法が中心である。
abstractthe RKM-D point cloud method for measuring leaf phenotypic parameters is proposed
Forests are a vital source of food, fuel, and medicine and play a crucial role in climate change mitigation. Strategic and policy decisions on forest management and conservation require accurate and up-to-date information on available forest resources. Forest inventory data such as tree parameters, heights, and crown diameters must be collected and analysed to monitor forests effectively. Traditional manual techniques are slow and labour-intensive, requiring additional personnel, while existing non-contact methods are costly, computationally intensive, or less accurate. Kenya plans to increase its forest cover to 30% by 2032 and establish a national forest monitoring system. Building capacity in forest monitoring through innovative field data collection technologies is encouraged to match the pace of increase in forest cover. This study explored the applicability of low-cost, non-contact tree inventory based on stereoscopic photogrammetry in a recently reforested stand in Kieni Forest, Kenya. A custom-built stereo camera was used to capture images of 251 trees in the study area from which the tree heights and crown diameters were successfully extracted quickly and with high accuracy. The results imply that stereoscopic photogrammetry is an accurate and reliable method that can support the national forest monitoring system and REDD+ implementation.
Why it matches plant phenotyping methodsステレオ写真測量を用いて樹高と樹冠径を非接触で抽出し、精度と信頼性を評価しているため、植物形質取得手法が研究の中心です。
abstractThis study explored the applicability of low-cost, non-contact tree inventory based on stereoscopic photogrammetry
Reproduction assets foundThe paper's Data Availability Statement explicitly states that the supporting data and software code for the stereoscopic photogrammetry tree inventory (tree height, crown diameter, DBH extraction from stereo images of 251 trees in Kieni Forest) are publicly available on the authors' GitHub repository DeKUT-DSAIL/TreeVCode · publicData Availability Statement: The data that support the findings of this study, as well as the software
code, are publicly available on GitHub: https://github.com/DeKUT-DSAIL/TreeVision (accessed on
11 August 2023).Open asset ↗DeKUT-DSAIL/TreeVisionpdf-page:11 lines:1-61Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 7 Sept 2026
Automated precision weed control requires visual methods to discriminate between crops and weeds. State-of-the-art plant detection methods fail to reliably detect weeds, especially in dense and occluded scenes. In the past, using hand-crafted detection models, both color (RGB) and depth (D) data were used for plant detection in dense scenes. Remarkably, the combination of color and depth data is not widely used in current deep learning-based vision systems in agriculture. Therefore, we collected an RGB-D dataset using a stereo vision camera. The dataset contains sugar beet crops in multiple growth stages with a varying weed densities. This dataset was made publicly available and was used to evaluate two novel plant detection models, the D-model, using the depth data as the input, and the CD-model, using both the color and depth data as inputs. For ease of use, for existing 2D deep learning architectures, the depth data were transformed into a 2D image using color encoding. As a reference model, the C-model, which uses only color data as the input, was included. The limited availability of suitable training data for depth images demands the use of data augmentation and transfer learning. Using our three detection models, we studied the effectiveness of data augmentation and transfer learning for depth data transformed to 2D images. It was found that geometric data augmentation and transfer learning were equally effective for both the reference model and the novel models using the depth data. This demonstrates that combining color-encoded depth data with geometric data augmentation and transfer learning can improve the RGB-D detection model. However, when testing our detection models on the use case of volunteer potato detection in sugar beet farming, it was found that the addition of depth data did not improve plant detection at high vegetation densities.
Why it matches plant phenotyping methodsRGB-Dデータセットと植物検出モデルを開発・評価し、密集環境での植物個体の画像ベース検出を中心的に扱っているため、植物フェノタイピング手法として収録する。
abstractTherefore, we collected an RGB-D dataset using a stereo vision camera.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
The increasing demand for quality, year-round food production in limited space has led to the widespread adoption of protected cropping. Effectively monitoring and maintaining crops within these facilities requires substantial labour and expertise. Traditional manual monitoring is labour intensive and time consuming. Therefore, non-destructive image-based techniques, particularly those utilising 3D structural data, have gained attention. We developed a stereo vision-based system to estimate the height of vertically supported tall plants in protected facilities, given plant height serves as a vital measure of crop growth. Our system uses a mobile platform with a top-angle view of a stereo vision depth camera for data acquisition and machine learning in its core for data analysis. First, we collected weekly RGB and depth (RGBD) streams from plant gutters in three glasshouse compartments with different light treatments. We used part of the RGB data collected to train and validate a deep learning segmentation model to detect plant tops and bases. Detected tops and bases of an image were then mapped to the generated 3D scene using the depth image of the same frame. Thresholds and 3D clustering are used respectively to remove background and eliminate outliers in top and base detection mapped to 3D space. Finally, the height of each plant was calculated using the cluster centres of the tops and bases of the plants. Manually measured heights of ten selected plants per environment were used to validate the height estimations. Similar growing patterns were observed between imaged and manually measured plant heights, which showed strong correlations of 0.87, 0.96, and 0.79 R2 scores, respectively, under unfiltered ambient light, Smart Glass film, and shifted light. These promising results demonstrate the feasibility of our proposed method for a vertically supported capsicum crop in a commercial-scale protected crop facility.
Why it matches plant phenotyping methodsステレオビジョンと機械学習を用いて植物体高を推定する手法を開発し、手動測定で検証しており、植物表現型の取得が研究の中心である。
abstractWe developed a stereo vision-based system to estimate the height of vertically supported tall plants in protected facilities
In intensively managed forests in Europe, where forests are divided into stands of small size and may show heterogeneity within stands, a high spatial resolution (10–––20 m) is needed to capture the differences in canopy height. In this work, we developed a deep learning model based on multi-sensor remote sensing measurements to create a high-resolution canopy height map over the “Landes de Gascogne” forest in France, a large maritime pine plantation of 13,000 km2 with flat terrain and intensive management. This area is characterized by even-aged and mono-specific stands, of a typical length of a few hundred meters, harvested every 35 to 50 years. Our deep learning U-Net model uses multi-band images from Sentinel-1 and Sentinel-2 with composite time averages as input to predict tree height derived from GEDI waveforms. The evaluation is performed with external validation data from forest inventory plots and a stereo 3D reconstruction model based on Skysat imagery available at specific locations. We trained seven different U-Net models based on combinations of Sentinel-1 and Sentinel-2 bands to evaluate the importance of each sensor in the dominant height retrieval. The model outputs allow us to generate a 10 m resolution canopy height map of the whole “Landes de Gascogne” forest area for 2020 with a mean absolute error of 2.02 m on the test dataset. The best predictions were obtained using all available bands from Sentinel-1 and Sentinel-2 but using only one satellite source also provided good predictions. For all validation datasets in coniferous forests, our model showed better metrics than previous canopy height models available in the same region.
Why it matches plant phenotyping methods衛星・GEDI・ステレオ画像を用いて樹高を推定する深層学習手法を開発・検証し、森林全体の林冠高マップを生成しているため、植物形質取得法が研究の中心である。
abstractwe developed a deep learning model based on multi-sensor remote sensing measurements to create a high-resolution canopy height map
High-quality agricultural multi-view stereo reconstruction technology is the key to precision and informatization in agriculture. Multi-view stereo reconstruction methods are an important part of 3D vision technology. In the multi-view stereo 3D reconstruction method based on deep learning, the effect of feature extraction directly affects the accuracy of reconstruction. Aiming at the actual problems in orchard fruit tree reconstruction, this paper designs an improved multi-view stereo structure based on the combination of remote sensing and artificial intelligence to realize the accurate reconstruction of jujube tree trunks. Firstly, an automatic key frame extraction method is proposed for the DSST target tracking algorithm to quickly recognize and extract high-quality data. Secondly, a composite U-Net feature extraction network is designed to enhance the reconstruction accuracy, while the DRE-Net feature extraction enhancement network improved by the parallel self-attention mechanism enhances the reconstruction completeness. Comparison tests show different levels of improvement on the Technical University of Denmark (DTU) dataset compared to other deep learning-based methods. Ablation test on the self-constructed dataset, the MVSNet + Co U-Net + DRE-Net_SA method proposed in this paper improves 20.4% in Accuracy, 12.8% in Completion, and 16.8% in Overall compared to the base model, which verifies the real effectiveness of the scheme.
Why it matches plant phenotyping methodsユジュベ樹幹の3D形態をUAVマルチビュー画像から再構成する画像・計算手法が研究の中心であり、比較試験とアブレーション試験で検証されている。
abstractthis paper designs an improved multi-view stereo structure based on the combination of remote sensing and artificial intelligence to realize the accurate reconstruction of jujube tree trunks.
Background The leaf angle distribution (LAD) is an important structural parameter of agricultural crops that influences light interception, radiation fluxes and consequently plant performance. Therefore, LAD and its parametrized form, the Beta distribution, is used in many photosynthesis models. However, in field cultivations, these parameters are difficult to assess and cereal crops in particular pose challenges since their leaves are thin, flexible, and often bent and twisted around their own axis. To our knowledge, there is only a very limited set of methods currently available to calculate LADs of field-grown cereal crops that explicitly takes these special morphological properties into account. Results In this study, a new processing pipeline is introduced that allows for the generation of realistic leaf surface models and the analysis of LADs of field-grown cereal crops from 3D point clouds. The data acquisition is based on a convenient stereo imaging setup. The approach was validated with different artificial targets and results on the accuracy of the 3D reconstruction, leaf surface modeling and calculated LAD are given. The mean error of the 3D reconstruction was below 1 mm for an inclination angle range between 0° and 75° and the leaf surface could be quantified with an average accuracy of 90%. The concordance correlation coefficient (CCC) of 99.6% (p-value = [Formula: see text]) indicated a high correlation between the reconstructed inclination angle and the identity line. The LADs for bent leaves were reconstructed with a mean error of 0.21° and a standard deviation of 1.55°. As an additional parameter, the insertion angle was reconstructed for the artificial leaf model with an average error Conclusion This study shows that our image processing pipeline can reconstruct the complex leaf shape of cereal crops from stereo images. The high accuracy of the approach was demonstrated with several validation experiments including artificial leaf targets. The derived leaf models were used to calculate LADs for artificial leaves and naturally grown cereal crops. This helps to better understand the influence of the canopy structure on light absorption and plant performance and allows for a more precise parametrization of photosynthesis models via the derived Beta distributions.
Why it matches plant phenotyping methodsステレオ画像から3D葉面モデルを構築し、葉角度分布や挿入角を推定する画像解析パイプラインを開発・検証しており、植物表現型取得が研究の中心である。
abstracta new processing pipeline is introduced that allows for the generation of realistic leaf surface models and the analysis of LADs of field-grown cereal crops from 3D point clouds
Precision agriculture relies on understanding crop growth dynamics and plant responses to short-term changes in abiotic factors. In this technical note, we present and discuss a technical approach for cost-effective, non-invasive, time-lapse crop monitoring that automates the process of deriving further plant parameters, such as biomass, from 3D object information obtained via stereo images in the red, green, and blue (RGB) color space. The novelty of our approach lies in the automated workflow, which includes a reliable automated data pipeline for 3D point cloud reconstruction from dynamic scenes of RGB images with high spatio-temporal resolution. The setup is based on a permanent rigid and calibrated stereo camera installation and was tested over an entire growing season of winter barley at the Global Change Experimental Facility (GCEF) in Bad Lauchstädt, Germany. For this study, radiometrically aligned image pairs were captured several times per day from 3 November 2021 to 28 June 2022. We performed image preselection using a random forest (RF) classifier with a prediction accuracy of 94.2% to eliminate unsuitable, e.g., shadowed, images in advance and obtained 3D object information for 86 records of the time series using the 4D processing option of the Agisoft Metashape software package, achieving mean standard deviations (STDs) of 17.3–30.4 mm. Finally, we determined vegetation heights by calculating cloud-to-cloud (C2C) distances between a reference point cloud, computed at the beginning of the time-lapse observation, and the respective point clouds measured in succession with an absolute error of 24.9–35.6 mm in depth direction. The calculated growth rates derived from RGB stereo images match the corresponding reference measurements, demonstrating the adequacy of our method in monitoring geometric plant traits, such as vegetation heights and growth spurts during the stand development using automated workflows.
Why it matches plant phenotyping methodsRGBステレオ画像から3D点群を再構成し、植生高や成長率を自動抽出するワークフローの開発・検証が研究の中心であるため、植物フェノタイピング手法として含める。
abstractwe present and discuss a technical approach for cost-effective, non-invasive, time-lapse crop monitoring that automates the process of deriving further plant parameters, such as biomass, from 3D object information obtained via stereo images
Abstract Canopy architecture traits are associated with productivity in sorghum [Sorghum bicolor (L.) Moench], and they are commonly measured at the time of flowering or harvest. Little is known about the dynamics of canopy architecture traits through the growing season. Utilizing the ground‐based high‐throughput phenotyping system Phenobot 1.0, we collected stereo images of a photoperiod‐sensitive and a photoperiod‐insensitive population over time to generate three‐dimensional (3D) representations of the canopy. Four descriptors were automatically extracted from the 3D point clouds: plot‐based plant height (PBPH), plot‐based plant width (PBPW), plant surface area (PSA), and convex hull volume (CHV). Additionally, genotypic growth rates were estimated for each canopy descriptor. Genome‐wide association analysis was performed on individual timepoints and the growth rates in both populations. We detected genotypic variation for each of the four canopy descriptors and their growth rates and discovered novel genomic regions associated with growth rates on chromosomes 1 (PBPH, CHV), 3 (PBPH), 4 (PBPH, PBPW), 5 (PBPH), 8 (PSA), and 9 (PBPW). These results provide new knowledge about the genetic control of canopy architecture, highlighting genomic regions that can be targeted in plant breeding programs.
Why it matches plant phenotyping methodsPhenobot 1.0によるステレオ画像取得、3D再構成、キャノピー形質の自動抽出が研究の主要な測定ワークフローであり、植物表現型解析への実質的な適用に該当する。
abstractUtilizing the ground‐based high‐throughput phenotyping system Phenobot 1.0, we collected stereo images of a photoperiod‐sensitive and a photoperiod‐insensitive population over time to generate three‐dimensional (3D) representations of the canopy.
Common beanStereoWhole plant / canopy / plot / field2D/3D reconstructionTrackingGrowth / development / phenology
Climbing plants, such as common beans ( Phaseolus vulgaris L.), exhibit complex motion patterns that have long captivated researchers. In this study, we introduce a stereo vision machine system for the in-depth analysis of the movement of climbing plants, using image processing and computer vision. Our approach involves two synchronized cameras, one lateral to the plant and the other overhead, enabling the simultaneous 2D position tracking of the plant tip. These data are then leveraged to reconstruct the 3D position of the tip. Furthermore, we investigate the impact of external factors, particularly the presence of support structures, on plant movement dynamics. The proposed method is able to extract the position of the tip in 86-98% of cases, achieving an average reprojection error below 4 px, which means an approximate error in the 3D localization of about 0.5 cm. Our method makes it possible to analyze how the plant nutation responds to its environment, offering insights into the interplay between climbing plants and their surroundings.
Why it matches plant phenotyping methodsクライミング植物の先端位置とナテーション運動を抽出するステレオビジョン手法を開発し、抽出率と3D定位誤差を評価しており、植物フェノタイピング手法が研究の中心である。
abstractwe introduce a stereo vision machine system for the in-depth analysis of the movement of climbing plants, using image processing and computer vision.
Field / plotRGB / grayscaleStereoObject detectionSegmentationPigment / colour / senescence
LITERAL is a lightweight, portable high-throughput phenotyping tool. It meets the need for low-cost, easyto-use yet accurate measuring equipment for monitoring small plot trials or a network of agricultural plots. In practical terms, it integrates a set of sensors, including three high-resolution cameras, connected to an acquisition box that triggers acquisitions, stores data, and communicates with a tablet PC that enables measurement scenarios to be defined via a user-friendly graphic interface. The measurement scenario describes the configuration of each sensor, the test plan, and the number of measurements in each plot. This makes it easy to use in the field and ensures that each image is correctly referenced. Once downloaded, the data are analyzed by a modular processing chain, implementing generic processing algorithms: semantic segmentation, object detection by deep learning, colorimetric analysis, stereovision. These algorithms can be parameterized by culture to achieve high precision. The quality of the images acquired, and the many possible configurations mean that LITERAL can be used for a wide range of uses: monitoring the growth of field crops and trees, characterizing mixed crops, quantifying the symptoms of leaf diseases, measuring the density of plants or fruits, etc. Ergonomic and scalable, LITERAL has been developed as part of a CASDAR project led by ARVALIS and involving INRAE 1 , GEVES 2 , Terres Inovia 3 , ITB 4 , CTIFL 5 and HIPHEN. It is currently used by technical teams in France, Portugal, USA and Australia. Wider distribution is planned from 2024.
Why it matches plant phenotyping methods携帯型の高スループット植物フェノタイピング装置と、画像取得・解析ワークフローを開発・提供する研究であり、植物形質の取得方法が中心的です。
abstractLITERAL is a lightweight, portable high-throughput phenotyping tool.
When unmanned aerial vehicles (UAVs) are used for orchard chemicals application, accurate measurement of the canopy volume can provide decision support for determining pesticide dosages, flight parameters, and droplet sizes.Using binocular camera ranging, this study presents a novel canopy segmentation algorithm that preprocesses light detection ranging data to extract sub-grid canopy volumes. A binocular vision-based canopy volume extraction system for UAV chemical application was developed. The system utilizes multi-degree-of-freedom adaptive balance technology to ensure that the binocular camera can still vertically detect the canopy even when the flight attitude changes. Performance experiments were conducted using artificial fruit trees with different leaf densities and regular cardboard box as measurement targets. The canopy volume measurements indicate that the new model accurately detects target contours. When flying at 2 m/s, the maximum errors between system-measured and actual volumes were 6.58 and 9.37% for the rectangular and triangular, respectively. Increasing speeds and attitudes lead to increased errors and measurement variations. However, the position of the system relative to the target does not cause significant differences in results. The maximum measurement errors between system-measured and actual LiDAR values were 6.44 and 9.17% for high- and low-density canopies, respectively. These results demonstrate that the proposed system has high measurement accuracy and provides a reliable precision UAV pesticide-spraying control system for plant protection based on real-time canopy detection.
Why it matches plant phenotyping methods二眼カメラとLiDARを用いて植物キャノピー体積を抽出・測定するシステムを開発し、実測値との精度検証も行っており、フェノタイピング手法が中心である。
abstractthis study presents a novel canopy segmentation algorithm that preprocesses light detection ranging data to extract sub-grid canopy volumes.
In this protocol, we present a noninvasive in planta bioimaging technique for the analysis of hydrogen peroxide (H 2 O 2 ) and glutathione redox potential in adult Arabidopsis thaliana plants. The technique is based on the use of stereo fluorescence microscopy to image A. thaliana plants expressing the two genetically encoded fluorescent sensors roGFP2-Orp1 and Grx1-roGFP2. We provide a detailed step-by-step protocol for performing low magnification imaging with mature plants grown in soil or hydroponic systems. This protocol aims to serve the scientific community by providing an accessible approach to noninvasive in planta bioimaging and data analysis.
Why it matches plant phenotyping methods植物体内の酸化還元状態を蛍光イメージングで非侵襲的に測定する手順を中心としたプロトコルであり、植物の生理状態を取得するフェノタイピング手法に該当する。
abstractIn this protocol, we present a noninvasive in planta bioimaging technique for the analysis of hydrogen peroxide (H 2 O 2 ) and glutathione redox potential in adult Arabidopsis thaliana plants.
Understanding and monitoring the surrounding environment increasingly rely on its 3-D representations. However, the often high costs of 3-D data equipment limit its wide usage, and low-cost solutions are in demand. Here, we propose a novel approach based on spherical stereo videos captured with a known baseline (distance between the cameras) for a low-cost and efficient 3-D point cloud reconstruction. In a forest environment, we evaluated 1) the influence of baseline length on point cloud quality and 2) the suitability of the generated point clouds for extracting primary forest attributes (tree position and diameter). Our results show that the proposed approach allows for feasible 3-D reconstruction of complex forest plots. The highest point cloud quality was achieved with a baseline of 60 cm. This setup enabled the correct detection of more than 65% of the trees within the forest plots, producing an average tree position error between 30 and 50 cm and clearly outperforming other setups. A multiscale model-to-model cloud comparison analysis showed signed distances between the generated point cloud and the reference data with zero mean and 1 m standard deviation. We demonstrate that the proposed approach can be a valuable low-cost solution for 3-D point cloud reconstruction, facilitating forest assessment and monitoring.
Why it matches plant phenotyping methods森林プロットの3D再構成手法を開発・評価し、樹木位置と直径という個体レベルの植物形質抽出性能を検証しており、フェノタイピング手法が中心である。
abstractwe propose a novel approach based on spherical stereo videos captured with a known baseline (distance between the cameras) for a low-cost and efficient 3-D point cloud reconstruction
Optical 3D measurement technology plays a vital role in diverse industries, particularly with the advancements in line laser scanning 3D imaging. In this paper, we propose a line laser scanning-based investigation for detecting Carya cathayensis Sarg. The Carya cathayensis Sarg specimens are scanned using a line laser to achieve three-dimensional reconstruction, enabling the calculation of their volume and quantity based on the acquired point cloud map. Through binocular acquisition and subsequent point cloud alignment and fusion, the error in the three-dimensional reconstruction is significantly reduced. The point cloud map facilitates the automatic identification of the number of scanned areas of Carya cathayensis Sarg areas and accurate volume calculations, with an error control of approximately 0.6% when compared to the actual volume. The application of this research in agriculture allows farmers to classify fruit sizes and optimize their selection, thus facilitating intelligent agricultural practices.
Why it matches plant phenotyping methods線レーザースキャンと点群融合を用いて植物果実の3D再構成、個数・体積推定を行う測定法が研究の中心であり、実測値との誤差による技術検証も含むため。
abstractThe Carya cathayensis Sarg specimens are scanned using a line laser to achieve three-dimensional reconstruction, enabling the calculation of their volume and quantity based on the acquired point cloud map.
It is necessary to recognize the tomato pollination features for the designing demand of intelligent and precise tomato supplementary pollination equipment. Mentioned pollination features include flower opening state and the three-dimensional position and pose of flower anther. Tomato flower pollination features recognition method is designed in this paper based on the deep learning full opened flower recognition model and binocular template matching three-dimensional information recognition method. First of all, a binocular stereo vision system is built to acquire the tomato flower images in greenhouse with natural lighting. Which can help vision system avoid the impact of inconsistent light intensity on image recognition. The acquired images were equalized with three groups parameters to labeled images. And the improved MC-AlexNet deep learning model is established to recognize the full opened tomato flowers in the left image of image pair acquired with binocular vision system. Then, the template is created with the full opened flower recognition result of deep learning model in left image based on gray value and deformation template matching method. And the template matching is established to recognize the corresponding full opened flowers in the right image of image pair. Finally, with the template matching result, the anther segmentation is conducted to calculate three-dimensional position and pose of anther. The experimental results show that the accuracy of full opened tomato flowers recognition model is 96.23%. The average position recognition deviation of anther is 5.94 mm. And the average anther pose recognition deviation in three plane is 6.24° for the images that anthers can be observed. And the average time consuming is about 143.18 ms per image pair. It can be concluded that the method of binocular template matching established in this paper can fulfill the demand of supplementary pollination equipment design, and the research result lays a foundation for designing and improvement of tomato supplementary pollination equipment in greenhouse.
Why it matches plant phenotyping methodsトマト花の開花状態と葯の三次元位置・姿勢という植物器官形質を、深層学習・両眼ステレオ・テンプレートマッチングで取得する方法を開発・評価しており、フェノタイピング手法が中心である。
abstractTomato flower pollination features recognition method is designed in this paper based on the deep learning full opened flower recognition model and binocular template matching three-dimensional information recognition method.
MaizeLaboratory / benchtopStereoRootMorphology / geometry measurement2D/3D reconstructionGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Background: The root of a plant is a fundamental organ for the multisensory perception of the environment. Investigating root growth dynamics as a mean of their interaction with the environment is of key importance for improving knowledge in plant behaviour, plant biology and agriculture. To date, it is difficult to study roots movements from a dynamic perspective given that available technologies for root imaging focus mostly on static characterizations, lacking of temporal and three-dimensional (3D) spatial information. This paper describes a new system based on timelapse for the 3D reconstruction and analysis of roots growing in hydroponics. Results The system is based on infrared stereo-cameras acquiring time-lapse images of the roots for 3D reconstruction. The acquisition protocol guarantees the root growth in complete dark while the upper part of the plant grows in normal light conditions. The system extracts the 3D trajectory of the root tip and a set of descriptive features in both the temporal and frequency domains. The system has been used on Zea mays L. (B73) during the first week of growth and shows good inter-reliability between operators with an Intra Class Correlation Coefficient (ICC) > 0.9 for all features extracted. It also showed measurement accuracy with a median difference of < 1 mm between computed and manually measured root length. Conclusions The system and the protocol presented in this study enable accurate 3D analysis of primary root growth in hydroponics. It can serve as a valuable tool for analyzing real-time root responses to environmental stimuli thus improving knowledge on the processes contributing to roots physiological and phenotypic plasticity.
Why it matches plant phenotyping methods根の3D動態を画像から再構成・解析するシステムを開発し、特徴量の信頼性と測定精度を検証しており、植物表現型取得法が中心である。
abstractThis paper describes a new system based on timelapse for the 3D reconstruction and analysis of roots growing in hydroponics.
Reproduction assets foundThe preprint explicitly states that the 3D root tip trajectory data underlying its phenotyping analysis are publicly deposited on Zenodo (record 8422242), making this a paper-specific, publicly accessible phenotype dataset. No author analysis code with a public URL is stated (SPROUTS is proprietary third-party softwareDataset · publicData describing 3D trajectories used in this paper are available here: https://zenodo.org/record/8422242Open asset ↗Zenodo · 8422242lines:119-156Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
Recognition and localization of fruits are key components to achieve automated fruit picking. However, current neural-network-based fruit recognition algorithms have disadvantages such as high complexity. Traditional stereo matching algorithms also have low accuracy. To solve these problems, this study targeting greenhouse tomatoes proposed an algorithm framework based on YOLO-TomatoSeg, a lightweight tomato instance segmentation model improved from YOLOv5n-seg, and an accurate tomato localization approach using RAFT-Stereo disparity estimation and least squares point cloud fitting. First, binocular tomato images were captured using a binocular camera system. The left image was processed by YOLO-TomatoSeg to segment tomato instances and generate masks. Concurrently, RAFT-Stereo estimated image disparity for computing the original depth point cloud. Then, the point cloud was clipped by tomato masks to isolate tomato point clouds, which were further preprocessed. Finally, a least squares sphere fitting method estimated the 3D centroid co-ordinates and radii of tomatoes by fitting the tomato point clouds to spherical models. The experimental results showed that, in the tomato instance segmentation stage, the YOLO-TomatoSeg model replaced the Backbone network of YOLOv5n-seg with the building blocks of ShuffleNetV2 and incorporated an SE attention module, which reduced model complexity while improving model segmentation accuracy. Ultimately, the YOLO-TomatoSeg model achieved an AP of 99.01% with a size of only 2.52 MB, significantly outperforming mainstream instance segmentation models such as Mask R-CNN (98.30% AP) and YOLACT (96.49% AP). The model size was reduced by 68.3% compared to the original YOLOv5n-seg model. In the tomato localization stage, at the range of 280 mm to 480 mm, the average error of the tomato centroid localization was affected by occlusion and sunlight conditions. The maximum average localization error was ±5.0 mm, meeting the localization accuracy requirements of the tomato-picking robots. This study developed a lightweight tomato instance segmentation model and achieved accurate localization of tomato, which can facilitate research, development, and application of fruit-picking robots.
Why it matches plant phenotyping methodsトマト果実の3D位置・サイズを画像分割、ステレオ推定、点群フィッティングで抽出する手法を開発・評価しており、単なる収穫対象の検出を超えて果実形態・位置を定量化する中心的な方法研究である。
abstractFinally, a least squares sphere fitting method estimated the 3D centroid co-ordinates and radii of tomatoes by fitting the tomato point clouds to spherical models.
The trichome trait is one of the important phenotypes for variety classification and breeding improvement of Chinese cabbage (Brassica campestris L. syn. B. rapa). However, obtaining the number of trichomes per unit area on leaves is a time-consuming and laborious detection work, especially when hundreds of germplasm resources need to be evaluated. Therefore, this study constructed the first diverse Chinese cabbage trichome dataset called CCTD with10,955 RGB images and proposed a deep learning model for trichome detection called TRI-YOLOv8. By adding the RepVGG module in the Backbone, adding a new detection layer in the Neck and replacing the loss function with Normalized Gaussian Wasserstein Distance Loss, the detection performance of the model for small trichomes was effectively improved. At the same time, Ghost convolution was used to reduce memory consumption and speed up inference. The experimental results showed that TRI-YOLOv8 outperformed other classical detection models. AP₅₀ was as high as 94.4%, which was 3.8% higher than YOLOv8n. Furthermore, the number of trichomes per unit area was obtained by TRI-YOLOv8 and combined with genome-wide association study and selective sweep analysis, the candidate gene BraA03g029740.3.5C (STP7) was screened out. Overall, this study achieved the accurate detection and counting of trichomes, and provided a feasible plan for breeders to digitally analyze phenotypes, automatically identify and screen Chinese cabbage germplasm resources.
Why it matches plant phenotyping methods中国白菜葉のトライコーム数という植物形質を、RGB画像・三眼ステレオ顕微鏡・深層学習モデルで検出および計数する方法を開発し、データセットと性能評価も提示しており、表現型取得が研究の中心である。
abstractthis study constructed the first diverse Chinese cabbage trichome dataset called CCTD with10,955 RGB images and proposed a deep learning model for trichome detection called TRI-YOLOv8.
Buckwheat plant height is an important indicator for producers. Due to the decline in agricultural labor, the automatic and real-time acquisition of crop growth information will become a prominent issue for farms in the future. To address this problem, we focused on stereo vision and a regression convolutional neural network (CNN) in order to estimate buckwheat plant height. MobileNet V3 Small, NasNet Mobile, RegNet Y002, EfficientNet V2 B0, MobileNet V3 Large, NasNet Large, RegNet Y008, and EfficientNet V2 L were modified into regression CNNs. Through a five-fold cross-validation of the modeling data, the modified RegNet Y008 was selected as the optimal estimation model. Based on the depth and contour information of buckwheat depth image, the mean absolute error (MAE), root mean square error (RMSE), mean square error (MSE), and mean relative error (MRE) when estimating plant height were 0.56 cm, 0.73 cm, 0.54 cm, and 1.7%, respectively. The coefficient of determination (R2) value between the estimated and measured results was 0.9994. Combined with the LabVIEW software development platform, this method can estimate buckwheat accurately, quickly, and automatically. This work contributes to the automatic management of farms.
Why it matches plant phenotyping methodsステレオビジョンと回帰CNNにより、圃場でのソバ草丈を自動推定する手法を開発・検証しており、植物表現型の取得方法が研究の中心です。
abstractwe focused on stereo vision and a regression convolutional neural network (CNN) in order to estimate buckwheat plant height.
Reproduction assets foundThe paper's buckwheat height estimation model code (modified regression CNNs with training results) is publicly available via an authors' GitHub repository explicitly stated in the text. The phenotype dataset (depth images with height labels) is only available by contacting the authors, so it is not a public asset.Code · publicndows 11 (64 bit), and an
Nvidia GeForce RTX 3090 24 GB graphics card with Nvidia Ampere architecture. All of
the models used the processed grayscale images of 224 × 224 pixels as the input and the
estimated buckwheat height as the output. The codes of the models with training results
are available at the following GitHub link: https://github.com/18801389568/Buckwheat-height-estimation (accessed on 26 July 2023).
Figure 5. Construction method of the buckwheat crop height estimation models.
Training the models was essentially a process of continually updating the trainable
parameters of each model in order to make the crop height estimation results increasingly
accurate. Considering the quantOpen asset ↗18801389568/Buckwheat-height-estimationpdf-raw-page:6 lines:1-60Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Normalized canopy area is a measure of spatial canopy fill or canopy coverage in a tree. It is an important factor for growers to consider in making various farming decisions, such as timing and amount of fertilization, pesticide application, and irrigation, as it gives a sense of the overall canopy growth. This study focused on estimating the normalized canopy area of apple trees using a ground-based stereo-vision system in a commercial orchard under natural lighting conditions. A color, depth, and height threshold, followed by a point density-based outlier removal was applied to segment the target tree canopies. The segmented canopy area was compared with the area estimated with manual segmentation, which showed an F1 score of 0.78. The overall growth of the tree canopies was then represented using normalized canopy area, which was calculated as a fraction of pixels occupied by the tree foliage over the total possible area a tree could occupy for a given tree spacing. It was found that the normalized canopy area of individual trees was highly correlated with the surface volume of the canopy (r = 0.7), which is a traditionally used parameter to assess or represent canopy growth. The estimated normalized canopy area was then compared against experts' nitrogen recommendation for the sample trees, which was based on their visual assessment of the vigor/growth of the trees. Four individual experts (one apple farmer, one orchard manager, and two horticulturists with research and outreach experience) were asked to provide their independent nitrogen recommendation and an average recommendation level was calculated. The comparison showed an overall correlation coefficient of - 0.5, indicating an opposite relationship (higher the canopy density, lower the need for nitrogen and vice versa) between the normalized canopy area and the average of the experts' nitrogen recommendation. When the relationship between the normalized area and nitrogen recommendation level was assessed for individual experts, the correlation coefficient was estimated to be -0.86, -0.84, -0.96, and -0.78, respectively, for Expert1, Expert2, Expert3, and Expert4 respectively.. The results from this study identified a critical measure for assessing nitrogen needs at individual tree levels in apple orchards and provides an automated tool to estimate the measure. The outcomes of this study could be used as a part of an integrated decision support system for orchard operations (e.g., fertilization or irrigation) that may include additional canopy characteristics such as spectral signature, trunk diameter, and rate of change of leaf color in the fall.
Why it matches plant phenotyping methodsリンゴ樹の正規化樹冠面積という植物形態・成長形質を、ステレオビジョンと画像分割で自動推定し、手動分割および専門家評価と比較検証しており、フェノタイピング手法が中心である。
abstractThis study focused on estimating the normalized canopy area of apple trees using a ground-based stereo-vision system in a commercial orchard under natural lighting conditions.
Addressing the challenge of the current harvester route detection method’s reduced robustness within lodging-affected farmland environments and its limited perception of crop lodging, this paper proposes a harvesting operation image segmentation method based on SLIC superpixel segmentation and the AdaBoost ensemble learning algorithm. This segmentation enables two essential tasks. Firstly, the RANSAC algorithm is employed to extract the harvester’s operational route through straight-line fitting from the segmented image. Secondly, the method utilizes a 3D point cloud generated by binocular vision, combined with IMU information for attitude correction, to estimate the height of the harvested crop in front of the harvester. Experimental results demonstrate the effectiveness of this method in successfully segmenting the harvested and unharvested areas of the farmland. The average angle error for the detected harvesting route is approximately 1.97°, and the average error for crop height detection in the unharvested area is around 0.054 m. Moreover, the algorithm exhibits a total running time of approximately 437 ms. The innovation of this paper lies in its simultaneous implementation of two distinct perception tasks, leveraging the same image segmentation results. This approach offers a robust and effective solution for addressing both route detection and crop height estimation challenges within lodging-affected farmland during harvesting operations.
Why it matches plant phenotyping methods画像分割・3D点群・IMUを用いて収穫前作物の草高を推定する手法を開発・評価しており、植物形質取得が中心です。
abstractthe method utilizes a 3D point cloud generated by binocular vision, combined with IMU information for attitude correction, to estimate the height of the harvested crop in front of the harvester.
This paper presents an advanced stereo vision-based solution aimed at enhancing the perception capabilities of combine harvesters during operations, with a specific focus on header height detection and rice crop height detection. To improve the accuracy of header height measurement, two novel methods for image-based header recognition are proposed: a barcode marking scheme-based method and an adjacent frame-based method, enabling real-time detection of header height. For crop height detection, the RANSAC plane fitting method is employed to obtain the fitting plane of crop height. Experimental evaluations conducted in the farmlands of Jiading and Fengxian districts in Shanghai demonstrate the performance of the two header and crop height detection methods. The method based on the barcode marking scheme exhibits superior accuracy, achieving a recognition rate of 93.3% with an average error of 0.04m, fulfilling the requirements for precise header height measurement. Conversely, the adjacent frame-based method achieves a recognition rate of 70.2% with an average error of 0.15m. The RANSAC-based crop point cloud height plane fitting method yields an average error of approximately 0.03m, which is adequate for accurate crop height measurement. Furthermore, the algorithms exhibit a speed that allows for timely calculations, completing image processing within 550ms per frame, thereby meeting the real-time requirements of combine harvester operations.
Why it matches plant phenotyping methodsステレオビジョンとRANSACを用いて作物高を直接測定する手法を開発・評価しており、植物形質の取得が中心である。ヘッダー高検出も併記されるが、作物高測定の技術的検証が明確。
abstractFor crop height detection, the RANSAC plane fitting method is employed to obtain the fitting plane of crop height.
We tested whether windthrow damage to Nordic conifer forest stands could be reliably detected as canopy height decrease between a pre-storm LiDAR (Light Detection and Ranging) digital surface model (DSM) and a photogrammetric DSM derived from a post-storm WorldView-3 stereo pair.The post-storm ground reference data consisted of field and unmanned aerial vehicle (UAV) observations of windthrow combined with no-damage areas collected by visual interpretation of the available very high resolution (VHR) satellite imagery.We trained and tested a thresholding model using canopy height change as the sole predictor.We undertook a two-step accuracy assessment by (1) running k-fold crossvalidation on the ground reference dataset and examining the effect of the potential imperfections in the ground reference data, and (2) conducting rigorous accuracy assessment of the classified map of the study area using an extended set of VHR imagery.The thresholding model produced accurate windthrow maps in dense, productive forest stands with a sensitivity of 96%, specificity of 71%, and Matthews correlation coefficient (MCC) over 0.7.However, in sparse and high elevation stands, the classification accuracy was poor.Despite certain collection challenges during the winter months in the Nordic region, we consider VHR stereo satellite imagery to be a viable source of forest canopy height information and sufficiently accurate to map windthrow disturbance in forest stands of high to moderate density.
Why it matches plant phenotyping methods森林キャノピー高の変化を用いた風倒害検出法を開発・検証し、ステレオ衛星画像による植物群落の損傷状態推定が中心である。
abstractWe tested whether windthrow damage to Nordic conifer forest stands could be reliably detected as canopy height decrease between a pre-storm LiDAR (Light Detection and Ranging) digital surface model (DSM) and a photogrammetric DSM derived from a post-storm WorldView-3 stereo pair.
Loblolly pine is one of the most planted forest tree species in the Southern United States for sawtimber production. The sawtimber yield potential of a pine tree is significantly impacted by its stem and branch architecture, which is of important focus in tree improvement programs. However, phenotyping these traits in the upper crown of pine trees is currently based on subjective visual assessments. This study investigated the feasibility of quantifying stem diameter, branch angle, and branch diameter of six-year-old loblolly pine trees in a progeny test using stereo 3D imaging, deep learning-based instance segmentation, and image and point cloud processing techniques. Instance segmentation of branches and stems was performed as well as principal component analysis (PCA) in 2D images, followed by 3D reconstruction of the segmented organs. The resulting 3D point clouds were further processed using random sample consensus (RANSAC) and statistical outlier removal to extract stem diameter, branch angle, and branch diameter. When compared to the manual ground-truth measurements, the three system-derived parameters achieved RMSEs of 0.055 m, 5.0˚, and 5.6 mm, respectively. In addition, Bland-Altman analyses showed that the stem diameter and branch angle estimations were found with limits of agreement of±0.098 m and±9.8˚, respectively, with nonsignificant biases. On the other hand, branch diameter estimation showed −12.1 mm and 9.3 mm for lower and upper limits of agreement with a bias. The proposed system demonstrates promising potential as a high-throughput low-cost precision phenotyping tool for the characterization of loblolly pine tree architecture under field conditions, facilitating the selection of superior genotypes with improved sawtimber properties.
Why it matches plant phenotyping methodsステレオ3D画像、深層学習、点群処理を組み合わせ、樹木の建築形質を抽出・検証する手法が研究の中心である。
abstractThis study investigated the feasibility of quantifying stem diameter, branch angle, and branch diameter of six-year-old loblolly pine trees in a progeny test using stereo 3D imaging, deep learning-based instance segmentation, and image and point cloud processing techniques.
Crop yield potential is intrinsically related to canopy photosynthesis; therefore, improving canopy photosynthetic efficiency is a major focus of current efforts to enhance crop yield. Canopy photosynthesis rate ( A c ) is influenced by several factors, including plant architecture, leaf chlorophyll content, and leaf photosynthetic properties, which interact with each other. Identifying factors that restrict canopy photosynthesis and target adjustments to improve canopy photosynthesis in a specific crop cultivar pose an important challenge for the breeding community. To address this challenge, we developed a novel pipeline that utilizes factorial analysis, canopy photosynthesis modeling, and phenomics data collected using a 64-camera multi-view stereo system, enabling the dissection of the contributions of different factors to differences in canopy photosynthesis between maize cultivars. We applied this method to 2 maize varieties, W64A and A619, and found that leaf photosynthetic efficiency is the primary determinant (17.5% to 29.2%) of the difference in A c between 2 maize varieties at all stages, and plant architecture at early stages also contribute to the difference in A c (5.3% to 6.7%). Additionally, the contributions of each leaf photosynthetic parameter and plant architectural trait were dissected. We also found that the leaf photosynthetic parameters were linearly correlated with A c and plant architecture traits were non-linearly related to A c . This study developed a novel pipeline that provides a method for dissecting the relationship among individual phenotypes controlling the complex trait of canopy photosynthesis.
Why it matches plant phenotyping methods64台カメラのマルチビュー・ステレオ計測によるフェノミクスデータと、キャノピー光合成モデル・因子分析を統合した新規パイプラインの開発が中心であり、植物形態形質とキャノピー光合成の関係を定量化している。
titleDevelopment of a Novel 3D Canopy Modeling Pipeline
Reproduction assets foundThe paper's Data Availability section explicitly deposits the authors' 3D canopy modeling pipeline source code and the FastTracer ray tracing software used for the canopy photosynthesis simulations, both on public GitHub repositories. No phenotype/image datasets are explicitly deposited.Code · publicThe source code used in this study is available for non-commercial use and the code can be downloaded from https://github.com/PlantSystemsBiology/3DCanopyModelOpen asset ↗PlantSystemsBiology/3DCanopyModellines:224-402Code · publicThe FastTracer software is available from https://github.com/PlantSystemsBiology/fastTracerPublicOpen asset ↗PlantSystemsBiology/fastTracerPubliclines:224-402Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 14 Sept 2026
Taking maize seedlings as the object, the implementation of crops 3D reconstruction based on RGB-D binocular vision and the selection of some key parameters are investigated in this research. First, multiple images are taken from different angles around the target. By mapping the maize seedling region coordinate values after the Otsu algorithm and global threshold segmentation to the corresponding depth image, the depth data of the maize seedling region can be obtained accurately. An improved mean filter is proposed to adaptively fill the holes in the depth image. Then, the different point clouds with the fixed step angle of the maize seedling are registered and fuzed. Finally, after the fusion point cloud is simplified, the 3D model of crops can be reconstructed. Experimental results show that the simplification effect of the octree algorithm is better than that of the voxel grid filter. Among all the step angles, the reconstruction error of the step angle with 60° is the smallest. Under this condition, the height error between the model and the maize seedling is 2.22%, and the error in stem diameter is 11.67%.
Why it matches plant phenotyping methodsRGB-D双目视觉三维重建方法是论文核心，并对玉米幼苗高度和茎径等表型测量误差进行了验证。
abstractthe implementation of crops 3D reconstruction based on RGB-D binocular vision and the selection of some key parameters are investigated
Phenotyping plays a significant role in the breeding of apple tree. However, existing researches mainly relied on instruments, such as LiDAR, RGB-D camera or UAV (unmanned aerial vehicle) embedded with depth sensor, etc., which requires additional costs for users and also inconvenient. Therefore, a novel method of smartphone-based heterogeneous binocular vision was developed to fulfill low-cost automated phenotyping for apple tree. In this study, a pair of cameras on multi-camera smartphone was selected to obtain heterogeneous binocular camera. After that, a so-called virtual focal method was developed to generate standard binocular images from heterogeneous binocular images of smartphone. A well-known YOLOv5s object detection model was trained on a four-class dataset to detect fruits, grafts, trunks and whole trees. Then, the model was simplified to fit the deployment on smartphone. Finally, five phenotypes (trunk diameter, ground diameter, tree height, fruit vertical diameter, and fruit horizontal diameter) of individual apple tree were obtained by pinhole camera model and standard binocular vision. After evaluation of phenotyping manually and by smartphone, our method shows MAPE (mean average percentage error) ranging from 6.00 % to 13.73 % for the five phenotypes. Compared with the existing studies, our method has reached a close or even better phenotyping accuracy with only a smartphone. As more and more smartphones have multi-camera, our method is probably the lowest cost phenotyping method for most of the potential users. Results indicated that the approach could be utilized to phenotyping of apple tree.
Why it matches plant phenotyping methodsスマートフォンのステレオ画像とYOLOv5sを用いて、リンゴ樹の複数形質を自動推定する手法を開発・評価しており、表現型取得が研究の中心である。
abstracta novel method of smartphone-based heterogeneous binocular vision was developed to fulfill low-cost automated phenotyping for apple tree.
Future crop varieties must be higher yielding, stress resilient and climate agile to feed a larger population, and overcome the effects of climate change. This will only be achieved by a fusion of plant breeding with multiple “omic” sciences. Field-based, proximal phenomics assesses plant growth and responses to stress and agronomic treatments, in a given environment, over time and requires instruments capable of capturing data, quickly and reliably. We designed the PlotCam following the concepts of cost effective phenomics, being low-cost, light-weight (6.8 kg in total) and portable with rapid and repeatable data collection at high spatial resolution. The platform consisted of a telescoping, square carbon fiber unipod, which allowed for data collection from many heights. A folding arm held the sensor head at the nadir position over the plot, and an accelerometer in the arm ensured the sensor head was level at the time of data acquisition. A computer mounted on the unipod ran custom software for data collection. RGB images were taken with an 18 MP, WiFi controlled camera, infrared thermography data was captured with a 0.3 MP infrared camera, and canopy height measured with a 0.3 MP stereo depth camera. Incoming light and air temperature were logged with every image. New operators were quickly trained to gather reliable and repeatable data and an experienced operator could image up to 300 plots per hour. The PlotCam platform was not limited by field design or topography. Multiple identical PlotCams permitted the study of larger populations generating phenomic information useful in variety improvement. We present examples of data collected with the PlotCam over field soybean experiments to show the effectiveness of the platform.
Why it matches plant phenotyping methods作物の形質取得を目的とした携帯型近接フェノミクス基盤を設計・提示しており、複数センサー、データ収集、再現性、処理速度を含むプラットフォーム自体が研究の中心です。
abstractWe designed the PlotCam following the concepts of cost effective phenomics, being low-cost, light-weight (6.8 kg in total) and portable with rapid and repeatable data collection at high spatial resolution.
Smart farming (SF) applications rely on robust and accurate computer vision systems. An important computer vision task in agriculture is semantic segmentation, which aims to classify each pixel of an image and can be used for selective weed removal. State-of-the-art implementations use convolutional neural networks (CNN) that are trained on large image datasets. In agriculture, publicly available RGB image datasets are scarce and often lack detailed ground-truth information. In contrast to agriculture, other research areas feature RGB-D datasets that combine color (RGB) with additional distance (D) information. Such results show that including distance as an additional modality can improve model performance further. Therefore, we introduce WE3DS as the first RGB-D image dataset for multi-class plant species semantic segmentation in crop farming. It contains 2568 RGB-D images (color image and distance map) and corresponding hand-annotated ground-truth masks. Images were taken under natural light conditions using an RGB-D sensor consisting of two RGB cameras in a stereo setup. Further, we provide a benchmark for RGB-D semantic segmentation on the WE3DS dataset and compare it with a solely RGB-based model. Our trained models achieve up to 70.7% mean Intersection over Union (mIoU) for discriminating between soil, seven crop species, and ten weed species. Finally, our work confirms the finding that additional distance information improves segmentation quality.
Why it matches plant phenotyping methods植物種の画素単位セグメンテーション用RGB-Dデータセットとベンチマークを構築し、植物識別・分離という表現型取得ワークフローを中心的に評価しているため。
abstractwe introduce WE3DS as the first RGB-D image dataset for multi-class plant species semantic segmentation in crop farming.
Reproduction assets foundThe paper's WE3DS RGB-D image dataset (2568 annotated images) and the authors' modified ESANet analysis code are publicly deposited on Zenodo (DOI 10.5281/zenodo.7457983), as stated in the experiments section. The MDPI supplementary file contains only tables (species list, depth accuracy, confusion matrices), not the影像Dataset · public024 × 512
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1024 × 512
52.4
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1024 × 512
59.1
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Information on the dataset and modified code of the ESANet can be found on our website https://doi.org/10.5281/zenodo.7457983 (accessed on 18 December 2022).
4.4. ResultsOpen asset ↗Zenodo · 10.5281/zenodo.7457983lines:69-146Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
Abstract Maize (Zea mays L.) is one of the three major cereal crops in the world. Leaf angle is an important architectural trait of crops due to its substantial role in light interception by the canopy and hence photosynthetic efficiency. Traditionally, leaf angle has been measured using a protractor, a process that is both slow and laborious. Efficiently measuring leaf angle under field conditions via imaging is challenging due to leaf density in the canopy and the resulting occlusions. However, advances in imaging technologies and machine learning have provided new tools for image acquisition and analysis that could be used to characterize leaf angle using three‐dimensional (3D) models of field‐grown plants. In this study, PhenoBot 3.0, a robotic vehicle designed to traverse between pairs of agronomically spaced rows of crops, was equipped with multiple tiers of PhenoStereo cameras to capture side‐view images of maize plants in the field. PhenoStereo is a customized stereo camera module with integrated strobe lighting for high‐speed stereoscopic image acquisition under variable outdoor lighting conditions. An automated image processing pipeline (AngleNet) was developed to measure leaf angles of nonoccluded leaves. In this pipeline, a novel representation form of leaf angle as a triplet of keypoints was proposed. The pipeline employs convolutional neural networks to detect each leaf angle in two‐dimensional images and 3D modeling approaches to extract quantitative data from reconstructed models. Satisfactory accuracies in terms of correlation coefficient (r) and mean absolute error (MAE) were achieved for leaf angle () and internode heights (). Our study demonstrates the feasibility of using stereo vision to investigate the distribution of leaf angles in maize under field conditions. The proposed system is an efficient alternative to traditional leaf angle phenotyping and thus could accelerate breeding for improved plant architecture.
Why it matches plant phenotyping methodsステレオビジョン、ロボット搭載カメラ、深層学習、3D再構成を用いてトウモロコシの葉角度と節間高を抽出する手法を開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractAn automated image processing pipeline (AngleNet) was developed to measure leaf angles of nonoccluded leaves.
Apple (Malus domestica) fruit size plays an integral role in orchard management decision-making, particularly during chemical thinning, fruit quality assessment, and yield prediction. A machine vision system was developed using stereo cameras synchronised to a custom-built LED strobe to perform on-tree sizing of fruit in images with high measurement accuracy. Two deep neural network models (Faster R–CNN and Mask R–CNN) were trained to detect fruit candidates for sizing followed by extrapolation of occluded fruit regions to improve size estimation. The segmented fruit shapes were converted to metric surface areas and diameters using spatial resolutions and depth information from the stereo cameras. Monthly field trials from June to October using the camera system were conducted, measuring fruit diameters ranging from 22 to 82 mm, and compared against ground truth diameters. Diameter estimates had a mean absolute error ranging from 1.1 to 4.2 mm for the five-month trial period, an average error of 4.8% compared to ground truth diameter measurements. Standard deviation errors ranged from 0.7 to 1.9 mm. Using neural network models for intelligent sampling of fruit in images followed by extrapolation of missing regions can be an alternative method of handling fruit occlusion in agricultural imaging and improving sizing accuracy.
Why it matches plant phenotyping methodsステレオビジョンと深層学習によって樹上果実のサイズを推定する画像ベース表現型計測法を開発し、実測値との精度比較で検証しているため、方法が研究の中心である。
abstractA machine vision system was developed using stereo cameras synchronised to a custom-built LED strobe to perform on-tree sizing of fruit in images with high measurement accuracy.
Stereo matching is a depth perception method for plant phenotyping with high throughput. In recent years, the accuracy and real-time performance of the stereo matching models have been greatly improved. While the training process relies on specialized large-scale datasets, in this research, we aim to address the issue in building stereo matching datasets. A semi-automatic method was proposed to acquire the ground truth, including camera calibration, image registration, and disparity image generation. On the basis of this method, spinach, tomato, pepper, and pumpkin were considered for experiment, and a dataset named PlantStereo was built for reconstruction. Taking data size, disparity accuracy, disparity density, and data type into consideration, PlantStereo outperforms other representative stereo matching datasets. Experimental results showed that, compared with the disparity accuracy at pixel level, the disparity accuracy at sub-pixel level can remarkably improve the matching accuracy. More specifically, for PSMNet, the EPE and bad−3 error decreased 0.30 pixels and 2.13%, respectively. For GwcNet, the EPE and bad−3 error decreased 0.08 pixels and 0.42%, respectively. In addition, the proposed workflow based on stereo matching can achieve competitive results compared with other depth perception methods, such as Time-of-Flight (ToF) and structured light, when considering depth error (2.5 mm at 0.7 m), real-time performance (50 fps at 1046 × 606), and cost. The proposed method can be adopted to build stereo matching datasets, and the workflow can be used for depth perception in plant phenotyping.
Why it matches plant phenotyping methods植物フェノタイピング用のステレオマッチングデータセット構築法を開発し、精度・性能を検証した研究であり、表現型取得手法が中心である。
abstractStereo matching is a depth perception method for plant phenotyping with high throughput.
As the largest component of crops, water has an important impact on the growth and development of crops. Timely, rapid, continuous, and non-destructive detection of crop water stress status is crucial for crop water-saving irrigation, production, and breeding. Indices based on leaf or canopy temperature acquired by thermal imaging are widely used for crop water stress diagnosis. However, most studies fail to achieve high-throughput, continuous water stress detection and mostly focus on two-dimension measurements. This study developed a low-cost three-dimension (3D) motion robotic system, which is equipped with a designed 3D imaging system to automatically collect potato plant data, including thermal and binocular RGB data. A method is developed to obtain 3D plant fusion point cloud with depth, temperature, and RGB color information using the acquired thermal and binocular RGB data. Firstly, the developed system is used to automatically collect the data of the potato plants in the scene. Secondly, the collected data was processed, and the green canopy was extracted from the color image, which is convenient for the speeded-up robust features algorithm to detect more effective matching features. Photogrammetry combined with structural similarity index was applied to calculate the optimal homography transform matrix between thermal and color images and used for image registration. Thirdly, based on the registration of the two images, 3D reconstruction was carried out using binocular stereo vision technology to generate the original 3D point cloud with temperature information. The original 3D point cloud data were further processed through canopy extraction, denoising, and k-means based temperature clustering steps to optimize the data. Finally, the crop water stress index (CWSI) of each point and average CWSI in the canopy were calculated, and its daily variation and influencing factors were analyzed in combination with environmental parameters. The developed system and the proposed method can effectively detect the water stress status of potato plants in 3D, which can provide support for analyzing the differences in the three-dimensional distribution and spatial and temporal variation patterns of CWSI in potato.
Why it matches plant phenotyping methods熱画像と双眼ステレオビジョンを統合した3D植物表現型取得システムと、温度付き点群からCWSIを算出する手法が研究の中心であるため。
abstractThis study developed a low-cost three-dimension (3D) motion robotic system, which is equipped with a designed 3D imaging system to automatically collect potato plant data, including thermal and binocular RGB data.
Highlights A real time stereo vision controlled variable rate sprayer for specialty crops was developed. The stereo vision system of the sprayer detected outdoor trees with similar canopy profiles under travel speeds ranging from 3.2 to 8 km h-1. Canopy volume measurements of the sprayer were impacted by lateral distances between the sprayer and the tree center and travel speeds. The sprayer required less than 200 ms from tree canopy detection to spray decisions. The sprayer achieved spray volume reductions from 72.6% to 80.5% compared to constant rate spray application. Abstract. A real time variable rate sprayer controlled by a stereo vision system was developed to increase the accuracy of spray applications and reduce the use of crop protection products. The sprayer was designed to detect tree canopies and calculate its volume using depth images from the stereo vision system, and discharge corresponding spray volumes every 200 ms through the embedded software in the graphical user interface. The sprayer was evaluated in an apple orchard at different travel speeds (3.2 to 8.0 km h-1) for its performance in detecting canopy and measuring its volume. In addition, spray volume, deposition, and coverage of the variable rate application of the sprayer were evaluated against a constant rate application. Test results showed that the sprayer detected visually similar tree canopies during the evaluations, although its canopy volume measurements deviated from manually measured canopy volume from 0.11 to 0.83 m3 due to lateral position changes of the sprayer. The sprayer adjusted duty cycles of pulse width modulated valves to accurately spray the intended volume for detected canopies (0.073 to 0.083 L m-3) and only used spray volumes of 19.5% to 26.7% compared to a constant rate spray application (338 L ha-1). The constant rate spray application generally had more spray deposition and coverage in tree canopies than the variable rate sprayer, as expected since its spray volume was approximately 3.7 times higher. However, the mean spray depositions from the constant rate spray application were significantly varied (p=0.05) by tree sizes, while the variable rate spray application achieved statistically equivalent mean spray depositions regardless of tree sizes. The stereo vision controlled sprayer offers a cost-effective real-time variable rate spray option for growers with the potential to perform other tasks by using image processing algorithms while applying crop protection products. Keywords: Automation, Canopy volume, Crop protection, Depth image, Orchard, Precision agriculture, Real-time application.
Why it matches plant phenotyping methodsステレオビジョンで樹冠を検出・体積推定する方法を開発し、走行速度や位置変化に対する性能を評価しているため、植物形質取得が中心的である。
abstractA real time variable rate sprayer controlled by a stereo vision system was developed
Highlights Stereo vision controlled variable rate sprayer reduced the spray volume by 44% to 99.6% for the tree canopy volumes from 2.2 to 0.03 m3. The variable rate sprayer increased the ratio of a spray deposit on trees to a spray volume by 16.1% compared to a constant rate sprayer. The application of the variable rate sprayer had 57.6% less ground loss compared to a constant rate application. The travel speeds of the sprayer had no significant impacts in spray deposition, coverage, or ground losses of the applications. Abstract. A prototype of a novel stereo vision controlled real-time variable rate sprayer was evaluated in an apple orchard in order to mitigate the risks derived from the use of crop protection products applied at a conventional constant rate in specialty crops. The effects of its travel speed (from 3.2 to 8.0 km h-1) on spray volume reduction, canopy deposition and coverage, and ground loss were assessed. Test results demonstrated that the travel speed did not influence spray deposition, coverage, or ground losses. The variable rate sprayer reduced the spray volume by more than 44% for the canopy volumes detected in the orchard (<2.2 m3) in comparison to the constant rate spray application. Overall spray volume reductions were 79.1%, 73.8%, 71.0%, and 69.8% when the sprayer traveled at 3.2, 4.8, 6.4, and 8.0 km h-1, respectively, compared to the constant rate applications at a travel speed of 8.0 km h-1. In addition, the variable rate application of the sprayer increased the crop interception, a ratio of a spray deposition to a spray volume, by 16.1% compared to the constant rate spray application when traveling at 8 km h-1. Moreover, the sprayer reduced total ground loss by 57.6% compared to the constant rate application, which would minimize adverse impacts of pesticide applications to non-target organisms and unnecessary pesticide losses to the environment. The results of this study suggest that the prototype sprayer has significant potential as a cost-effective solution for sustainable specialty crop spray applications. Keywords: Crop protection, Depth image, Machine vision, Orchards, Precision agriculture, Pulse width modulation.
Why it matches plant phenotyping methodsステレオビジョンで樹冠体積を推定し、その測定に基づく可変散布機を評価しており、植物形態の取得と技術性能評価が研究の中心である。
abstractA prototype of a novel stereo vision controlled real-time variable rate sprayer was evaluated in an apple orchard
In intensively managed forests in Europe, where forests are divided into stands of small size and may show heterogeneity within stands, a high spatial resolution (10 - 20 meters) is arguably needed to capture the differences in canopy height. In this work, we developed a deep learning model based on multi-stream remote sensing measurements to create a high-resolution canopy height map over the "Landes de Gascogne" forest in France, a large maritime pine plantation of 13,000 km$^2$ with flat terrain and intensive management. This area is characterized by even-aged and mono-specific stands, of a typical length of a few hundred meters, harvested every 35 to 50 years. Our deep learning U-Net model uses multi-band images from Sentinel-1 and Sentinel-2 with composite time averages as input to predict tree height derived from GEDI waveforms. The evaluation is performed with external validation data from forest inventory plots and a stereo 3D reconstruction model based on Skysat imagery available at specific locations. We trained seven different U-net models based on a combination of Sentinel-1 and Sentinel-2 bands to evaluate the importance of each instrument in the dominant height retrieval. The model outputs allow us to generate a 10 m resolution canopy height map of the whole "Landes de Gascogne" forest area for 2020 with a mean absolute error of 2.02 m on the Test dataset. The best predictions were obtained using all available satellite layers from Sentinel-1 and Sentinel-2 but using only one satellite source also provided good predictions. For all validation datasets in coniferous forests, our model showed better metrics than previous canopy height models available in the same region.
Why it matches plant phenotyping methodsSentinel/GEDI等のリモートセンシング画像から樹冠高を推定する深層学習手法を開発し、外部データで検証しているため、植物形質取得法が中心である。
abstractwe developed a deep learning model based on multi-stream remote sensing measurements to create a high-resolution canopy height map
Reproduction assets foundThe paper's primary phenotyping-relevant input is the GEDI L2A canopy height dataset (526,449 footprints over the Landes forest, 2020), explicitly downloaded from NASA's EarthDataSearch. This is a public, paper-specific sensor dataset directly used for the study's canopy height measurements and model training. No code,Dataset · publicwater bodies (Beck et al., 2020). Indeed, these
surfaces mirror the transmitted waveforms that have a pulse width of ~ 15 ns which
corresponds to a ~ 2.25 m wide waveform (Dubayah et al., 2020).
In total, 526,449 footprints from the GEDIv002 L2A product (Dubayah et al., 2021) were
downloaded from NASA’s EarthDataSearch website
(https://search.earthdata.nasa.gov/search) for this study, covering the entire area of interest
for 2020. Due to atmospheric perturbations, some waveforms could not be used to give
information on the vertical forest structure. Therefore, several filtering criteria were applied to
remove unusable waveforms: (1) When the quality_flag provided in the GEDI data was set toOpen asset ↗GEDIv002 L2Apdf-raw-page:6 lines:1-45Plant phenotyping relevance match · UnverifiedEurope PMC · checked 8 Sept 2026
Plant disease evaluation is crucial to pathogen management and plant breeding. Human field scouting has been widely used to monitor disease progress and provide qualitative and quantitative evaluation, which is costly, laborious, subjective, and often imprecise. To improve disease evaluation accuracy, throughput, and objectiveness, an image-based approach with a deep learning-based analysis pipeline was developed to calculate infection severity of grape foliar diseases. The image-based approach used a ground imaging system for field data acquisition, consisting of a custom stereo camera with strobe light for consistent illumination and real time kinematic (RTK) GPS for accurate localization. The deep learning-based pipeline used the hierarchical multiscale attention semantic segmentation (HMASS) model for disease infection segmentation, color filtering for grapevine canopy segmentation, and depth and location information for effective region masking. The resultant infection, canopy, and effective region masks were used to calculate the severity rate of disease infections in an image sequence collected in a given unit (e.g., grapevine panel). Fungicide trials for grape downy mildew (DM) and powdery mildew (PM) were used as case studies to evaluate the developed approach and pipeline. Experimental results showed that the HMASS model achieved acceptable to good segmentation accuracy of DM (mIoU > 0.84) and PM (mIoU > 0.74) infections in testing images, demonstrating the model capability for symptomatic disease segmentation. With the consistent image quality and multimodal metadata provided by the imaging system, the color filter and overlapping region removal could accurately and reliably segment grapevine canopies and identify repeatedly imaged regions between consecutive image frames, leading to critical information for infection severity calculation. Image-derived severity rates were highly correlated (r > 0.95) with human-assessed values, and had comparable statistical power in differentiating fungicide treatment efficacy in both case studies. Therefore, the developed approach and pipeline can be used as an effective and efficient tool to quantify the severity of foliar disease infections, enabling objective, high-throughput disease evaluation for fungicide trial evaluation, genetic mapping, and breeding programs.
Why it matches plant phenotyping methods画像取得と深層学習による葉面病害の感染重症度推定手法を開発・検証しており、植物病態という観測可能な形質の抽出が研究の中心である。
abstractan image-based approach with a deep learning-based analysis pipeline was developed to calculate infection severity of grape foliar diseases.
To guide grape picking robots to recognize and classify the grapes with different maturity quickly and accurately in the complex environment of the orchard, and to obtain the spatial position information of the grape clusters, an algorithm of grape maturity detection and visual pre-positioning based on improved YOLOv4 is proposed in this study. The detection algorithm uses Mobilenetv3 as the backbone feature extraction network, uses deep separable convolution instead of ordinary convolution, and uses the h-swish function instead of the swish function to reduce the number of model parameters and improve the detection speed of the model. At the same time, the SENet attention mechanism is added to the model to improve the detection accuracy, and finally the SM-YOLOv4 algorithm based on improved YOLOv4 is constructed. The experimental results of maturity detection showed that the overall average accuracy of the trained SM-YOLOv4 target detection algorithm under the verification set reached 93.52%, and the average detection time was 10.82 ms. Obtaining the spatial position of grape clusters is a grape cluster pre-positioning method based on binocular stereo vision. In the pre-positioning experiment, the maximum error was 32 mm, the mean error was 27 mm, and the mean error ratio was 3.89%. Compared with YOLOv5, YOLOv4-Tiny, Faster_R-CNN, and other target detection algorithms, which have greater advantages in accuracy and speed, have good robustness and real-time performance in the actual orchard complex environment, and can simultaneously meet the requirements of grape fruit maturity recognition accuracy and detection speed, as well as the visual pre-positioning requirements of grape picking robots in the orchard complex environment. It can reliably indicate the growth stage of grapes, so as to complete the picking of grapes at the best time, and it can guide the robot to move to the picking position, which is a prerequisite for the precise picking of grapes in the complex environment of the orchard.
Why it matches plant phenotyping methodsブドウ果実の成熟度という植物状態を画像から推定するYOLO法を開発・検証しており、成熟度検出の精度・速度評価が研究の中心である。位置推定も含むが、成熟度フェノタイピング手法が明確に含まれる。
abstractan algorithm of grape maturity detection and visual pre-positioning based on improved YOLOv4 is proposed in this study.
Recent deep learning methods have allowed important steps forward in the automatic detection of wheat ears in the field. Nevertheless, it was still lacking a method able to both count and segment the ears, validated at all the development stages from heading to maturity. Moreover, the critical step of converting the ear count in an image to an ear density, i.e. a number of ears per square metre in the field, has been widely ignored by most of the previous studies. For this research, wheat RGB images have been acquired from heading to maturity in two field trials displaying contrasted fertilisation scenarios. An unsupervised learning approach on the YOLOv5 model, as well as the cutting-edge DeepMAC segmentation method were exploited to develop a wheat ear counting and segmentation pipeline that necessitated only a limited amount of labelling work for the training. An additional label set including all the development stages was built for validation. The average F1 score of ear bounding box detection was 0.93 and the average F1 score of segmentation was 0.86. To convert the ear counts to ear densities, a second RGB camera was used so that the distance between the cameras and the ears could be measured by stereovision. That distance was exploited to compute the image footprint at ear level, and thus divide the number of ears by this footprint to get the ear density. The obtained ear densities were coherent regarding the fertilisation scenarios but, for a same fertilisation, differences were observed between acquisition dates. This highlights that the measurement was not able to retrieve absolute ear densities for all the development stages and conditions. The deep learning measurement considered the most reliable outperformed observations from three human operators.
Why it matches plant phenotyping methods小麦穂の検出・セグメンテーション・計数から穂密度を推定する画像ベースの表現型計測パイプラインを開発し、複数生育段階で性能検証しているため。
abstractTo convert the ear counts to ear densities, a second RGB camera was used so that the distance between the cameras and the ears could be measured by stereovision.
Intelligent detection and localization of mature citrus fruits is a critical challenge in developing an automatic harvesting robot. Variable illumination conditions and different occlusion states are some of the essential issues that must be addressed for the accurate detection and localization of citrus in the orchard environment. In this paper, a novel method for the detection and localization of mature citrus using improved You Only Look Once (YOLO) v5s with binocular vision is proposed. First, a new loss function (polarity binary cross-entropy with logit loss) for YOLO v5s is designed to calculate the loss value of class probability and objectness score, so that a large penalty for false and missing detection is applied during the training process. Second, to recover the missing depth information caused by randomly overlapping background participants, Cr-Cb chromatic mapping, the Otsu thresholding algorithm, and morphological processing are successively used to extract the complete shape of the citrus, and the kriging method is applied to obtain the best linear unbiased estimator for the missing depth value. Finally, the citrus spatial position and posture information are obtained according to the camera imaging model and the geometric features of the citrus. The experimental results show that the recall rates of citrus detection under non-uniform illumination conditions, weak illumination, and well illumination are 99.55%, 98.47%, and 98.48%, respectively, approximately 2-9% higher than those of the original YOLO v5s network. The average error of the distance between the citrus fruit and the camera is 3.98 mm, and the average errors of the citrus diameters in the 3D direction are less than 2.75 mm. The average detection time per frame is 78.96 ms. The results indicate that our method can detect and localize citrus fruits in the complex environment of orchards with high accuracy and speed. Our dataset and codes are available at https://github.com/AshesBen/citrus-detection-localization.
Why it matches plant phenotyping methods収穫ロボット向けの位置検出が主目的だが、果実形状・姿勢・3D直径を画像から抽出し、精度を検証する技術開発が中心であり、再利用可能な植物器官形質の推定に該当する。
abstracta novel method for the detection and localization of mature citrus using improved You Only Look Once (YOLO) v5s with binocular vision is proposed.
Reproduction assets foundThe authors explicitly state that their citrus image dataset (4855 binocular image groups with depth maps) and analysis code are publicly available on GitHub, matching the allowed URL.Dataset · publicnt occlusion conditions in natural orchards. Future work will focus on few-shot learning and reduce the number of citrus fruits in the training dataset to improve citrus detection and localization.
Data availability statement
The original contributions presented in this study are publicly available. This data can be found here: https://github.com/AshesBen/citrus-detection-localization .
Author contributions
All authors contributed to the method and result of the study, dataset generation, model training and testing, analysis of results, and the drafting, revising, and approving of the contents of the manuscript.
Funding
We acknowledged support from the Natural Science Foundation of GuangdongOpen asset ↗AshesBen/citrus-detection-localizationlines:335-356Code · public98 mm, and the average errors of the citrus diameters in the 3D direction are less than 2.75 mm. The average detection time per frame is 78.96 ms. The results indicate that our method can detect and localize citrus fruits in the complex environment of orchards with high accuracy and speed. Our dataset and codes are available at https://github.com/AshesBen/citrus-detection-localization .
Keywords: citrus detection, citrus localization, binocular vision, YOLO v5s, loss function
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2022 Jun 18; Accepted 2022 Jul 12; Collection date 2022.
IntroductionOpen asset ↗AshesBen/citrus-detection-localizationlines:1-28Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 15 Sept 2026
A 4D dual-mode staring hyperspectral-depth imager (DSHI), which acquire reflectance spectra, fluorescence spectra, and 3D structural information by combining a staring hyperspectral scanner and a binocular line laser stereo vision system, is introduced. A 405 nm laser line generated by a focal laser line generation module is used for both fluorescence excitation and binocular stereo matching of the irradiated line region. Under the configuration, the two kinds of hyperspectral data collected by the hyperspectral scanner can be merged into the corresponding points in the 3D model, forming a dual-mode 4D model. The DSHI shows excellent performance with spectral resolution of 3 nm, depth accuracy of 26.2 µm. Sample experiments on a fluorescent figurine, real and plastic sunflowers and a clam are presented to demonstrate system's with potential within a broad range of applications such as, e.g., digital documentation, plant phenotyping, and biological analysis.
Why it matches plant phenotyping methods植物のスペクトル・蛍光・3D構造を統合取得するイメージング装置を開発し、植物フェノタイピングへの応用を実証しているため、方法が中心的である。
abstractA 4D dual-mode staring hyperspectral-depth imager (DSHI), which acquire reflectance spectra, fluorescence spectra, and 3D structural information
This work focuses on the problem of non-contact measurement for vegetables in agricultural automation. The application of computer vision in assisted agricultural production significantly improves work efficiency due to the rapid development of information technology and artificial intelligence. Based on object detection and stereo cameras, this paper proposes an intelligent method for vegetable recognition and size estimation. The method obtains colorful images and depth maps with a binocular stereo camera. Then detection networks classify four kinds of common vegetables (cucumber, eggplant, tomato and pepper) and locate six points for each object. Finally, the size of vegetables is calculated using the pixel position and depth of keypoints. Experimental results show that the proposed method can classify four kinds of common vegetables within 60 cm and accurately estimate their diameter and length. The work provides an innovative idea for solving the vegetable's non-contact measurement problems and can promote the application of computer vision in agricultural automation.
Why it matches plant phenotyping methods野菜の長さ・直径という植物器官形質を、ステレオカメラ、深度画像、キーポイント検出で非接触推定する手法が研究の中心であるため。
abstractThis work focuses on the problem of non-contact measurement for vegetables in agricultural automation.
Reproduction assets foundThe authors publicly released both the vegetable keypoint dataset (1600 COCO-format images with ROI boxes and six keypoints) and the implementation code for their size estimation method on GitHub. Labelme is a generic third-party annotation tool and is excluded.Code · publicThe implementation code of our size estimation method can be accessed on https://github.com/BourneZ130/VegetableDetection , accessed on 15 February 2022.Open asset ↗BourneZ130/VegetableDetectionlines:76-141Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Plants modify the climate and provide natural cooling through transpiration. However, plant response is not only dependent on the atmospheric evaporative demand due to the combined effects of wind speed, air temperature, humidity, and solar radiation, but is also dependent on the water transport within the plant leaf-xylem-root system. These interactions result in a dynamic response of the plant where transpiration hysteresis can influence the cooling provided by the plant. Therefore, a detailed understanding of such dynamics is key to the development of appropriate mitigation strategies and numerical models. In this study, we unveil the diurnal dynamics of the microclimate of a Buxus sempervirens plant using multiple high-resolution non-intrusive imaging techniques. The wake flow field is measured using stereoscopic particle image velocimetry, the spatiotemporal leaf temperature history is obtained using infrared thermography, and additionally, the plant porosity is obtained using X-ray tomography. We find that the wake velocity statistics are not directly linked with the distribution of the porosity but depends mainly on the geometry of the plant foliage which generates the shear flow. The interaction between the shear regions and the upstream boundary layer profile is seen to have a dominant effect on the wake turbulent kinetic energy distribution. Furthermore, the leaf area density distribution has a direct impact on the short-wave radiative heat flux absorption inside the foliage where 50% of the radiation is absorbed in the top 20% of the foliage. This localized radiation absorption results in a high local leaf and air temperature. Furthermore, a comparison of the diurnal variation of leaf temperature and the net plant transpiration rate enabled us to quantify the diurnal hysteresis resulting from the stomatal response lag. The day of this plant is seen to comprise of four stages of climatic conditions: no-cooling, high-cooling, equilibrium, and decaying-cooling stages.
Why it matches plant phenotyping methods複数の高解像度非侵襲イメージング技術を中核として、葉温、植物空隙率、葉面積密度、蒸散応答などの植物形質・状態を取得・解析しており、単なるルーチン測定ではない。
abstractIn this study, we unveil the diurnal dynamics of the microclimate of a Buxus sempervirens plant using multiple high-resolution non-intrusive imaging techniques.
In this work, a cost-effective prototype is designed to automate the determination of the Plant Area Index (PAI) and its transmission to the cloud in high tunnel crops. In these installations, the impossibility of using satellite or aerial drones imagery constitute a limitation for the application of traditional orthogonal imagery analysis, and thus image capture routines are required to be manually assisted for scaling and perspective correction. As an alternative to this laborious procedure, the proposed solution makes use of stereo-image analysis to gather depth information to assist the calculation of PAI. To this end, an ad-hoc node has been fully designed, exploiting the computational capabilities of a single board computer and the low energy consumption of a microcontroller. The performance of the device is validated experimentally in a preliminary deployment in the gardens of Universidad Loyola Andalucía.
Why it matches plant phenotyping methodsステレオ画像と深度情報を用いて作物のPlant Area Indexを自動測定する装置・手法を設計し、実環境で性能検証しているため、植物フェノタイピング手法が中心である。
abstracta cost-effective prototype is designed to automate the determination of the Plant Area Index (PAI)
Canopy cover is an important parameter affecting forest succession, carbon fluxes, and wildlife habitats. Several global maps with different spatial resolutions have been produced based on satellite images, but facing the deficiency of reliable references for accuracy assessments. The rapid development of unmanned aerial vehicle (UAV) equipped with consumer-grade camera enables the acquisition of high-resolution images at low cost, which provides the research community a promising tool to collect reference data. However, it is still a challenge to distinguish tree crowns and understory green vegetation based on the UAV-based true color images (RGB) due to the limited spectral information. In addition, the canopy height model (CHM) derived from photogrammetric point clouds has also been used to identify tree crowns but limited by the unavailability of understory terrain elevations. This study proposed a simple method to distinguish tree crowns and understories based on UAV visible images, which was referred to as BAMOS for convenience. The central idea of the BAMOS was the synergy of spectral information from digital orthophoto map (DOM) and structural information from digital surface model (DSM). Samples of canopy covers were produced by applying the BAMOS method on the UAV images collected at 77 sites with a size of about 1.0 km 2 across Daxing’anling forested area in northeast of China. Results showed that canopy cover extracted by the BAMOS method was highly correlated to visually interpreted ones with correlation coefficient ( r ) of 0.96 and root mean square error (RMSE) of 5.7%. Then, the UAV-based canopy covers served as references for assessment of satellite-based maps, including MOD44B Version 6 Vegetation Continuous Fields (MODIS VCF), maps developed by the Global Land Cover Facility (GLCF) and by the Global Land Analysis and Discovery laboratory (GLAD). Results showed that both GLAD and GLCF canopy covers could capture the dominant spatial patterns, but GLAD canopy cover tended to miss scattered trees in highly heterogeneous areas, and GLCF failed to capture non-tree areas. Most important of all, obvious underestimations with RMSE about 20% were easily observed in all satellite-based maps, although the temporal inconsistency with references might have some contributions.
Why it matches plant phenotyping methodsUAV画像のスペクトル・構造情報から森林樹冠被覆を抽出するBAMOS法を開発し、目視判読との精度検証および広域適用を行っており、植物状態の取得手法が中心である。
abstractThis study proposed a simple method to distinguish tree crowns and understories based on UAV visible images, which was referred to as BAMOS for convenience.
UAV photogrammetry was used to obtain the spatial distributions of ground height and vegetation height in a sandy, oligotrophic wetland in Naruto-Togane, where carnivorous plants grow. The correspondences between these ground and vegetation height distributions, groundwater quality, and vegetation distributions of Iris ensata, Patrinia scabiosifolia, and Phragmites australis were analyzed. The entire wetland is burned totally at the end of January every year and irrigated after the burning in order to preserve the carnivorous plant community in this wetland. Immediately after the burning, the 3D ground surface was measured using stereo photographs from a UAV. The 3D plant canopy surfaces of the entire wetland were also measured in June and September, when Iris ensata and Patrinia scabiosifolia were in bloom, respectively. The ground resolution was 15 mm. As a result, the ground surface elevation values were obtained with the average error of 33 mm. The spatial distribution of vegetation height was obtained from the ground and vegetation surface measurements. The wetland characteristics shown in this study provide the basis for the conservation of Naruto-Togane wetland.
Why it matches plant phenotyping methodsUAVステレオフォトグラメトリにより植生高を面的に推定し、測定誤差も評価しているため、植物形態の取得手法が研究の中心である。
abstractUAV photogrammetry was used to obtain the spatial distributions of ground height and vegetation height
Tree canopy density is an important parameter in developing a decision support system for precision orchard management including application of the right amount of nutrients at the right time and right location. Previous studies mostly focus on canopy characterization using the light detection and ranging (LiDAR) sensor which lacks critical color and texture information. This study utilized a ground-based stereo-vision sensor mounted on a utility vehicle to capture the canopy data during the growing stage (July, 2021) in a commercial orchard. The acquired color images, along with the point-cloud data, was used to segment out individual trees and estimate canopy density which is the measure of canopy cover per unit total area for each tree. A K-means segmentation method followed by depth thresholding was used to segment the desired tree canopies. The segmentation was compared to the manual segmentation and a F1 score of 0.78 was obtained. The density was obtained using the ratio of pixel count of vegetation and total area of interest around the trunk of specific trees. The obtained result was compared against the expert's assessment of tree vigor (categorical variable with values from 1 to 5), which showed a good correlation (R2 = 0.81). The obtained canopy density, along with other parameters including trunk size, and canopy color change during fall, will be used in the future to develop a decision support system for the assessment of nutrient requirement for individual trees to achieve the plant level precision nutrient management.
Why it matches plant phenotyping methodsステレオビジョン画像と点群からリンゴ樹の樹冠密度を抽出し、手動 segmentation および樹勢評価で検証しており、植物形質取得手法が中心である。
abstractThe acquired color images, along with the point-cloud data, was used to segment out individual trees and estimate canopy density
In order to obtain the phenotypic parameters of apple quickly and accurately, which were commonly used as the basis of fruit sorting, a fast estimation method of apple phenotypic parameters based on three-dimensional (3D) reconstruction was proposed in this study. In this study, a three-dimensional model was constructed to estimate the phenotypic parameters of apple, such as volume, height, diameter, and fruit shape index. Firstly, an image acquisition system was built to capture sequence images of fruit with a binocular stereo vision system, and the images were extracted and matched using the Accelerated-KAZE algorithm to create the point cloud data. Secondly, the point cloud data were matched with the algorithm of Iterative Closest Point to establish a whole model of apple, and the surface reconstruction model of fruit was obtained by constructing irregular triangulation network. Finally, the apple phenotypic parameters were calculated by means of segmentation, surface complement and integral of the fruit model. Total of 200 apples were used as samples in the experiment. By this method, the phenotypic parameters of the apples were estimated based on their 3D reconstruction model, and the linear regression analysis was carried out between the estimated values and the real values. The results showed that R2 of the linear regression fitting of each parameter was higher than 0.90. Among them, the fitting of volume was the best with R2 of 0.97. In addition, the average errors of apple volume, height, fruit shape index, maximum diameter D and minimum diameter d were 8.73 cm3, 1.43 mm, 1.28%, 0.90 mm, and 1.23 mm, respectively. According to the Chinese national standard of “fresh Apple”, the average error of the estimated result is within the range of allowable error. It indicates that the method of apple phenotypic parameter estimation based on 3D reconstruction has a high accuracy and practicability, and it can be used as the support for fruit sorting. Keywords: apple, 3D reconstruction, phenotypic parameter, stereo vision system, sequence image, sorting DOI: 10.25165/j.ijabe.20211405.6258 Citation: Ma H, Zhu X, Ji J T, Wang H, Jin X, Zhao K X. Rapid estimation of apple phenotypic parameters based on 3D reconstruction. Int J Agric & Biol Eng, 2021; 14(5): 180–188.
Why it matches plant phenotyping methodsリンゴの3D画像再構成から体積・高さ・直径・果形指数を推定する手法を開発し、実測値との回帰で精度検証しており、表現型取得が研究の中心である。
abstracta fast estimation method of apple phenotypic parameters based on three-dimensional (3D) reconstruction was proposed in this study.
Farm management and crop quality assessment is becoming increasingly automated to keep up with demand. The physical examination of the plant leaves, stems and fruit can provide valuable information about a plant’s health. Automating the visual inspection through machine vision spawns challenges such as occlusions, irregular lightning and varying environmental conditions. In this paper, a plant leaf extraction algorithm utilising depth from a stereo vision sensor is presented. The algorithm tackles multiple leaf segmentation and overlapping leaf separation through synergising features such as colour, shape and depth. Depth is particularly used to measure discontinuities along its gradient in the disparity maps . The algorithm has a segmentation rate of 78% for individual plant leaves, over a range of complex backgrounds and changing plant canopies. The proposed algorithm was evaluated using 272 cotton and hibiscus plant images with results demonstrating that depth properties were effective in separating occluded and overlapping leaves, with a high separation rate of 84%. Leaf occlusion could be detected automatically without adding any artificial tags on the leaf boundaries. Furthermore, the results show a nearly identical performance for both types of plants (cotton and hibiscus) under various lighting and environmental conditions. The developed algorithm could be potentially applied to other types of plants that have similar structures to cotton and hibiscus.
Why it matches plant phenotyping methods植物葉のセグメンテーションと重なり分離を、ステレオビジョンによって開発・評価した手法研究であり、葉形態の取得が中心です。
abstractIn this paper, a plant leaf extraction algorithm utilising depth from a stereo vision sensor is presented.
Measurement and estimation of physical properties of plant leaves have always been considered as important requirements for monitoring and optimizing of plant growth. This study aimed at utilization of image processing and artificial intelligence techniques for non-invasive and non-destructive estimation of bell pepper leaves properties in the first month of growth. Physical properties of bell pepper plant leaves were extracted from RGB images. The algorithm makes use of gradient magnitude and watershed image. Leaf area as the most important index of growth was estimated as a function of other physical parameters including leaf length, width, perimeter etc. Using stereo imaging, the leaf distance from the camera was measured and applied in pixel-wise calculations. Artificial neural networks (ANN) were trained based on a database of actual values of leaf properties (i.e. 311 bell-pepper plant leaves). The success rate of the developed algorithm for detection and separation of leaves was 84.32%. The Multilayer Perceptron (MLP) network could successfully estimate the leaf area values with a validation performance of 0.912.
Why it matches plant phenotyping methodsRGB画像、画像処理、ステレオ計測、ANNを用いて葉面積を推定する手法の開発と検証が中心であり、植物形質の取得・抽出方法に該当する。
abstractThis study aimed at utilization of image processing and artificial intelligence techniques for non-invasive and non-destructive estimation of bell pepper leaves properties
Monitoring plant growth is essential in modern agriculture to guarantee productivity. Since manual measurement of plant characteristics is laborious and expensive, automatic measures are desirable. This can be accomplished by methods such as vision-based structure from motion (SFM) to obtain the 3-D information of a plant. An SFM method based on binocular vision is here developed to acquire the physical parameters of plants. In this method, image sequences are captured by a binocular camera from multiple views of the target plant to improve the effectiveness and simplify the implementation. The spatial relationships between adjacent images are estimated through image feature extraction and matching. A disparity map is then built and the 3-D coordinate of each image pixel is obtained by applying stereo-vision. The connected coordinates then constitute the 3-D model of the plant. By doing so, plant structure parameters, such as height, canopy size, and trunk diameter, can be derived from the 3-D model. Experimental results show that the measured plant height, the canopy width, and the trunk diameter of the target plant are within an acceptable accuracy at the millimeter level, and the mean errors of the measured sizes are all less than 2%. This demonstrates the potential value of the proposed method for online growth monitoring of agricultural plants.
Why it matches plant phenotyping methods植物の3次元形状復元と構造形質推定手法を開発し、精度検証まで行っており、フェノタイピング手法が研究の中心である。
abstractAn SFM method based on binocular vision is here developed to acquire the physical parameters of plants.
Plantation forests play a critical role in forest products and ecosystems. Unmanned aerial vehicle (UAV) remote sensing has become a promising technology in forest related applications. The stand heights will reflect the growth and competition of individual trees in plantation. UAV laser scanning (ULS) and UAV stereo photogrammetry (USP) can both be used to estimate stand heights using different algorithms. Thus, this study aimed to deeply explore the variations of four kinds of stand heights including mean height, Lorey’s height, dominated height, and median height of coniferous plantations using different models based on ULS and USP data. In addition, the impacts of thinned point density of 30 pts to 10 pts, 5 pts, 1 pts, and 0.8 pts/m2 were also analyzed. Forest stand heights were estimated from ULS and USP data metrics by linear regression and the prediction accuracy was assessed by 10-fold cross validation. The results showed that the prediction accuracy of the stand heights using metrics from USP was basically as good as that of ULS. Lorey’s height had the highest prediction accuracy, followed by dominated height, mean height, and median height. The correlation between height percentiles metrics from ULS and USP increased with the increased height. Different stand heights had their corresponding best height percentiles as variables based on stand height characteristics. Furthermore, canopy height model (CHM)-based metrics performed slightly better than normalized point cloud (NPC)-based metrics. The USP was not able to extract exact terrain information in a continuous coniferous plantation for forest canopy cover (CC) over 0.49. The combination of USP and terrain from ULS can be used to estimate forest stand heights with high accuracy. In addition, the estimation accuracy of each forest stand height was slightly affected by point density, which can also be ignored.
Why it matches plant phenotyping methodsUAVレーザースキャンとステレオ画像から森林スタンド高を推定する手法を比較・検証しており、植物群落の形態形質取得が研究の中心である。
abstractForest stand heights were estimated from ULS and USP data metrics by linear regression and the prediction accuracy was assessed by 10-fold cross validation.
Published28 Jun 2021The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 4 · OpenAlex ↗
Abstract. The article discusses methods for constructing and using digital photogrammetric and cartographic models as a basis for growing tree height control and plantation planning in aerodrome areas. Forests or gardens in the take-off and landing flight areas, exceeding special limitation surfaces, are dangerous obstacles and intended to cut down. Tree and bush vegetation should be under periodic monitoring because of their growth. The research was aimed to determine the maximum permissible obstacle height and tree age when it reaches the obstacle limitation surface altitude. For these purposes, it is proposed to use geospatial modeling and geoinformation analysis methods. As a basis for geospatial models, remote sensing optical stereo images were used. The allowable height is calculated as a difference between 3D obstacle limitation surface and the earth surface altitude values. The article presents the study results for a Belarus climatic zone, where the tree species predictive age in reaching the maximum permissible height is calculated. The main goal of the technology is to manage the aerodrome forest plantation growth without further labor-intensive monitoring, while ensuring the safety of aircraft flights.
Why it matches plant phenotyping methods航空障害物監視を目的とするが、リモートセンシングのステレオ画像と3D地理空間モデルにより樹木高を推定・監視する手法が研究の中心であり、植物の明示的形質を測定している。
abstractThe article discusses methods for constructing and using digital photogrammetric and cartographic models as a basis for growing tree height control and plantation planning in aerodrome areas.
The objective of this study was to develop a machine-vision-based height measurement system for an autonomous cultivation robot. The system was developed with a simple stereo camera configuration to facilitate practical field applications and was used to acquire accurate height measurements of various field crops. The acquired stereo images were converted to disparity maps through stereo matching, and the disparity of each pixel was calculated to determine the depth of the distance between the camera and the crop. Depth maps were used to determine the edges of regions of interest (ROI) and the crop regions were segmented using the edges located in the expected crop region closest to the camera without using any additional labelling. The crop height was calculated using the highest points in the ROI. This approach was tested on five crops, and the results showed that the system could detect the target crop region even when objects overlapped in the acquired images. Furthermore, the crop heights estimated with the developed system showed strong agreement with actual crop heights measured manually, with the R² ranging from 0.78 to 0.84. These results indicate that the developed algorithm is capable of measuring crop heights in various ranges for agricultural robot applications.
Why it matches plant phenotyping methodsステレオビジョンによる作物高の取得・推定システムを開発し、手動測定と比較検証しており、植物形質の計測手法が研究の中心である。
abstractThe objective of this study was to develop a machine-vision-based height measurement system for an autonomous cultivation robot.
MaizeSorghumSoybeanField / plotLiDAR / point cloudStereoLeafSeed / grainStem / branchWhole plant / canopy / plot / field
Highlights A custom-built camera module named PhenoStereo was developed for high-throughput field-based plant phenotyping. Novel integration of strobe lights facilitated application of PhenoStereo in various environmental conditions. Image-derived stem diameters were found to have high correlations with ground truth, which outperformed any previously reported sensing approach. PhenoStereo showed promising potential to characterize a broad spectrum of plant phenotypes. Abstract. The stem diameter of sorghum plants is an important trait for evaluation of stalk strength and biomass potential, but it is a challenging sensing task to automate in the field due to the complexity of the imaging object and the environment. In recent years, stereo vision has offered a viable three-dimensional (3D) solution due to its high spatial resolution and wide selection of camera modules. However, the performance of in-field stereo imaging for plant phenotyping is adversely affected by textureless regions, occlusion of plants, variable outdoor lighting, and wind conditions. In this study, a portable stereo imaging module named PhenoStereo was developed for high-throughput field-based plant phenotyping. PhenoStereo features a self-contained embedded design, which makes it capable of capturing images at 14 stereoscopic frames per second. In addition, a set of customized strobe lights is integrated to overcome lighting variations and enable the use of high shutter speed to overcome motion blur. PhenoStereo was used to acquire a set of sorghum plant images, and an automated point cloud data processing pipeline was developed to automatically extract the stems and then quantify their diameters via an optimized 3D modeling process. The pipeline employed a mask region convolutional neural network (Mask R-CNN) for detecting stalk contours and a semi-global block matching (SGBM) stereo matching algorithm for generating disparity maps. The correlation coefficient (r) between the image-derived stem diameters and the ground truth was 0.97 with a mean absolute error (MAE) of 1.44 mm, which outperformed any previously reported sensing approach. These results demonstrate that, with proper customization, stereo vision can be an effective sensing method for field-based plant phenotyping using high-fidelity 3D models reconstructed from stereoscopic images. Based on the results from sorghum plant stem diameter sensing, this proposed stereo sensing approach can likely be extended to characterize a broad range of plant phenotypes, such as the leaf angle and tassel shape of maize plants and the seed pods and stem nodes of soybean plants. Keywords: Field-based high-throughput phenotyping, Point cloud, Stem diameter, Stereo vision.
Why it matches plant phenotyping methodsソルガム茎径という植物形質を対象に、ステレオ撮像モジュールと自動点群処理パイプラインを開発し、地上真値との精度検証まで行っており、表現型取得手法が研究の中心である。
abstracta portable stereo imaging module named PhenoStereo was developed for high-throughput field-based plant phenotyping
In order to obtain the phenotypic parameters of apple quickly and accurately, which were commonly used as the basis of fruit sorting, a fast estimation method of apple phenotypic parameters based on three-dimensional (3D) reconstruction was proposed in this study. In this study, a three-dimensional model was constructed to estimate the phenotypic parameters of apple, such as volume, height, diameter, and fruit shape index. Firstly, an image acquisition system was built to capture sequence images of fruit with a binocular stereo vision system, and the images were extracted and matched using the Accelerated-KAZE algorithm to create the point cloud data. Secondly, the point cloud data were matched with the algorithm of Iterative Closest Point to establish a whole model of apple, and the surface reconstruction model of fruit was obtained by constructing irregular triangulation network. Finally, the apple phenotypic parameters were calculated by means of segmentation, surface complement and integral of the fruit model. Total of 200 apples were used as samples in the experiment. By this method, the phenotypic parameters of the apples were estimated based on their 3D reconstruction model, and the linear regression analysis was carried out between the estimated values and the real values. The results showed that R2 of the linear regression fitting of each parameter was higher than 0.90. Among them, the fitting of volume was the best with R2 of 0.97. In addition, the average errors of apple volume, height, fruit shape index, maximum diameter D and minimum diameter d were 8.73 cm3, 1.43 mm, 1.28%, 0.90 mm, and 1.23 mm, respectively. According to the Chinese national standard of “fresh Apple”, the average error of the estimated result is within the range of allowable error. It indicates that the method of apple phenotypic parameter estimation based on 3D reconstruction has a high accuracy and practicability, and it can be used as the support for fruit sorting. Keywords: apple, 3D reconstruction, phenotypic parameter, stereo vision system, sequence image, sorting DOI: 10.25165/j.ijabe.20211405.6258 Citation: Ma H, Zhu X, Ji J T, Wang H, Jin X, Zhao K X. Rapid estimation of apple phenotypic parameters based on 3D reconstruction. Int J Agric & Biol Eng, 2021; 14(5): 180–188.
Why it matches plant phenotyping methodsリンゴの体積・高さ・直径・果形指数を3D再構成で推定する画像計測手法を開発し、実測値との回帰で精度検証しており、表現型取得が研究の中心である。
abstracta fast estimation method of apple phenotypic parameters based on three-dimensional (3D) reconstruction was proposed in this study
Forest canopy height is an indispensable forest vertical structure parameter for understanding the carbon cycle and forest ecosystem services. A variety of studies based on spaceborne Lidar, such as ICESat, ICESat-2 and airborne Lidar, were conducted to estimate forest canopy height at multiple scales. However, while a few studies have been conducted based on ICESat-2 simulated data from airborne Lidar data, few studies have analyzed ATL08 and ATL03 products derived from the ATLAS sensor onboard ICESat-2 for regional vegetation canopy height mapping. It is necessary and promising to explore how data obtained by ICESat-2 can be applied to estimate forest canopy height. This study proposes a new means to estimate forest canopy height, defined as the mean height of trees within a given forest area, using a combination of ICESat-2 ATL08 and ATL03 data and ZY-3 satellite stereo images. Five procedures were used to estimate the forest canopy height of the city of Nanning in China: (1) Processing ground photons in a 30 m × 30 m grid; (2) Extracting a digital surface model (DSM) using ZY-3 stereo images; (3) Calculating a discontinuous canopy height model (CHM) dataset; (4) Validating the DSM and ground photon height using GEDI data; (5) Estimating the regional wall-to-wall forest canopy height product based on the backpropagation artificial neural network (BP-ANN) model and Landsat 8 vegetation indices and independent accuracy assessments with field measured plots. The validation shows a root mean square error (RMSE) of 3.34 m to 3.47 m and a coefficient of determination R2 = 0.51. The new method shows promise and can be used for large-scale forest canopy height mapping at various resolutions or in combination with other data, such as SAR images. Finally, this study analyzes resolutions and how to filter effective data when ATL08 data are directly used to generate regional or global vegetation height products, which will be the focus of future research.
Why it matches plant phenotyping methodsICESat-2、ステレオ画像、CHM、ANNを組み合わせて森林キャノピー高を推定し、GEDIおよび実測プロットで検証する手法研究であり、植物形質の取得・推定が中心である。
abstractThis study proposes a new means to estimate forest canopy height, defined as the mean height of trees within a given forest area, using a combination of ICESat-2 ATL08 and ATL03 data and ZY-3 satellite stereo images.
Machine-vision-based crop detection is a central issue for digital farming, and crop height is an important factor that should be automatically measured in robot-based cultivations. Three-dimensional (3D) imaging cameras make it possible to measure actual crop height; however, camera tilt due to irregular ground conditions in farmland prevents accurate height measurements. In this study, stereo-vision-based crop height was measured with compensation for the camera tilt effect. For implementing the tilt of the camera installed on farm machines (e.g., tractors), we developed a posture tilt simulator for indoor testing that could implement the camera tilt by pitch and roll rotations. Stereo images were captured under various simulator tilt conditions, and crop height was measured by detecting the crop region in a disparity map, which was generated by matching stereo images. The measured height was compensated for by correcting the position of the region of interest (RoI) in the 3D image through coordinate transformation between camera coordinates and simulator coordinates. The tests were conducted by roll and pitch rotation around the simulator coordinates. The results showed that crop height could be measured using stereo vision, and that tilt compensation reduced the average error from 15.6 to 3.9 cm. Thus, the crop height measurement system proposed in this study, based on 3D imaging and a tilt sensor, can contribute to the automatic perception of agricultural robots.
Why it matches plant phenotyping methodsステレオ画像と傾斜センサーを用いた作物高の測定システムを開発・検証しており、植物形質の取得手法が研究の中心である。
abstractIn this study, stereo-vision-based crop height was measured with compensation for the camera tilt effect.
Forest monitoring tools are needed to promote effective and data driven forest management and forest policies. Remote sensing techniques can increase the speed and the cost-efficiency of the forest monitoring as well as large scale mapping of forest attribute (wall-to-wall approach). Digital Aerial Photogrammetry (DAP) is a common cost-effective alternative to airborne laser scanning (ALS) which can be based on aerial photos routinely acquired for general base maps. DAP based on such pre-existing dataset can be a cost effective source of large scale 3D data. In the context of forest characterization, when a quality Digital Terrain Model (DTM) is available, DAP can produce photogrammetric Canopy Height Model (pCHM) which describes the tree canopy height. While this potential seems pretty obvious, few studies have investigated the quality of regional pCHM based on aerial stereo images acquired by standard official aerial surveys. Our study proposes to evaluate the quality of pCHM individual tree height estimates based on raw images acquired following such protocol using a reference filed-measured tree height database. To further ensure the replicability of the approach, the pCHM tree height estimates benchmarking only relied on public forest inventory (FI) information and the photogrammetric protocol was based on low-cost and widely used photogrammetric software. Moreover, our study investigates the relationship between the pCHM tree height estimates based on the neighboring forest parameter provided by the FI program. Our results highlight the good agreement of tree height estimates provided by pCHM using DAP with both field measured and ALS tree height data. In terms of tree height modeling, our pCHM approach reached similar results than the same modeling strategy applied to ALS tree height estimates. Our study also identified some of the drivers of the pCHM tree height estimate error and found forest parameters like tree size (diameter at breast height) and tree type (evergreenness/deciduousness) as well as the terrain topography (slope) to be of higher importance than image survey parameters like the variation of the overlap or the sunlight condition in our dataset. In combination with the pCHM tree height estimate, the terrain slope, the Diameter at Breast Height (DBH) and the evergreenness factor were used to fit a multivariate model predicting the field measured tree height. This model presented better performance than the model linking the pCHM estimates to the field tree height estimates in terms of r² (0.90 VS 0.87) and root mean square error (RMSE, 1.78 VS 2.01 m). Such aspects are poorly addressed in literature and further research should focus on how pCHM approaches could integrate them to improve forest characterization using DAP and pCHM. Our promising results can be used to encourage the use of regional aerial orthophoto surveys archive to produce large scale quality tree height data at very low additional costs, notably in the context of updating national forest inventory programs.
Why it matches plant phenotyping methods航空画像と写真測量による個体樹高（植物形態形質）の推定手法を開発・評価し、現地測定およびALSとベンチマークしているため、森林モニタリング一般ではなく植物表現型取得手法が中心です。
abstractOur study proposes to evaluate the quality of pCHM individual tree height estimates based on raw images acquired following such protocol using a reference filed-measured tree height database.
Abstract The plant factory is extensive cultivation to produce a high quality of vegetables under a controllable environment. The concept of Precision Agriculture (PA) was introduced to improve the plant factory production by the implementation of a crop growth monitoring system. Crop growth can be estimated by monitoring of crop height and canopy foliage by the use of computer vision technology. In our previous study, we have introduced a plant height monitoring system based on depth perception using a stereo camera. However, the validity of the various type of leave is necessary to be tested. The objective of this study was to implement the crop growth monitoring system to monitor plant development with various type of leave for system validation and evaluation. The crop growth monitoring system composed of a stereo camera implementing the depth perception for estimating the distance from camera to highest point in the crop. The implementation of the system with various types of leaves and characteristics has been conducted for (a) Samhon, (b) Lettuce, and (c) Pagoda. The developed crop growth monitoring system could perform the time series estimation of crop height with a maximum error of RMSE 0.875 cm on Pagoda, and MAPE of 5.56% on Lettuce. The system demonstrates better estimation on Samhong with minimum error RMSE of 0.408cm and 2.27 % of MAPE. Overall validation for the estimated height vs actual measurement indicates that the coefficient of determination higher than 0.7 means that it has substantial features for estimating the plant height.
Why it matches plant phenotyping methodsステレオカメラによる植物高推定システムを実装し、異なる葉型で性能を検証・評価しており、植物フェノタイピング手法が中心である。
abstractThe objective of this study was to implement the crop growth monitoring system to monitor plant development with various type of leave for system validation and evaluation.
Automatic vision-based picking in orchards and fields is a highly challenging task. The orchard banana central stock, which is large in size, low in color contrast, and falls within a complex background, was taken as the subject in this research. A measurement framework based on multi-vision technology was established, and a set of general methods were utilized to improve the comprehensive performance of multi-view-geometry-based vision modules in orchard picking tasks. Multiple cameras at different angles were deployed to maximize the perception range. The global geometric parameters of the cameras were calibrated and a robust semantic segmentation network was trained to achieve effective image pre-processing. A novel adaptive stereo matching strategy was designed to ensure that the robot reliably completes 3D triangulation at various depths as it moves across the target area. Global calibration errors were corrected via a high-accuracy point cloud stitching algorithm. Experimental results indicated that the proposed adaptive stereo matching strategy was accurate to different sampling depths and showed stable performance, and the proposed point cloud stitching algorithm accurately stitched multi-view point clouds. This work provides theoretical and practical references for the 3D sensing of banana central stocks in complex environments. The proposed technique was designed for adaptability of the multi-vision system for field perception, so it can be easily transferred to similar applications such as the 3D reconstruction of agricultural targets, 3D positioning of fruit clusters, and 3D robotic arm obstacle avoidance.
Why it matches plant phenotyping methodsバナナ株の3次元形状を取得・再構成するマルチビジョン計測法が研究の中心であり、単なる収穫対象の位置検出を超えた植物器官の形態計測手法に該当する。
abstractA measurement framework based on multi-vision technology was established
During the process of automated crop picking, the two hand-eye coordination operation systems, namely "eye to hand" and "eye in hand" have their respective advantages and disadvantages. It is challenging to simultaneously consider both the operational accuracy and the speed of a manipulator. In response to this problem, this study constructs a "global-local" visual servo picking system based on a prototype of a picking robot to provide a global field of vision (through binocular vision) and carry out the picking operation using the monocular visual servo. Using tomato picking as an example, experiments were conducted to obtain the accuracies of judgment and range of fruit maturity, and the scenario of fruit-bearing was simulated over an area where the operation was ongoing to examine the rate of success of the system in terms of continuous fruit picking. The results show that the global-local visual servo picking system had an average accuracy of correctly judging fruit maturity of 92.8%, average error of fruit distance measurement in the range 0.485 cm, average time for continuous fruit picking of 20.06 s, and average success rate of picking of 92.45%.
Why it matches plant phenotyping methodsトマト果実の成熟度を画像視覚システムで判定し、その精度を評価することが収穫ロボット手法の中心的な技術要素であるため、果実状態の画像ベース計測として含める。
abstractthis study constructs a "global-local" visual servo picking system based on a prototype of a picking robot to provide a global field of vision (through binocular vision) and carry out the picking operation using the monocular visual servo.
The method proposed in this paper is part of the vision module of a garden robot capable of navigating towards rose bushes and clip them according to a set of pruning rules. The method is responsible for performing the segmentation of the branches and recovering their morphology in 3D. The obtained reconstruction allows the manipulator of the robot to select the candidate branches to be pruned. This method first obtains a stereo pair of images and calculates the disparity image using block matching and the segmentation of the branches using a Fully Convolutional Neuronal Network modified to return a map with the probability at the pixel level of the presence of a branch. A post-processing step combines the segmentation and the disparity in order to improve the results. Then, the skeleton of the plant and the branching structure are calculated, and finally, the 3D reconstruction is obtained. The proposed approach is evaluated with five different datasets, three of them compiled by the authors and two from the state of the art, including indoor and outdoor scenes with uncontrolled environments. The different steps of the proposed pipeline are evaluated and compared with other state-of-the-art methods, showing that the accuracy of the segmentation improves other methods for this task, even with variable lighting, and also that the skeletonization and the reconstruction processes obtain robust results.
Why it matches plant phenotyping methodsバラ植物の枝のセグメンテーション、骨格化、分枝構造および3D形態を抽出する画像解析手法が中心で、複数データセットによる評価・比較も行っている。単なる収穫対象の検出を超え、再利用可能な植物構造形質を推定している。
abstractThe method is responsible for performing the segmentation of the branches and recovering their morphology in 3D.
Forest inventorying is time-consuming and expensive. Recent research involving photogrammetry promises to reduce the cost of inventorying. Existing photogrammetry methods require substantial data-processing time, however. Our aim was to reduce data-acquisition and processing times while obtaining relatively accurate diameter estimates compared to manual and other digital measurements. We developed an algorithm to identify the ground and measure diameter at breast height (dbh) or any height along a stem from the recorded video footage of trees taken with a stereo camera. Footage acquisition time, dbh root mean square error, and mean absolute error were used as comparison metrics with other methods. The time to perform three recordings for 40 trees was about 30 minutes. We recorded data at 1 m, 3 m, and 5 m from the trunk, and our dbh root mean square errors were 1.28 cm (0.50 in.), 1.47 cm (0.58 in.), and 2.57 cm (1.01 in.), respectively, using manual measures as the control. This terrestrial stereoscopic photogrammetric method is much more efficient computationally than popular terrestrial structure-from-motion photogrammetry and substantially lowers time, costs, and complexity for data acquisition and processing compared with terrestrial laser scanning.
Why it matches plant phenotyping methodsステレオカメラ映像から樹木幹径（dbh）を自動測定するアルゴリズムを開発し、手動・既存手法と精度および処理時間を比較検証しており、植物形質取得法が研究の中心です。
abstractWe developed an algorithm to identify the ground and measure diameter at breast height (dbh) or any height along a stem from the recorded video footage of trees taken with a stereo camera.
Stereo vision is a 3D imaging method that allows quick measurement of plant architecture. Historically, the method has mainly been developed in controlled conditions. This study identified several challenges to adapt the method to natural field conditions and propose solutions. The plant traits studied were leaf area, mean leaf angle, leaf angle distribution, and canopy height. The experiment took place in a winter wheat, Triticum aestivum L., field dedicated to fertilization trials at Gembloux (Belgium). Images were acquired thanks to two nadir cameras. A machine learning algorithm using RGB and HSV color spaces is proposed to perform soil-plant segmentation robust to light conditions. The matching between images of the two cameras and the leaf area computation was improved if the number of pixels in the image of a scene was binned from 2560 × 2048 to 1280 × 1024 pixels, for a distance of 1 m between the cameras and the canopy. Height descriptors such as median or 95th percentile of plant heights were useful to precisely compare the development of different canopies. Mean spike top height was measured with an accuracy of 97.1 %. The measurement of leaf area was affected by overlaps between leaves so that a calibration curve was necessary. The leaf area estimation presented a root mean square error (RMSE) of 0.37. The impact of wind on the variability of leaf area measurement was inferior to 3% except at the stem elongation stage. Mean leaf angles ranging from 53° to 62° were computed for the whole growing season. For each acquisition date during the vegetative stages, the variability of mean angle measurement was inferior to 1.5% which underpins that the method is precise.
Why it matches plant phenotyping methodsステレオビジョンによる植物形態形質の取得法を圃場条件へ適応し、画像処理、形質推定、精度・誤差を検証しており、フェノタイピング手法が中心である。
abstractThis study identified several challenges to adapt the method to natural field conditions and propose solutions.
The stereo vision experiments were conducted under the laboratory conditions by using LabVIEW programming language. An artificial crop plant and six types of artificial weed samples were used in the experiments. The information related to the plant height is a relevant feature to classify the crop plant and weed, especially in the early growth stage. A binocular stereo vision system was established by using two identical webcams with parallel optical axes and a laptop computer to discriminate the artificial crop plant and six types of weeds correctly. The calculated depth values were compared with the physical measurements for the same points. While the measurement error of the system was less than 3.50% for the artificial crop plant, it was less than 4.20% for six artificial weed samples. There were also strong, positive and significant linear correlations between the stereo vision and physical height measurements for artificial crop plant and weed samples. Calculated correlation values (R2) between the stereo vision and physical height measurements were 0.962 for the artificial crop plant and 0.978 for the artificial weed samples, respectively. That stereo vision system could be integrated into automatic spraying systems for intra-row spraying applications.
Why it matches plant phenotyping methodsステレオビジョンで植物体高を測定・検証する手法が研究の中心であり、作物・雑草の識別に用いる植物形質の取得精度も評価している。
abstractA binocular stereo vision system was established by using two identical webcams with parallel optical axes and a laptop computer to discriminate the artificial crop plant and six types of weeds correctly.
As a plant organ with the largest surface area, leaves are the main place where photosynthesis and respiration take place. High-throughput phenotyping of crop leaves is of great significance for breeding, growth monitoring, and increasing crop yield. Due to the highly complex and diversified plant structures, automated leaf segmentation and phenotypic feature extraction remain to be challenging tasks. In this article, we propose a novel five-stage framework that comprises multiview stereo point cloud reconstruction, preprocessing, stems removal in canopy, leaf segmentation, and leaf phenotypic feature extraction to carry out leaf phenotyping on two types of ornamentals-Maranta arundinacea and Dieffenbachia picta. The phenotypic traits such as the leaf area, leaf length, width, and leaf inclination angle for each single leaf are calculated and compared with ground truths. The experimental results show that the average accuracy of calculated leaf area of the two species reached 96.8% and 97.8%, respectively. The average errors of both the calculated leaf length and width of Maranta arundinacea are less than 4.0%, and for Dieffenbachia picta, the average errors of calculated leaf length and width are both no higher than 4.7%. The average errors of calculated leaf inclination angle for the two plant species are 2.9° and 3.0°, respectively.
Why it matches plant phenotyping methodsマルチビュー点群から葉を分割し、葉面積・長さ・幅・傾斜角を抽出する植物フェノタイピング手法を開発・検証しており、方法が研究の中心である。
abstractwe propose a novel five-stage framework that comprises multiview stereo point cloud reconstruction, preprocessing, stems removal in canopy, leaf segmentation, and leaf phenotypic feature extraction to carry out leaf phenotyping
In remote sensing-based forest inventories 3D point cloud data, such as acquired from airborne laser scanning, are well suited for estimating the volume of growing stock and stand height, but tree species recognition often requires additional optical imagery. A combination of 3D data and optical imagery can be acquired based on aerial imaging only, by using stereo photogrammetric 3D canopy modeling. The use of aerial imagery is well suited for large-area forest inventories, due to low costs, good area coverage and temporally rapid cycle of data acquisition. Stereo-photogrammetric canopy modeling can also be applied to previously acquired imagery, such as for aerial ortho-mosaic production, assuming that the imagery has sufficient stereo overlap. In this study we compared two stereo-photogrammetric canopy models combined with contemporary satellite imagery in forest inventory. One canopy model was based on standard archived imagery acquired primarily for ortho-mosaic production, and another was based on aerial imagery whose acquisition parameters were better oriented for stereo-photogrammetric canopy modeling, including higher imaging resolution and greater stereo-coverage. Aerial and satellite data were tested in the estimation of growing stock volume, volumes of main tree species, basal area and diameter and height. Despite the better quality of the latter canopy model, the difference of the accuracy of the forest estimates based on the two different data sets was relatively small for most variables (differences in RMSEs were 0–20%, depending on variable). However, the estimates based on stereo-photogrammetrically oriented aerial data retained better the original variation of the forest variables present in the study area.
Why it matches plant phenotyping methods航空画像からステレオ写真測量による林冠モデルを構築し、森林の体積・樹高・胸高断面積・直径などの植物・林分形質を推定する手法を比較検証しており、測定法が研究の中心です。
abstractIn this study we compared two stereo-photogrammetric canopy models combined with contemporary satellite imagery in forest inventory.
The dimensions of phenotyping parameters such as the thickness of rice play an important role in rice quality assessment and phenotyping research. The objective of this study was to propose an automatic method for extracting rice thickness. This method was based on the principle of binocular stereovision but avoiding the problem that it was difficult to directly match the corresponding points for 3D reconstruction due to the lack of texture of rice. Firstly, the shape features of edge, instead of texture, was used to match the corresponding points of the rice edge. Secondly, the height of the rice edge was obtained by way of space intersection. Finally, the thickness of rice was extracted based on the assumption that the average height of the edges of multiple rice is half of the thickness of rice. According to the results of the experiments on six kinds of rice or grain, errors of thickness extraction were no more than the upper limit of 0.1 mm specified in the national industry standard. The results proved that edge features could be used to extract rice thickness and validated the effectiveness of the thickness extraction algorithm we proposed, which provided technical support for the extraction of phenotyping parameters for crop researchers.
Why it matches plant phenotyping methods米粒の厚さという植物形質を、ステレオビジョンとエッジ特徴により自動抽出する手法を開発・検証しており、フェノタイピング手法が研究の中心です。
abstractThe objective of this study was to propose an automatic method for extracting rice thickness.
Plant architectural traits are important factors in determining grain and biomass productivity of sorghum (Sorghum bicolor (L) Moench). However, collecting data on these architectural traits is labor-intensive and time-consuming, especially when using numerous lines in quantitative genetic studies or breeding programs. Therefore, we used the high-throughput field-based robotic platform PhenoBot 1.0 to collect whole canopy stereo images from a large association mapping panel under field conditions. These images were used to create a plot-based 3D reconstruction of the canopy from which phenotypic features were automatically extracted. These features included: plot-based plant height (PPH), plot-based plant width (PPW), plant surface area (PSA), and convex-hull volume (CHV). A small sub-set of sorghum lines were used to obtain ground-truth measurements to validate the image-derived descriptors, and determine their biological significance. PPH was highly correlated with manually measured plant height; PPW correlated with SinAL, defined as leaf length multiplied by the sine of its angle; PSA was associated with manually measured total plant surface area; and CHV was a function of both flag-leaf height and SinAL. Association mapping of PPH identified chromosomal regions containing known plant height genes, confirming the accuracy of the automatic feature extraction process. For the other phenotypic features, significant markers were identified within genomic regions that have been previously reported to control plant architectural characteristics in sorghum such as tiller number, shoot compactness, leaf length, surface area, and angle. The image processing method used in this study contributes new knowledge to the development of high-throughput phenotyping techniques and represents a novel tool for plant breeders.
Why it matches plant phenotyping methods圃場ロボットで取得したステレオ画像から3D植物形態特徴を自動抽出し、地上真値で検証した高スループット表現型計測研究であり、方法が中心的です。
abstractwe used the high-throughput field-based robotic platform PhenoBot 1.0 to collect whole canopy stereo images from a large association mapping panel under field conditions.
Published18 Oct 2019The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 2 · OpenAlex ↗
Abstract. Stereo photogrammetry enables collecting precise and detailed three-dimensional data of terrestrial objects. The estimation of qualitative and quantitative tree attributes, in particular those related to geometric measures, is crucial for forest management. In this study, a stereo imaging system is designed in order to measure a set of geometric attributes of urban trees such as crown dimensions, height and diameter at multiple height levels. The system consists of two hardware and software components. The hardware comprises two cameras with a specified baseline, two raspberry pi 3 model B+ boards, a GPS, an IMU and a power bank, all embedded in a box. The software includes a connection between the camera and the raspberry pi 3 in each side as well as data transfer to a laptop. The calibration is conducted in laboratory prior to applying the system and leads to achieve a disparity image from a pair of stereo imagery, which is then processed to extract dense point clouds. The system enables measuring basic, yet crucial tree attributes such as height and diameter in near real-time basis. The entire process is conducted by means of drastic libraries in Robot Operating System (ROS). Apart from being convenient and real-time, the system is associated with the potential for timely and precise measurements, which enable comparative analysis against other existing remote measurement systems as well as reference field data.
Why it matches plant phenotyping methodsステレオ画像と三次元点群を用いて樹木の高さ・樹冠寸法・幹径を測定するシステムを設計・実装しており、植物形質の取得手法が研究の中心である。
abstractIn this study, a stereo imaging system is designed in order to measure a set of geometric attributes of urban trees such as crown dimensions, height and diameter at multiple height levels.
This study was conducted to estimate the dimensions of horticultural products and the mean plant height of plug seedlings using three-dimensional (3D) images.Two types of camera, a ToF camera and a stereo-vision camera, were used to acquire 3D images for horticultural products and plug seedlings.The errors calculated from the ToF images for dimensions of horticultural products and mean height of plug seedlings were lower than those predicted from stereo-vision images.A new indicator was defined for determining the mean plant height of plug seedlings.Except for watermelon with tap, the errors of circumference and height of horticultural products were 0.0-3.0%and 0.0-4.7%,respectively.Also, the error of mean plant height for plug seedlings was 0.0-5.5%.The results revealed that 3D images can be utilized to estimate accurately the dimensions of horticultural products and the plant height of plug seedlings.Moreover, our method is potentially applicable for segmenting objects and for removing outliers from the point cloud data based on the 3D images of horticultural crops.
Why it matches plant phenotyping methods3D画像を用いて園芸作物の寸法と苗の平均草丈を推定する手法を開発・誤差検証しており、植物形質の取得・抽出が研究の中心です。
abstractThis study was conducted to estimate the dimensions of horticultural products and the mean plant height of plug seedlings using three-dimensional (3D) images.
Abstract. Precise and detailed reconstruction of 3D plant models is an important goal in computer vision. Based on these models, important parameters can be extracted, which would be very useful for monitoring the tree health situation. This paper has firstly constructed the 3D plant model based on MC-CNN using close-range photogrammetric imagery, and then applied a leaf index based segmentation to highlight the leaves region. In the end, the 3D model of each leaf can be represented and some geometric parameters of the leaf are designed and analyzed to predict the drought status. The experiments on real close-range stereo imagery justified the performance of the proposed approach to differentiate drought and healthy leaves.
Why it matches plant phenotyping methods3D画像再構成、葉領域分割、葉の幾何形状パラメータ抽出を組み合わせ、葉の乾燥状態を判別する植物フェノタイピング手法が中心である。
abstractThis paper has firstly constructed the 3D plant model based on MC-CNN using close-range photogrammetric imagery, and then applied a leaf index based segmentation to highlight the leaves region.
Phenotyping provides important support for corn breeding. Unfortunately, the rapid detection of phenotypes has been the major limiting factor in estimating and predicting the outcomes of breeding programs. This study was focused on the potential of phenotyping to support corn breeding using unmanned aerial vehicle (UAV) images, aiming at mining and deepening UAV techniques for comparing phenotypes and screening new corn varieties. Two geometric traits (plant height, canopy leaf area index (LAI)) and one lodging resistance trait (lodging area) were estimated in this study. It was found that stereoscopic and photogrammetric methods were promising ways to calculate a digital surface model (DSM) for estimating corn plant height from UAV images, with R2 = 0.7833 (p
Why it matches plant phenotyping methodsUAV画像とステレオ・写真測量を用いて、トウモロコシの草丈、LAI、倒伏面積を推定する手法を中心に評価しており、植物表現型取得・推定が研究の主要目的である。
abstractThis study was focused on the potential of phenotyping to support corn breeding using unmanned aerial vehicle (UAV) images, aiming at mining and deepening UAV techniques for comparing phenotypes and screening new corn varieties.
This article describes the design and field evaluation of a low-cost, high-throughput phenotyping robot for energy sorghum for use in biofuel production. High-throughput phenotyping approaches have been used in isolated growth chambers or greenhouses, but there is a growing need for field-based, precision agriculture techniques to measure large quantities of plants at high spatial and temporal resolutions throughout a growing season. A low-cost, tracked mobile robot was developed to collect phenotypic data for individual plants and tested on two separate energy sorghum fields in Central Illinois during summer 2016. Stereo imaging techniques determined plant height, and a depth sensor measured stem width near the base of the plant. A data capture rate of 0.4 ha, bi-weekly, was demonstrated for platform robustness consistent with various environmental conditions and crop yield modeling needs, and formative human–robot interaction observations were made during the field trials to address usability. This work is of interest to researchers and practitioners advancing the field of plant breeding because it demonstrates a new phenotyping platform that can measure individual plant architecture traits accurately (absolute measurement error at 15% for plant height and 13% for stem width) over large areas at a sub-daily frequency; furthermore, the design of this platform can be extended for phenotyping applications in maize or other agricultural row crops.
Why it matches plant phenotyping methods個体の草丈・茎幅を取得する高スループット表現型計測ロボットの設計と圃場評価が中心であり、方法開発・技術評価に該当する。
abstractThis article describes the design and field evaluation of a low-cost, high-throughput phenotyping robot for energy sorghum for use in biofuel production.
The spatial and temporal variability of crop parameters are fundamental in precision agriculture. Remote sensing of crop canopy can provide important indications on the growth variability and help understand the complex factors influencing crop yield. Plant biomass is considered an important parameter for crop management and yield estimation, especially for grassland and cover crops. A recent approach introduced to model crop biomass consists in the use of RGB (red, green, blue) stereo images acquired from unmanned aerial vehicles (UAV) coupled with photogrammetric softwares to predict biomass through plant height (PHT) information. In this study, we generated prediction models for fresh (FBM) and dry biomass (DBM) of black oat crop based on multi-temporal UAV RGB imaging. Flight missions were carried during the growing season to obtain crop surface models (CSMs), with an additional flight before sowing to generate a digital terrain model (DTM). During each mission, 30 plots with a size of 0.25 m² were distributed across the field to carry ground measurements of PHT and biomass. Furthermore, estimation models were established based on PHT derived from CSMs and field measurements, which were later used to build prediction maps of FBM and DBM. The study demonstrates that UAV RGB imaging can precisely estimate canopy height (R2 = 0.68–0.92, RMSE = 0.019–0.037 m) during the growing period. FBM and DBM models using PHT derived from UAV imaging yielded R2 values between 0.69 and 0.94 when analyzing each mission individually, with best results during the flowering stage (R2 = 0.92–0.94). Robust models using datasets from different growth stages were built and tested using cross-validation, resulting in R2 values of 0.52 for FBM and 0.84 for DBM. Prediction maps of FBM and DBM yield were obtained using calibrated models applied to CSMs, resulting in a feasible way to illustrate the spatial and temporal variability of biomass. Altogether the results of the study demonstrate that UAV RGB imaging can be a useful tool to predict and explore the spatial and temporal variability of black oat biomass, with potential use in precision farming.
Why it matches plant phenotyping methodsUAV RGB画像と写真測量により作物の草高・バイオマスを推定し、地上測定との比較、モデル検証、交差検証、予測マップ作成まで行っており、植物形質取得手法が研究の中心である。
abstractA recent approach introduced to model crop biomass consists in the use of RGB (red, green, blue) stereo images acquired from unmanned aerial vehicles (UAV) coupled with photogrammetric softwares to predict biomass through plant height (PHT) information.
Field data in forest inventories are increasingly obtained using proximal sensing technologies, often under fixed-point sampling. Under fixed-point sampling some trees are not detected due to instrument bias and occlusions, hence involving an underestimation of the number of trees per hectare (N). The aim here is to evaluate various approaches to correct tree occlusions and instrument bias estimates calculated with data from ForeStereo (proximal sensor based on stereoscopic hemispherical images) under a fixed-point sampling strategy. Distance-sampling and the new hemispherical photogrammetric correction (HPC), which combines image segmentation-based correction for instrument bias with a novel approach for estimating the proportion of shadowed sampling area in stereoscopic hemispherical images, best estimated N and basal area (BA). Distance-sampling slightly overestimated N (11% bias, 0.60 Pearson coefficient with the reference measures) and BA (4%, 0.82). HPC provided less biased N estimates (-6%, 0.61) but underestimated BA (-8%, 0.83). HPC most accurately retrieved the diameter distribution.
Why it matches plant phenotyping methodsForeStereoを用いた樹木の遮蔽・機器バイアス補正手法を開発・比較検証し、立木本数、胸高断面積、直径分布という樹木形質を推定しているため、方法が研究の中心である。
abstractThe aim here is to evaluate various approaches to correct tree occlusions and instrument bias estimates calculated with data from ForeStereo
Invasive plant species are major threats to biodiversity. They can be identified and monitored by means of high spatial resolution remote sensing imagery. This study aimed to test the potential of multiple very high-resolution (VHR) optical multispectral and stereo imageries (VHRSI) at spatial resolutions of 1.5 and 5 m to quantify the presence of the invasive lantana (Lantana camara L.) and predict its distribution at large spatial scale using medium-resolution fractional cover analysis. We created initial training data for fractional cover analysis by classifying smaller extent VHR data (SPOT-6 and RapidEye) along with three dimensional (3D) VHRSI derived digital surface model (DSM) datasets. We modelled the statistical relationship between fractional cover and spectral reflectance for a VHR subset of the study area located in the Himalayan region of India, and finally predicted the fractional cover of lantana based on the spectral reflectance of Landsat-8 imagery of a larger spatial extent. We classified SPOT-6 and RapidEye data and used the outputs as training data to create continuous field layers of Landsat-8 imagery. The area outside the overlapping region was predicted by fractional cover analysis due to the larger extent of Landsat-8 imagery compared with VHR datasets. Results showed clear discrimination of understory lantana from upperstory vegetation with 87.38% (for SPOT-6), and 85.27% (for RapidEye) overall accuracy due to the presence of additional VHRSI derived DSM information. Independent validation for lantana fractional cover estimated root-mean-square errors (RMSE) of 11.8% (for RapidEye) and 7.22% (for SPOT-6), and R2 values of 0.85 and 0.92 for RapidEye (5 m) and SPOT-6 (1.5 m), respectively. Results suggested an increase in predictive accuracy of lantana within forest areas along with increase in the spatial resolution for the same Landsat-8 imagery. The variance explained at 1.5 m spatial resolution to predict lantana was 64.37%, whereas it decreased by up to 37.96% in the case of 5 m spatial resolution data. This study revealed the high potential of combining small extent VHR and VHRSI- derived 3D optical data with larger extent, freely available satellite data for identification and mapping of invasive species in mountainous forests and remote regions.
Why it matches plant phenotyping methods高解像度ステレオ・マルチスペクトル画像を用いて侵入植物のfractional coverを推定し、精度を独立検証しているため、植物状態の取得・推定法が中心です。
abstractThis study aimed to test the potential of multiple very high-resolution (VHR) optical multispectral and stereo imageries (VHRSI) at spatial resolutions of 1.5 and 5 m to quantify the presence of the invasive lantana (Lantana camara L.)
Field / plotLiDAR / point cloudStereoLeafWhole plant / canopy / plot / fieldObject detection2D/3D reconstructionGrowth / development / phenology
Stereo matching can provide complete and dense three-dimensional reconstruction to study plant growth. Recently, high-quality stereo matching results were achieved combining Semi-Global Matching (SGM) with deep learning. However, due to a lack of suitable training data, this technique is not readily applicable for plant reconstruction. We propose a self-supervised Matching Cost with a Convolutional Neural Network (MC-CNN) scheme to calculate matching cost and test it for plant reconstruction. The MC-CNN network is retrained using the initial matching results obtained from the standard MC-CNN weights. For the experiment, closerange photogrammetric imagery of an in-house plant is used. The results show that the performance of self-supervised MC-CNN is superior to the Census algorithm and comparable to MC-CNN trained by a Light Detection and Ranging point cloud. Another experiment is performed using stereo imagery of a field beech tree. The proposed self-training strategy is tested and has proved capable of identifying the drought condition of trees from the reconstructed leaves.
Why it matches plant phenotyping methods植物のステレオ画像から3次元再構成を行う自己教師ありCNN手法を開発・評価しており、再構成に基づく生育・乾燥状態の推定が中心的な方法論的貢献である。
abstractWe propose a self-supervised Matching Cost with a Convolutional Neural Network (MC-CNN) scheme to calculate matching cost and test it for plant reconstruction.
Abstract The in-field measurement of phenotypes or traits of wheat such as ear size is important data for use in the development of newer wheat varieties. The data is currently gathered manually from hundreds of test plots by random sampling of the what within each plot. To improve the data quality and data collection speed, we investigate and compare the use three different 3D imaging technologies: multistereo imaging, time-of-flight and structured light laser scanning to produce point clouds of a wheat plant in-situ. Measurements of the wheat plant’s ear is made from the generated point clouds.
Why it matches plant phenotyping methods小麦の穂サイズという植物形質を対象に、3種類の3D画像技術を比較し、点群から測定する手法が研究の中心であるため。
abstractwe investigate and compare the use three different 3D imaging technologies: multistereo imaging, time-of-flight and structured light laser scanning to produce point clouds of a wheat plant in-situ.
Stem diameter is an important parameter in the process of plant growth which can indicate the growth state and moisture content of the plant, its automatic detection is necessary. Traditional devices have many drawbacks that limit their practical uses in general case. To solve those problems, a stem diameter inspection spherical robot was developed in this study. The particular mechanism of the robot has turned out to be suitable for performing monitoring tasks in greenhouse mainly due to its spherical shape, small size, low weight and traction system that do not produce soil compacting or erosion. The mechanical structure and hardware architecture of the spherical robot were described, the algorithm based on binocular stereo vision was developed to measure the stem diameter of the plant. The effectiveness of the prototype robot was confirmed by field experiments in a tomato greenhouse. The results showed that the machine measurement data was linearly correlated with the manual measurement data with R2 of 0.9503. There was no significant difference for each attribute between machine measurement data and manual measurement data (sig > 0.05). The results showed that this method was feasible for nondestructive testing of the stem diameter of greenhouse plants. Keywords: stem diameter inspection, spherical robot, binocular stereo vision, Census transform DOI: 10.25165/j.ijabe.20191202.4163 Citation: Quan L Z, Chen C, Li Y J, Qiao Y J, Xi D J, Zhang T Y, et al. Design and test of stem diameter inspection spherical robot. Int J Agric & Biol Eng, 2019; 12(2): 141–151.
Why it matches plant phenotyping methods植物の茎径という形態形質を、ステレオビジョン搭載ロボットで自動計測する手法の開発と検証が研究の中心であるため。
abstractTo solve those problems, a stem diameter inspection spherical robot was developed in this study.
Illumination in the natural environment is uncontrollable, and the field background is complex and changeable which all leads to the poor quality of broccoli seedling images. The colors of weeds and broccoli seedlings are close, especially under weedy conditions. The factors above have a large influence on the stability, velocity and accuracy of broccoli seedling recognition based on traditional 2D image processing technologies. The broccoli seedlings are higher than the soil background and weeds in height due to the growth advantage of transplanted crops. A method of broccoli seedling recognition in natural environments based on Binocular Stereo Vision and a Gaussian Mixture Model is proposed in this paper. Firstly, binocular images of broccoli seedlings were obtained by an integrated, portable and low-cost binocular camera. Then left and right images were rectified, and a disparity map of the rectified images was obtained by the Semi-Global Matching (SGM) algorithm. The original 3D dense point cloud was reconstructed using the disparity map and left camera internal parameters. To reduce the operation time, a non-uniform grid sample method was used for the sparse point cloud. After that, the Gaussian Mixture Model (GMM) cluster was exploited and the broccoli seedling points were recognized from the sparse point cloud. An outlier filtering algorithm based on k-nearest neighbors (KNN) was applied to remove the discrete points along with the recognized broccoli seedling points. Finally, an ideal point cloud of broccoli seedlings can be obtained, and the broccoli seedlings recognized. The experimental results show that the Semi-Global Matching (SGM) algorithm can meet the matching requirements of broccoli images in the natural environment, and the average operation time of SGM is 138 ms. The SGM algorithm is superior to the Sum of Absolute Differences (SAD) algorithm and Sum of Squared Differences (SSD) algorithms. The recognition results of Gaussian Mixture Model (GMM) outperforms K-means and Fuzzy c-means with the average running time of 51 ms. To process a pair of images with the resolution of 640×480, the total running time of the proposed method is 578 ms, and the correct recognition rate is 97.98% of 247 pairs of images. The average value of sensitivity is 85.91%. The average percentage of the theoretical envelope box volume to the measured envelope box volume is 95.66%. The method can provide a low-cost, real-time and high-accuracy solution for crop recognition in natural environment.
Why it matches plant phenotyping methods双目立体视觉与GMMによるブロッコリー苗の認識・点群抽出法を開発し、複数手法との性能比較と精度評価を行っており、植物表現型取得が中心である。
abstractThe experimental results show that the Semi-Global Matching (SGM) algorithm can meet the matching requirements of broccoli images in the natural environment
Forest canopy height plays an important role in forest management and ecosystem modeling. There are a variety of techniques employed to map forest height using remote sensing data but it is still necessary to explore the use of new data and methods. In this study, we demonstrate an approach for mapping canopy heights of poplar plantations in plain areas through a combination of stereo and multispectral data from China’s latest civilian stereo mapping satellite ZY3-02. First, a digital surface model (DSM) was extracted using photogrammetry methods. Then, canopy samples and ground samples were selected through manual interpretation. Canopy height samples were obtained by calculating the DSM elevation differences between the canopy samples and ground samples. A regression model was used to correlate the reflectance of a ZY3-02 multispectral image with the canopy height samples, in which the red band and green band reflectance were selected as predictors. Finally, the model was extrapolated to the entire study area and a wall-to-wall forest canopy height map was obtained. The validation of the predicted canopy height map reported a coefficient of determination (R2) of 0.72 and a root mean square error (RMSE) of 1.58 m. This study demonstrates the capacity of ZY3-02 data for mapping the canopy height of pure plantations in plain areas.
Why it matches plant phenotyping methods衛星ステレオ・マルチスペクトルデータからポプラ植林地の樹冠高を推定し、予測結果を検証しており、植物形質の取得・推定手法が中心である。
abstractwe demonstrate an approach for mapping canopy heights of poplar plantations in plain areas through a combination of stereo and multispectral data
Recent advances in high throughput phenotyping have made it possible to collect large datasets following plant growth and development over time, and those in machine learning have made inferring phenotypic plant traits from such datasets possible. However, there remains a dirth of datasets following plant growth under stress conditions along with methods for inferring them using only remotely sensed data, especially under a combination of multiple stress factors such as drought, weeds and nutrient deficiency. Such stress factors and their combinations are commonly encountered during crop production and being able to accurately detect and treat such stress conditions in an automated and timely manner can provide a major boost to farm yields with minimal resource input. We present a generic framework for remote plant stress phenotyping that consists of a dataset with spatio-temporal-spectral data following sugarbeet crop growth under optimal, drought, low and surplus nitrogen fertilization, and weed stress conditions, along with a machine learning based methodology for systematically inferring these stress conditions from the remotely measured data. The dataset contains biweekly color images, infra-red stereo image pairs and hyperspectral camera images along with applied treatment parameters and environmental factors like temperature and humidity, collected over two months. We present a plant agnostic methodology for deriving plant trait indicators such as canopy cover, height, hyperspectral reflectance and vegetation indices along with a spectral 3D reconstruction of the plants from the raw data to serve as a benchmark. Additionally, we provide fresh and dry weight measurements for both the above (canopy) and below (beet) ground biomass at the end of the growing period to serve as indicators of expected yield. We further describe a data driven, machine learning based method to infer water, Nitrogen and weed stress using the derived plant trait indicators. We use the plant trait indicators to evaluate 8 different classification approaches from which the best classifier achieved a mean cross validation accuracy of $$\approx$$ 93, 76 and 83% for drought, nitrogen and weed stress severity classification respectively. We also show that our multi-modal approach significantly improves classifier performance over using any single modality. The presented framework and dataset can serve as a valuable reference for creating and comparing processing pipelines which extract plant trait indicators and infer prevalent stress factors from remote sensing data under a variety of environments and cropping conditions. These techniques can then be deployed on farm machinery or robots enabling automated, precise and timely corrective interventions for maximising yield.
Why it matches plant phenotyping methods植物ストレス表現型を推定するデータセット、マルチモーダル画像・分光計測、形質抽出、機械学習推定を一体化した汎用フレームワークであり、表現型取得・解析手法が研究の中心である。
abstractWe present a generic framework for remote plant stress phenotyping that consists of a dataset with spatio-temporal-spectral data
Reproduction assets foundThe paper releases its own plant stress phenotyping dataset (RGB, stereo IR, hyperspectral imagery, reference measurements) and accompanying pre-processing/classification software, both publicly available at author-provided URLs.Dataset · publicThe images and reference data that support the findings of this study are available from ETH Zürich ASL Datasets Repository, “ https://projects.asl.ethz.ch/datasets/doku.php?id=2018plantstressphenotyping ”.Open asset ↗ETH Zürich ASL Datasets Repository · 2018plantstressphenotypinglines:367-481Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Understanding of the view (scene) and 3D object recognition is one of the magnificent challenges in computer vision. A wide variety of techniques and goals, such as structure from motion, optical flow, stereo, edge detection, and segmentation, can be viewed as subtasks in scene understanding and object recognition. Many methods can be applied by previous investigators. On the contrary, this research is focused on highlevel representation for scenery and objects, especially physical representation recognize 3D view of the underlying image. This study aims to answer the following questions: • How to relate 2D image with a 3D scene, and how we can take advantage of the relationship perspective? • How does the physical scene space can be modeled, and how to estimate the space scene of an image? • How to represent and recognize objects in a way that is robust to changes in viewpoint? • How can use the knowledge and perspective of the scene to improve the recognition space, or vice versa? In this study, carried out the stages of development of the plant recognition system based on 3D stereo images leaves, namely: image enhancement and segmentation, stereo correspondence, disparity map calculation and depth maps, feature extraction using Gray Level Coocurence Matrix, and classification using Euclidian distance. The results obtained in this study indicate that the recognition accuracy of the plant with the highest 3D image of the leaf is 83.3% to recognize 3 varieties of plants. While to recognize 9 varieties of plants obtained low accuracy. The low accuracy is due to the quality of the disparity and depth maps are possible for further research.
Why it matches plant phenotyping methods3D葉画像のセグメンテーション、ステレオ対応、深度推定、特徴抽出、分類による植物認識システムの開発が研究の中心であり、植物画像から形態的情報を抽出する方法論的貢献が明確です。
abstractthis research is focused on highlevel representation for scenery and objects, especially physical representation recognize 3D view of the underlying image.
Forest canopy height is an important parameter for studying biodiversity and the carbon cycle. A variety of techniques for mapping forest height using remote sensing data have been successfully developed in recent years. However, the demands for forest height mapping in practical applications are often not met, due to the lack of corresponding remote sensing data. In such cases, it would be useful to exploit the latest, cheaper datasets and combine them with free datasets for the mapping of forest canopy height. In this study, we proposed a method that combined ZiYuan-3 (ZY-3) stereo images, Shuttle Radar Topography Mission global 1 arc second data (SRTMGL1), and Landsat 8 Operational Land Imager (OLI) surface reflectance data. The method consisted of three procedures: First, we extracted a digital surface model (DSM) from the ZY-3, using photogrammetry methods and subtracted the SRTMGL1 to obtain a crude canopy height model (CHM). Second, we refined the crude CHM and correlated it with the topographically corrected Landsat 8 surface reflectance data, the vegetation indices, and the forest types through a Random Forest model. Third, we extrapolated the model to the entire study area covered by the Landsat data, and obtained a wall-to-wall forest canopy height product with 30 m × 30 m spatial resolution. The performance of the model was evaluated by the Random Forest’s out-of-bag estimation, which yielded a coefficient of determination (R2) of 0.53 and a root mean square error (RMSE) of 3.28 m. We validated the predicted forest canopy height using the mean forest height measured in the field survey plots. The validation result showed an R2 of 0.62 and a RMSE of 2.64 m.
Why it matches plant phenotyping methods森林キャノピー高という植物群落の形態形質を、ステレオ画像・衛星データ・機械学習で推定する方法を開発し、野外調査プロットで検証しているため、方法開発・検証が中心である。
abstractIn this study, we proposed a method that combined ZiYuan-3 (ZY-3) stereo images, Shuttle Radar Topography Mission global 1 arc second data (SRTMGL1), and Landsat 8 Operational Land Imager (OLI) surface reflectance data.
Stem diameter is an important parameter in the process of plant growth which can indicate the growth state and moisture content of the plant, its automatic detection is necessary. Traditional devices have many drawbacks that limit their practical uses in general case. To solve those problems, a stem diameter inspection spherical robot was developed in this study. The particular mechanism of the robot has turned out to be suitable for performing monitoring tasks in greenhouse mainly due to its spherical shape, small size, low weight and traction system that do not produce soil compacting or erosion. The mechanical structure and hardware architecture of the spherical robot were described, the algorithm based on binocular stereo vision was developed to measure the stem diameter of the plant. The effectiveness of the prototype robot was confirmed by field experiments in a tomato greenhouse. The results showed that the machine measurement data was linearly correlated with the manual measurement data with R2 of 0.9503. There was no significant difference for each attribute between machine measurement data and manual measurement data (sig > 0.05). The results showed that this method was feasible for nondestructive testing of the stem diameter of greenhouse plants. Keywords: stem diameter inspection, spherical robot, binocular stereo vision, Census transform DOI: 10.25165/j.ijabe.20191202.4163 Citation: Quan L Z, Chen C, Li Y J, Qiao Y J, Xi D J, Zhang T Y, et al. Design and test of stem diameter inspection spherical robot. Int J Agric & Biol Eng, 2019; 12(2): 141–151.
Why it matches plant phenotyping methods植物の茎径を測定する球形ロボットと両眼ステレオビジョン手法を開発し、トマト温室で手動測定と比較検証しており、表現型取得法が研究の中心である。
abstracta stem diameter inspection spherical robot was developed in this study
MaizeField / plotLiDAR / point cloudStereoWhole plant / canopy / plot / field
A rapidly increasing world population and changing climate means plant scientists will need to be able to efficiently develop crop varieties to feed the world. Although the technology for sequencing the genomes of plants has advanced, the technology for characterizing the physical traits of plants has remained relatively static. This gap in technology has become known as the “Phenotyping Bottleneck.” To close this gap, researchers are working to develop robotic systems that can efficiently recognize various physical traits of plants. The goal of this research was to investigate how the design requirements for a vehicle that interacts with a biological system are translated into a working mechanical system. The design requirements include the ability to traverse and image crops in 30-inch wide space between crop rows. This thesis reports the concepts, design decisions, and manufacturing process around the construction of such as a phenotyping robot namely Phenobot 3.0, which stands for the 3rd generation of our phenotyping robot series. Phenobot 3.0 is optimized for phenotyping maize plants in the field but can be adapted for phenotyping other crops. Specifically, Phenobot 3.0 is designed to be narrow to fit between the rows but also tall for sensor placement so that it can gather data from the emergence to the full height of maize plants. To achieve the needed stability of the sensors (LiDAR, stereo cameras), the robot employs a self-leveling mast to cope with the uneven terrain while ensuring proper sensor to plant placement. Unlike many other field-based phenotyping robots, Phenobot 3.0 employs a 4-wheel-drive articulated drivetrain that has differentials on each pair of wheels to ensure maximum steering efficiency and prolonged operational time in the field. Phenobot 3.0 will be a member of PhenoNet, a network of five robots for maize plant phenotyping under different growing environments, a project funded by the National Science Foundation. The scale of this project implies that each design requirement must be carefully evaluated so that the manufacturing process can be easily scaled up to produce multiple units. The results from the preliminary tests of the Phenobot 3.0 prototype have demonstrated satisfactory functionalities and expected performance metrics.
Why it matches plant phenotyping methods植物フェノタイピング用ロボットプラットフォームの設計・製作と予備性能評価が中心であり、LiDAR・ステレオカメラによる作物形質取得を目的とする方法開発研究である。
titleA robotic proximal sensing platform for in-field high-throughput crop phenotyping
Monitoring the growth of trees, plants, and crops is an important work in precision agriculture. Tree canopy geometric characteristics are related to tree growth and productivity. Computer vision techniques can be used to map tree canopy volume, which is useful for planning management. This study investigates the potential of using stereo vision system for obtaining tree disparity map for the analysis of geometric attributes. Experiments were conducted to examine the effects of the canopy shapes and foliage density on the performance of stereo vision system in disparity map computation. Two canopy shapes (conic and ellipse) and three foliage density levels were evaluated using two algorithms (algorithm based on local methods (ABLM) and algorithm based on global methods (ABGM)) to match pair stereo images. The well-known Middlebury dataset was considered and the performance of algorithms was evaluated on that. The results showed that the ABGM studied algorithm succeeded in computation disparity map on both Middlebury and trees images because it aggregated matching cost from several directions. The tree canopy shapes and foliage density did not affect the results of algorithms. Also, noises were numerous and more dispersed in ABLM matching algorithm. It was observed that maximum disparity limits search space. Best results were obtained in real value. For smaller value of maximum disparity than that of real value, disparity map had missed disparities. Window size was affected on maps and noise level and best results were obtained when this parameter was set to 15. Smaller value obtained more detailed map but with noise and un-smooth. The algorithm was robust for real trees in natural conditions.
Why it matches plant phenotyping methodsステレオ画像から樹冠の幾何学的特徴を抽出する視差マップ計算法を開発・評価しており、植物形態計測が研究の中心である。
abstractThis study investigates the potential of using stereo vision system for obtaining tree disparity map for the analysis of geometric attributes.
Abstract Sorghum ( Sorghum bicolor ) is known as a major feedstock for biofuel production. To improve its biomass yield through genetic research, manually measuring yield component traits (e.g. plant height, stem diameter, leaf angle, leaf area, leaf number, and panicle size) in the field is the current best practice. However, such laborious and time‐consuming tasks have become a bottleneck limiting experiment scale and data acquisition frequency. This paper presents a high‐throughput field‐based robotic phenotyping system which performed side‐view stereo imaging for dense sorghum plants with a wide range of plant heights throughout the growing season. Our study demonstrated the suitability of stereo vision for field‐based three‐dimensional plant phenotyping when recent advances in stereo matching algorithms were incorporated. A robust data processing pipeline was developed to quantify the variations or morphological traits in plant architecture, which included plot‐based plant height, plot‐based plant width, convex hull volume, plant surface area, and stem diameter (semiautomated). These image‐derived measurements were highly repeatable and showed high correlations with the in‐field manual measurements. Meanwhile, manually collecting the same traits required a large amount of manpower and time compared to the robotic system. The results demonstrated that the proposed system could be a promising tool for large‐scale field‐based high‐throughput plant phenotyping of bioenergy crops.
Why it matches plant phenotyping methodsステレオ画像を用いた圃場ロボット表現型解析システムと、植物形態形質を抽出する処理パイプラインの開発・検証が研究の中心であるため。
abstractThis paper presents a high‐throughput field‐based robotic phenotyping system which performed side‐view stereo imaging for dense sorghum plants with a wide range of plant heights throughout the growing season.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 10 Sept 2026
Leaves account for the largest proportion of all organ areas for most kinds of plants, and are comprise the main part of the photosynthetically active material in a plant. Observation of individual leaves can help to recognize their growth status and measure complex phenotypic traits. Current image-based leaf segmentation methods have problems due to highly restricted species and vulnerability toward canopy occlusion. In this work, we propose an individual leaf segmentation approach for dense plant point clouds using facet over-segmentation and facet region growing. The approach can be divided into three steps: (1) point cloud pre-processing, (2) facet over-segmentation, and (3) facet region growing for individual leaf segmentation. The experimental results show that the proposed method is effective and efficient in segmenting individual leaves from 3D point clouds of greenhouse ornamentals such as Epipremnum aureum, Monstera deliciosa, and Calathea makoyana, and the average precision and recall are both above 90%. The results also reveal the wide applicability of the proposed methodology for point clouds scanned from different kinds of 3D imaging systems, such as stereo vision and Kinect v2. Moreover, our method is potentially applicable in a broad range of applications that aim at segmenting regular surfaces and objects from a point cloud.
Why it matches plant phenotyping methods個葉を3D点群から分割する画像解析手法を開発し、複数植物種・撮像システムで精度検証しており、植物表現型抽出が研究の中心である。
abstractIn this work, we propose an individual leaf segmentation approach for dense plant point clouds using facet over-segmentation and facet region growing.
StrawberryStereoThermalLeafVisualization / data managementStress response / tolerance
Freezing in plants can be monitored using infrared (IR) thermography, because when water freezes, it gives off heat. However, problems with color contrast make 2-dimensions (2D) infrared images somewhat difficult to interpret. Viewing an IR image or the video of plants freezing in 3 dimensions (3D) would allow a more accurate identification of sites for ice nucleation as well as the progression of freezing. In this paper, we demonstrate a relatively simple means to produce a 3D infrared video of a strawberry plant freezing. Strawberry is an economically important crop that is subjected to unexpected spring freeze events in many areas of the world. An accurate understanding of the freezing in strawberry will provide both breeders and growers with more economical ways to prevent any damage to plants during freezing conditions. The technique involves a positioning of two IR cameras at slightly different angles to film the strawberry freezing. The two video streams will be precisely synchronized using a screen capture software that records both cameras simultaneously. The recordings will then be imported into the imaging software and processed using an anaglyph technique. Using red-blue glasses, the 3D video will make it easier to determine the precise site of ice nucleation on leaf surfaces.
Why it matches plant phenotyping methods植物の凍結部位・進行を可視化する3D赤外線動画の取得・処理手法を開発・実証しており、植物状態の計測法が中心である。
abstractIn this paper, we demonstrate a relatively simple means to produce a 3D infrared video of a strawberry plant freezing.
This article describes the design and field evaluation of a low-cost, high-throughput phenotyping robot for energy sorghum for use in biofuel production. High-throughput phenotyping approaches have been used in isolated growth chambers or greenhouses, but there is a growing need for field-based, precision agriculture techniques to measure large quantities of plants at high spatial and temporal resolutions throughout a growing season. A low-cost, tracked mobile robot was developed to collect phenotypic data for individual plants and tested on two separate energy sorghum fields in Central Illinois during summer 2016. Stereo imaging techniques determined plant height, and a depth sensor measured stem width near the base of the plant. A data capture rate of 0.4 ha, bi-weekly, was demonstrated for platform robustness consistent with various environmental conditions and crop yield modeling needs, and formative human–robot interaction observations were made during the field trials to address usability. This work is of interest to researchers and practitioners advancing the field of plant breeding because it demonstrates a new phenotyping platform that can measure individual plant architecture traits accurately (absolute measurement error at 15% for plant height and 13% for stem width) over large areas at a sub-daily frequency; furthermore, the design of this platform can be extended for phenotyping applications in maize or other agricultural row crops.
Why it matches plant phenotyping methods個体の草高・茎幅を取得するロボット型ハイスループット表現型解析プラットフォームの設計と圃場評価が研究の中心である。
abstractThis article describes the design and field evaluation of a low-cost, high-throughput phenotyping robot for energy sorghum for use in biofuel production.
In this paper we report on an automated procedure to capture and characterize the detailed structure of a crop canopy by means of stereo imaging. We focus attention specifically on the detailed characteristic of canopy height distribution-canopy shoot area as a function of height-which can provide an elaborate picture of canopy growth and health under a given set of conditions. We apply the method to a wheat field trial involving ten Australian wheat varieties that were subjected to two different fertilizer treatments. A novel camera self-calibration approach is proposed which allows the determination of quantitative plant canopy height data (as well as other valuable phenotypic information) by stereo matching. Utilizing the canopy height distribution to provide a measure of canopy height, the results compare favourably with manual measurements of canopy height (resulting in an R2 value of 0.92), and are indeed shown to be more consistent. By comparing canopy height distributions of different varieties and different treatments, the methodology shows that different varieties subjected to the same treatment, and the same variety subjected to different treatments can respond in much more distinctive and quantifiable ways within their respective canopies than can be captured by a simple trait measure such as overall canopy height.
Why it matches plant phenotyping methodsステレオ画像による作物キャノピー構造・高さ分布の自動取得法を開発し、手動測定と比較検証しているため、植物表現型取得が中心である。
abstractwe report on an automated procedure to capture and characterize the detailed structure of a crop canopy by means of stereo imaging
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe sub-dataset for land-based crop phenotyping using stereo images can be downloaded from https://sourceforge.net/projects/land-based-crop-phenotyping/ , which includes images of all 60 plots and their depth maps on 23 Sept 2016 and 11 Oct 2016. These data are sufficient to validate the results presented in this paper.Open asset ↗sourceforge · land-based-crop-phenotypinglines:135-142Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 10 Sept 2026
Published30 Apr 2018The International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesCited by 2 · OpenAlex ↗
Abstract. Hyperspectral and three-dimensional measurement can obtain the intrinsic physicochemical properties and external geometrical characteristics of objects, respectively. Currently, a variety of sensors are integrated into a system to collect spectral and morphological information in agriculture. However, previous experiments were usually performed with several commercial devices on a single platform. Inadequate registration and synchronization among instruments often resulted in mismatch between spectral and 3D information of the same target. And narrow field of view (FOV) extends the working hours in farms. Therefore, we propose a high throughput prototype that combines stereo vision and grating dispersion to simultaneously acquire hyperspectral and 3D information.
Why it matches plant phenotyping methods植物の高さを含む3D形態とハイパースペクトル情報を同時取得する高スループット計測システムの開発が中心であり、植物フェノタイピング手法に該当する。
titleHIGH THROUGHPUT SYSTEM FOR PLANT HEIGHT AND HYPERSPECTRAL MEASUREMENT
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 10 Sept 2026
Hyperspectral and three-dimensional measurements can obtain the intrinsic physicochemical properties and external geometrical characteristics of objects, respectively. The combination of these two kinds of data can provide new insights into objects, which has gained attention in the fields of agricultural management, plant phenotyping, cultural heritage conservation, and food production. Currently, a variety of sensors are integrated into a system to collect spectral and morphological information in agriculture. However, previous experiments were usually performed with several commercial devices on a single platform. Inadequate registration and synchronization among instruments often resulted in mismatch between spectral and 3D information of the same target. In addition, using slit-based spectrometers and point-based 3D sensors extends the working hours in farms due to the narrow field of view (FOV). Therefore, we propose a high throughput prototype that combines stereo vision and grating dispersion to simultaneously acquire hyperspectral and 3D information. Furthermore, fiber-reformatting imaging spectrometry (FRIS) is adopted to acquire the hyperspectral images. Test experiments are conducted for the verification of the system accuracy, and vegetation measurements are carried out to demonstrate its feasibility. The proposed system is an improvement in multiple data acquisition and has the potential to improve plant phenotyping.
Why it matches plant phenotyping methods植物フェノタイピング向けのハイパースペクトル・3D同時計測システムを開発し、精度検証と植生計測で実証しているため、計測手法が中心です。
abstractTherefore, we propose a high throughput prototype that combines stereo vision and grating dispersion to simultaneously acquire hyperspectral and 3D information.
Disturbances caused by the European spruce bark beetle (Ips typographus L.) infestations are amongst the main drivers of forest ecosystem dynamics in stands dominated by Norway spruce (Picea abies [L.] Karst.). Monitoring the post-disturbance stand development including establishment of the new tree cohorts (regeneration) is of particular importance, and is conventionally done by time-intensive field surveys. Efficiency of techniques such as airborne light detection and ranging (LiDAR) or stereo photogrammetry is constrained due to their quality or costs in small-scaled and substantially heterogeneous post-disturbed areas. Small, multi-rotor unmanned aerial vehicles (UAVs) offer alternatives via their lower cost, temporal flexibility and high spatial resolution. We investigated the Digital Surface Models (DSM) derived from the UAV for inventories in post-disturbed sites of the Bavarian Forest National Park, Germany. We compared the numbers and structural attributes of detected living trees and snags from UAV data with standard stereo aerial photogrammetry using conventional field survey as a reference. Moreover, we processed the UAV data both by manual and automated tree recognitions. The results differentiated for individual tree classes (Living Spruce/Standing Deadwood and Individual/Grouped) showed varying performance with best results achieved for Standing Deadwood of moderate height. The UAV products were superior to aerial photography for the height retrieval: UAV-based data showed in average the root mean square error (RMSE) = 1.56 m, coefficient of determination R2 = 0.74 and bias = −0.73 m, compared to the aerial photogrammetry RMSE = 2.71 m, R2 = 0.17 and bias = −1.27 m. In particular, the heights of tall snags were more biased. Furthermore, the UAV data provided good results in crown diameter determination (RMSE = 0.13 m, R2 = 0.87, bias = 0.05 m). The automated recognition method was associated with qualitative and quantitative drawbacks compared to the manual method. Detection rates for trees and regeneration growing individually (60.7% and 39.1% for manual and automated method, respectively) were higher compared to regeneration in groups (28.6% and 17.8%). To conclude, the UAV-based inventory has clear advantages over aerial photogrammetry, especially for inventory of sites dominated by larger individual trees with sparse understorey. However, it cannot fully replace the field survey in post-disturbed sites with dense regeneration, where the performance can be augmented by combining UAVs with reduced fieldwork in different stand structural classes.
Why it matches plant phenotyping methodsUAV画像から個体樹木・枯死木の検出、樹高、樹冠直径などの植物形質を抽出し、航空写真および現地調査と定量比較しているため、植物フェノタイピング手法の検証・応用が中心である。
abstractWe compared the numbers and structural attributes of detected living trees and snags from UAV data with standard stereo aerial photogrammetry using conventional field survey as a reference.
Non-destructive monitoring of crop development is of key interest for agronomy and crop breeding. Crop Surface Models (CSMs) representing the absolute height of the plant canopy are a tool for this. In this study, fresh and dry barley biomass per plot are estimated from CSM-derived plot-wise plant heights. The CSMs are generated in a semi-automated manner using Structure-from-Motion (SfM)/Multi-View-Stereo (MVS) software from oblique stereo RGB images. The images were acquired automatedly from consumer grade smart cameras mounted at an elevated position on a lifting hoist. Fresh and dry biomass were measured destructively at four dates each in 2014 and 2015. We used exponential and simple linear regression based on different calibration/validation splits. Coefficients of determination R 2 between 0.55 and 0.79 and root mean square errors (RMSE) between 97 and 234 g/m2 are reached for the validation of predicted vs. observed dry biomass, while Willmott’s refined index of model performance d r ranges between 0.59 and 0.77. For fresh biomass, R 2 values between 0.34 and 0.61 are reached, with root mean square errors (RMSEs) between 312 and 785 g/m2 and d r between 0.39 and 0.66. We therefore established the possibility of using this novel low-cost system to estimate barley dry biomass over time.
Why it matches plant phenotyping methods低コストのRGB画像とSfM/MVSによるCSMから作物バイオマスを推定する取得・解析手法を開発し、検証しており、表現型計測が研究の中心である。
abstractCrop Surface Models (CSMs) representing the absolute height of the plant canopy are a tool for this.
Leaf length is one of important parameters for crop growth estimation. In order to accurately obtain the plant leaf length, this paper proposes a slope linear hypothesis in which the leaf length could be measured in 3-D space with a right-angle model. A binocular stereo vision system was applied with RGB and depth image output. The projection of leaf could be obtained in RGB and depth image. The depth camera is used to obtain one right-angle side to correct the measurement result in RGB image. Four methods were present to compare the extract accuracy in the images. First, RGB images without leaf segmentation were used to extract leaf length ( L 1 → ) on the horizontal projection plane. Second, leaf length ( L 2 → ) were calculated with the right-angle model correction by depth image based on ( L 1 → ). Third, the preprocessing of RGB images was conducted with color image segmentation and morphological operation, then the leaf length ( L 3 → ) was extracted. Fourth, the correction leaf length ( L 4 → ) were obtained by depth image correction to L 3 → . Four prediction models were established to analyze L 1 → , L 2 → , L 3 → , and L 4 → results. It is indicated that the prediction model based on the L 2 → measurement has better performance, in which R c 2 is 0.7178 and R v 2 is 0.833.
Why it matches plant phenotyping methods深度カメラとステレオビジョンを用いたジャガイモ葉長の画像計測手法を提案・比較・評価しており、表現型取得が研究の中心です。
abstractIn order to accurately obtain the plant leaf length, this paper proposes a slope linear hypothesis in which the leaf length could be measured in 3-D space with a right-angle model.
A novel, non-destructive method for the biomass estimation of biological samples on culture dishes was developed. To achieve this, a photogrammetric system, which consists of a digital single-lens reflex camera (DSLR), an illuminated platform where the culture dishes are positioned and an Arduino board which controls the capturing process, was constructed. The camera was mounted on a holder which set the camera at different title angles and the platform rotated, to capture images from different directions. A software, based on stereo photogrammetry, was developed for the three-dimensional (3D) reconstruction of the samples. The proof-of-concept was demonstrated in a series of experiments with plant tissue cultures and specifically with calli cultures of Salvia fruticosa and Ocimum basilicum. For a period of 14 days images of these cultures were acquired and 3D-reconstructions and volumetric data were obtained. The volumetric data correlated well with the experimental measurements and made the calculation of the specific growth rate, µ max , possible. The µ max value for S. fruticosa samples was 0.14 day -1 and for O. basilicum 0.16 day -1 . The developed method demonstrated the high potential of this photogrammetric approach in the biological sciences.
Why it matches plant phenotyping methods植物組織培養のバイオマスを、ステレオフォトグラメトリによる3D再構成と体積推定で非破壊測定する手法の開発・実証が中心であり、植物形質の取得方法に該当する。
abstractA novel, non-destructive method for the biomass estimation of biological samples on culture dishes was developed.
Tree height estimation is fundamental in forestry inventory especially in the computation of biomass. Traditional methods for tree height estimation are not cost effective because of time, manpower and resources involved. Multiple return LiDAR capabilities offer convenient solutions for height estimations though at equally increased costs. This study seeks to provide an assessment of the accuracy of Unmanned Aerial System (UAS) stereo imagery in establishing tree distribution and canopy heights in open forests as an inexpensive alternative. To achieve this, we: generate accurate 3 dimensional surface and bare earth models from UAS data and using these products; establish tree distribution and estimate canopy heights using data filters; and validate the results using ground methods. A Mavinci Sirius fixed wing Unmanned Aerial Vehicle (UAV) fitted with a 16 Megapixel camera and flying at an average height of 371 m Above Ground Level (AGL) was used to image approximately 2 km2 capturing 380 images per flight. An image overlap of up to 85% was sufficient for stereo generation at a Ground Sample Distance (GSD) of 10 cm for a flight period of 40 minutes. The stereo imagery captured were processed into orthomosaics and photogrammetric point clouds with an average point density of 23 points per square meters using Structure from Motion (SfM) techniques. Point cloud segmentation revealed tree distribution patterns in the Ifakara area, with the Near Infrared band proving useful in filtering out trees from non-vegetated areas. From the tree height estimations and with validation information from 46 sample trees yielded a correlation coefficient, R2=75%. The study highlights a simplified and cost-effective approach for generation of accurate three dimension (3D) models from stereo UAS data. With a survey grade GPS/IMU/INS for direct-on-board geo-referencing, limited controls were required which reduces the cost of the project. With the ease of varying the size of imagery overlap and flying height, imagery with improved radiometry can be obtained hence improving the determination of tree distribution, and with multi-view image matching algorithms processing of UAS imagery is made accurate and inexpensive.
Why it matches plant phenotyping methodsUASステレオ画像とSfMによる樹木分布・樹冠高推定を開発・精度検証しており、植物形質取得法が研究の中心である。
abstractThis study seeks to provide an assessment of the accuracy of Unmanned Aerial System (UAS) stereo imagery in establishing tree distribution and canopy heights in open forests as an inexpensive alternative.
Introduction Great areas of the orchards in the world are dedicated to cultivation of the grapevine. Normally grape vineyards are pruned twice a year. Among the operations of grape production, winter pruning of the bushes is the only operation that still has not been fully mechanized while it is known as the most laborious jobs in the farm. Some of the grape producing countries use various mechanical machines to prune the grapevines, but in most cases, these machines do not have a good performance. Therefore intelligent pruning machine seems to be necessary in this regard and this intelligent pruning machines can reduce the labor required to prune the vineyards. It this study in was attempted to develop an algorithm that uses image processing techniques to identify which parts of the grapevine should be cut. Stereo vision technique was used to obtain three dimensional images from the bare bushes whose leaves were fallen in autumn. Stereo vision systems are used to determine the depth from two images taken at the same time but from slightly different viewpoints using two cameras. Each pair of images of a common scene is related by a popular geometry, and corresponding points in the images pairs are constrained to lie on pairs of conjugate popular lines. Materials and Methods Photos were taken from gardens of the Research Center for Agriculture and Natural Resources of Fars province, Iran. At first, the distance between the plants and the cameras should be determined. The distance between the plants and cameras can be obtained by using the stereo vision techniques. Therefore, this method was used in this paper by two pictures taken from each plant with the left and right cameras. The algorithm was written in MATLAB. To facilitate the segmentation of the branches from the rows at the back, a blue plate with dimensions of 2×2 m2 were used at the background. After invoking the images, branches were segmented from the background to produce the binary image. Then, the plant distance from the cameras was calculated by using the stereo vision. In next stage, the main trunk and one year old branches were identified and branches with thicknesses less than 7 mm were removed from the image. To omit these branches consecutive dilation and erosion operations were applied with circular structures having radii of 2 and 4 pixels. Then, based on the branch diameter, one-year-old branches were detected and pruned through considering the pruning parameters. The branches were pruned so that only three buds were left on them. For this aim, the branches should be pruned to have a length of 15 cm. To truncate the branches to 15 cm, the length of the main stem was measured for each of the branches, and branches with length less than 15 cm were omitted from the images. Then the main skeleton of grapevine was determined. Using this skeleton, the attaching points of the branches as well as attachment points to the trunk were identified. Distance between the branches was maintained. At the last step, the cutting points on the branches were determined by labeling the removed branches at each step. Results and Discussion The results indicated that the color components in the texture of the branches could not be used to identify one year old branches and evaluation results of algorithm showed that the proposed algorithm had acceptable performance and in all photos, one year old branches were correctly identified and pruning point of the grapevines were correctly marked. Also among 254 cut off-points extracted from 20 images, just 7 pruning points were misdiagnosed. These results revealed that the accuracy of the algorithm was about 96.8 percent. Conclusions Based on the reasonable achievement of the algorithm it can be concluded that it is possible to use machine vision routines to determine the most suitable cut off points for pruning robots. By an intelligent pruning robot, the one year old branches are diagnosed properly and the cut off points of the plants are determined. This can reduce the required labor to perform winter pruning in vineyards which subsequently reduces the time required and the costs needed for pruning the vineyards.
Why it matches plant phenotyping methodsブドウ樹の画像から枝構造、枝径、枝長、剪定位置を抽出するステレオビジョン・画像処理アルゴリズムを開発し、精度評価も行っており、植物表現型取得法が研究の中心である。
abstractIt this study in was attempted to develop an algorithm that uses image processing techniques to identify which parts of the grapevine should be cut.
This paper presents the design process of an embedded stereo vision system, which investigates the most relevant criteria for developing the hardware and software architectures for plant phenotyping. In other words, this paper is the result of a preliminary study in which the main motivation was the evaluation of the viability of a low-cost visual system for such field of knowledge. In addition, the implications of the adversities in an actual agricultural scenario under the system design are presented, since the system should not only meet the portability requirements but also the quality and precision for the measurements carried out by cameras. After the use of such method, the systems obtained may present a high chance of satisfying a set of constraints, and meeting their possibility to be used for machine vision applied in agricultural decision-making processes related to plant architecture and in situ recognition.
Why it matches plant phenotyping methods植物表現型取得のための低コスト組込みステレオビジョンシステムのハードウェア・ソフトウェア設計と実現可能性評価が中心であり、植物形態測定を目的とする手法開発に該当する。
abstractThis paper presents the design process of an embedded stereo vision system, which investigates the most relevant criteria for developing the hardware and software architectures for plant phenotyping.
In this paper, the method of spatial information extraction of tree branch was studied. The region matching method was used to get the disparity map of stereo image, extracted feature points combining with branch skeleton image by multi-segment approximation method, and calculated the spatial coordinates and the radius of branch feature points by using binocular stereo vision. Real-time model reconstruction for fruit tree has been researched on. Test proposed that each branch module was constructed by 12-prism in the coordinate origin, and then rotated twice and translated once to get correct posture, finally combined with other modules for the fruit tree model. Test has optimized extraction algorithm and matching algorithm of the branch region, improved matching rate, reduced matching errors, avoided matching confusion, accurately extracted branch spatial information and improved the success rate of robot path planning for obstacle avoidance.
Why it matches plant phenotyping methods双眼ステレオ画像から枝の空間座標・半径を抽出し、果樹の枝構造モデルを再構成する手法が中心であり、単なる収穫対象の検出を超えて植物器官の形態・構造を定量化している。
abstractthe method of spatial information extraction of tree branch was studied
Recent advances in omics technologies have not been accompanied by equally efficient, cost-effective, and accurate phenotyping methods required to dissect the genetic architecture of complex traits. Even though high-throughput phenotyping platforms have been developed for controlled environments, field-based aerial and ground technologies have only been designed and deployed for short-stature crops. Therefore, we developed and tested Phenobot 1.0, an auto-steered and self-propelled field-based high-throughput phenotyping platform for tall dense canopy crops, such as sorghum ( Sorghum bicolor ). Phenobot 1.0 was equipped with laterally positioned and vertically stacked stereo RGB cameras. Images collected from 307 diverse sorghum lines were reconstructed in 3D for feature extraction. User interfaces were developed, and multiple algorithms were evaluated for their accuracy in estimating plant height and stem diameter. Tested feature extraction methods included the following: (1) User-interactive Individual Plant Height Extraction (UsIn-PHe) based on dense stereo three-dimensional reconstruction; (2) Automatic Hedge-based Plant Height Extraction (Auto-PHe) based on dense stereo 3D reconstruction; (3) User-interactive Dense Stereo Matching Stem Diameter Extraction; and (4) User-interactive Image Patch Stereo Matching Stem Diameter Extraction (IPaS-Di). Comparative genome-wide association analysis and ground-truth validation demonstrated that both UsIn-PHe and Auto-PHe were accurate methods to estimate plant height, while Auto-PHe had the additional advantage of being a completely automated process. For stem diameter, IPaS-Di generated the most accurate estimates of this biomass-related architectural trait. In summary, our technology was proven robust to obtain ground-based high-throughput plant architecture parameters of sorghum, a tall and densely planted crop species.
Why it matches plant phenotyping methodsソルガムの草丈・茎径を取得する野外型高スループット画像計測プラットフォームを開発し、アルゴリズムの精度検証と地上実測比較を行っており、表現型取得法が研究の中心である。
abstractTherefore, we developed and tested Phenobot 1.0, an auto-steered and self-propelled field-based high-throughput phenotyping platform for tall dense canopy crops
Field / plotGreenhouseLaboratory / benchtopPhotogrammetry / SfM / MVSLiDAR / point cloudStereoLeafWhole plant / canopy / plot / field2D/3D reconstructionGrowth / development / phenology
Nowadays, 3D imaging of plants not only contributes to monitoring and managing plant growth, but is also becoming an essential part of high-throughput plant phenotyping. In this paper, an inexpensive (less than 70 USD) and portable platform with binocular stereo vision is established, which can be controlled by a laptop. In the stereo matching step, an efficient cost calculating measure—AD-Census—is integrated with the adaptive support-weight (ASW) approach to improve the ASW’s performance on real plant images. In the quantitative assessment, our stereo algorithm reaches an average error rate of 6.63% on the Middlebury datasets, which is lower than the error rates of the original ASW approach and several other popular algorithms. The imaging experiments using the proposed stereo system are carried out in three different environments including an indoor lab, an open field with grass, and a multi-span glass greenhouse. Six types of greenhouse plants are used in experiments; half of them are ornamentals and the others are greenhouse crops. The imaging accuracy of the proposed method at different baseline settings is investigated, and the results show that the optimal length of the baseline (distance between the two cameras of the stereo system) is around 80 mm for reaching a good trade-off between the depth accuracy and the mismatch rate for a plant that is placed within 1 m of the cameras. Error analysis from both theoretical and experimental sides show that for an object that is approximately 800 mm away from the stereo platform, the measured depth error of a single point is no higher than 5 mm, which is tolerable considering the dimensions of greenhouse plants. By applying disparity refinement, the proposed methodology generates dense and accurate point clouds of crops in different environments including an indoor lab, an outdoor field, and a greenhouse. Our approach also shows invariance against changing illumination in a real greenhouse, as well as the capability of recovering 3D surfaces of highlighted leaf regions. The method not only works on a binocular stereo system, but is also potentially applicable to a SFM-MVS (structure-from-motion and multiple-view stereo) system or any multi-view imaging system that uses stereo matching.
Why it matches plant phenotyping methods植物の3D形状取得を目的とする低価格ステレオ画像プラットフォームとステレオマッチング手法を開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractan inexpensive (less than 70 USD) and portable platform with binocular stereo vision is established
The arrangement of leaf material is critical in determining the light environment, and subsequently the photosynthetic productivity of complex crop canopies. However, links between specific canopy architectural traits and photosynthetic productivity across a wide genetic background are poorly understood for field grown crops. The architecture of five genetically diverse rice varieties-four parental founders of a multi-parent advanced generation intercross (MAGIC) population plus a high yielding Philippine variety (IR64)-was captured at two different growth stages using a method for digital plant reconstruction based on stereocameras. Ray tracing was employed to explore the effects of canopy architecture on the resulting light environment in high-resolution, whilst gas exchange measurements were combined with an empirical model of photosynthesis to calculate an estimated carbon gain and total light interception. To further test the impact of different dynamic light patterns on photosynthetic properties, an empirical model of photosynthetic acclimation was employed to predict the optimal light-saturated photosynthesis rate ( P max ) throughout canopy depth, hypothesizing that light is the sole determinant of productivity in these conditions. First, we show that a plant type with steeper leaf angles allows more efficient penetration of light into lower canopy layers and this, in turn, leads to a greater photosynthetic potential. Second the predicted optimal P max responds in a manner that is consistent with fractional interception and leaf area index across this germplasm. However, measured P max , especially in lower layers, was consistently higher than the optimal P max indicating factors other than light determine photosynthesis profiles. Lastly, varieties with more upright architecture exhibit higher maximum quantum yield of photosynthesis indicating a canopy-level impact on photosynthetic efficiency.
Why it matches plant phenotyping methodsステレオカメラによる3D植物再構成を用いてイネの群落構造形質を取得し、光環境・光合成との関係を解析しており、表現型取得ワークフローが研究の中心である。
abstractRay tracing was employed to explore the effects of canopy architecture on the resulting light environment in high-resolution
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Table S2
Physiological characteristics of the 15 parental MAGIC lines + IR64 used in the initial screening . All measurements, apart from harvest dry weight and seed dry weight, were taken 55–60 days after transplanting (DAT), corresponding to the vegetative growth stage.Open asset ↗lines:538-570Plant phenotyping relevance match · UnverifiedEurope PMC · checked 10 Sept 2026
Stereogrammetry applied to globally available high resolution spaceborne imagery (HRSI; Larix forests with slopes 35° and < 25° (during snow-free conditions) produced characteristic and consistently distinct distributions of elevation differences from reference lidar. The former include DSMs of near-ground surfaces with root mean square errors < 0.68 m relative to lidar. The latter, particularly those with angles < 10°, show distributions with larger differences from lidar that are associated with open canopy forests whose vegetation surface elevations are captured. Terrain aspect did not have a strong effect on the distribution of vegetation surfaces. Using the two DSM types together, the distribution of DSM-differenced heights in forests (μ = 6.0 m, σ = 1.4 m) was consistent with the distribution of plot-level mean tree heights (μ = 6.5 m, σ = 1.2 m). We conclude that the variation in sun elevation angle at time of stereopair acquisition can create illumination conditions conducive for capturing elevations of surfaces either near the ground or associated with vegetation canopy. Knowledge of HRSI acquisition solar geometry and snow cover can be used to understand and combine stereogrammetric surface elevation estimates to co-register and difference overlapping DSMs, providing a means to map forest height at fine scales, resolving the vertical structure of groups of trees from spaceborne platforms in open canopy forests.
Why it matches plant phenotyping methodsステレオ画像と太陽高度を用いて森林樹冠高を推定し、LiDARおよびプロット平均樹高と比較検証する手法が中心であるため。
abstractUsing the two DSM types together, the distribution of DSM-differenced heights in forests (μ = 6.0 m, σ = 1.4 m) was consistent with the distribution of plot-level mean tree heights (μ = 6.5 m, σ = 1.2 m).
Digital surface models (DSMs) derived from spaceborne and airborne sensors enable the monitoring of the vertical structures for forests in large areas. Nevertheless, due to the lack of an objective performance assessment for this task, it is difficult to select the most appropriate data source for DSM generation. In order to fill this gap, this paper performs change detection analysis including forest decrease and tree growth. The accuracy of the DSMs is evaluated by comparison with measured tree heights from inventory plots (field data). In addition, the DSMs are compared with LiDAR data to perform a pixel-wise quality assessment. DSMs from four different satellite stereo sensors (ALOS/PRISM, Cartosat-1, RapidEye and WorldView-2), one satellite InSAR sensor (TanDEM-X), two aerial stereo camera systems (HRSC and UltraCam) and two airborne laser scanning datasets with different point densities are adopted for the comparison. The case study is a complex central European temperate forest close to Traunstein in Bavaria, Germany. As a major experimental result, the quality of the DSM is found to be robust to variations in image resolution, especially when the forest density is high. The forest decrease results confirm that besides aerial photogrammetry data, very high resolution satellite data, such as WorldView-2, can deliver results with comparable quality as the ones derived from LiDAR, followed by TanDEM-X and Cartosat DSMs. The quality of the DSMs derived from ALOS and Rapid-Eye data is lower, but the main changes are still correctly highlighted. Moreover, the vertical tree growth and their relationship with tree height are analyzed. The major tree height in the study site is between 15 and 30 m and the periodic annual increments (PAIs) are in the range of 0.30–0.50 m.
Why it matches plant phenotyping methods複数センサーによるDSMから樹高・樹木成長を推定し、実測樹高およびLiDARとの比較で精度を評価しており、植物形質計測手法の検証が中心である。
abstractThe accuracy of the DSMs is evaluated by comparison with measured tree heights from inventory plots (field data).
Background and aims Intercropping systems contain two or more species simultaneously in close proximity. Due to contrasting features of the component crops, quantification of the light environment and photosynthetic productivity is extremely difficult. However it is an essential component of productivity. Here, a low-tech but high-resolution method is presented that can be applied to single- and multi-species cropping systems to facilitate characterization of the light environment. Different row layouts of an intercrop consisting of Bambara groundnut ( Vigna subterranea ) and proso millet ( Panicum miliaceum ) have been used as an example and the new opportunities presented by this approach have been analysed. Methods Three-dimensional plant reconstruction, based on stereo cameras, combined with ray tracing was implemented to explore the light environment within the Bambara groundnut-proso millet intercropping system and associated monocrops. Gas exchange data were used to predict the total carbon gain of each component crop. Key results The shading influence of the tall proso millet on the shorter Bambara groundnut results in a reduction in total canopy light interception and carbon gain. However, the increased leaf area index (LAI) of proso millet, higher photosynthetic potential due to the C4 pathway and sub-optimal photosynthetic acclimation of Bambara groundnut to shade means that increasing the number of rows of millet will lead to greater light interception and carbon gain per unit ground area, despite Bambara groundnut intercepting more light per unit leaf area. Conclusions Three-dimensional reconstruction combined with ray tracing provides a novel, accurate method of exploring the light environment within an intercrop that does not require difficult measurements of light interception and data-intensive manual reconstruction, especially for such systems with inherently high spatial possibilities. It provides new opportunities for calculating potential productivity within multi-species cropping systems, enables the quantification of dynamic physiological differences between crops grown as monoculture and those within intercrops, and enables the prediction of new productive combinations of previously untested crops.
Why it matches plant phenotyping methodsステレオカメラによる3次元植物再構成とレイトレーシングを開発・適用し、作物キャノピーの光環境や生産性を定量化する手法が研究の中心である。
abstractHere, a low-tech but high-resolution method is presented that can be applied to single- and multi-species cropping systems to facilitate characterization of the light environment.
Trees outside forest (TOF) can perform a variety of social, economic and ecological functions including carbon sequestration. However, detailed quantification of tree biomass is usually limited to forest areas. Taking advantage of structural information available from stereo aerial imagery and airborne laser scanning (ALS), this research models tree biomass using national forest inventory data and linear least-square regression and applies the model both inside and outside of forest to create a nationwide model for tree biomass (above ground and below ground). Validation of the tree biomass model against TOF data within settlement areas shows relatively low model performance (R 2 of 0.44) but still a considerable improvement on current biomass estimates used for greenhouse gas inventory and carbon accounting. We demonstrate an efficient and easily implementable approach to modelling tree biomass across a large heterogeneous nationwide area. The model offers significant opportunity for improved estimates on land use combination categories (CC) where tree biomass has either not been included or only roughly estimated until now. The ALS biomass model also offers the advantage of providing greater spatial resolution and greater within CC spatial variability compared to the current nationwide estimates.
Why it matches plant phenotyping methods航空ステレオ画像とALSから樹木バイオマスという植物形質を推定するモデルを開発し、TOFデータで検証しているため、測定・推定手法が中心である。
abstractTaking advantage of structural information available from stereo aerial imagery and airborne laser scanning (ALS), this research models tree biomass using national forest inventory data and linear least-square regression
Background The fitness of the rape leaf is closely related to its biomass and photosynthesis. The study of leaf traits is significant for improving rape leaf production and optimizing crop management. Canopy structure and individual leaf traits are the major indicators of quality during the rape seedling stage. Differences in canopy structure reflect the influence of environmental factors such as water, sunlight and nutrient supply. The traits of individual rape leaves traits indicate the growth period of the rape as well as its canopy shape. Results We established a high-throughput stereo-imaging system for the reconstruction of the three-dimensional canopy structure of rape seedlings from which leaf area and plant height can be extracted. To evaluate the measurement accuracy of leaf area and plant height, 66 rape seedlings were randomly selected for automatic and destructive measurements. Compared with the manual measurements, the mean absolute percentage error of automatic leaf area and plant height measurements was 3.68 and 6.18%, respectively, and the squares of the correlation coefficients (R 2 ) were 0.984 and 0.845, respectively. Compared with the two-dimensional projective imaging method, the leaf area extracted using stereo-imaging was more accurate. In addition, a semi-automatic image analysis pipeline was developed to extract 19 individual leaf shape traits, including 11 scale-invariant traits, 3 inner cavity related traits, and 5 margin-related traits, from the images acquired by the stereo-imaging system. We used these quantified traits to classify rapes according to three different leaf shapes: mosaic-leaf, semi-mosaic-leaf, and round-leaf. Based on testing of 801 seedling rape samples, we found that the leave-one-out cross validation classification accuracy was 94.4, 95.6, and 94.8% for stepwise discriminant analysis, the support vector machine method and the random forest method, respectively. Conclusions In this study, a nondestructive and high-throughput stereo-imaging system was developed to quantify canopy three-dimensional structure and individual leaf shape traits with improved accuracy, with implications for rape phenotyping, functional genomics, and breeding.
Why it matches plant phenotyping methodsステレオ画像による3次元再構成と画像解析パイプラインを開発し、葉面積・草丈・葉形質を検証しており、植物フェノタイピング手法が研究の中心である。
abstractWe established a high-throughput stereo-imaging system for the reconstruction of the three-dimensional canopy structure of rape seedlings from which leaf area and plant height can be extracted.
Stereogrammetry applied to globally available high resolution spaceborne imagery (HRSI; 35° and <25° (during snow-free conditions) produced characteristic and consistently distinct distributions of elevation differences from reference lidar. The former include DSMs of near-ground surfaces with root mean square errors<0.68m relative to lidar. The latter, particularly those with angles<10°, show distributions with larger differences from lidar that are associated with open canopy forests whose vegetation surface elevations are captured. Terrain aspect did not have a strong effect on the distribution of vegetation surfaces. Using the two DSM types together, the distribution of DSM-differenced heights in forests (μ=6.0m, σ=1.4m) was consistent with the distribution of plot-level mean tree heights (μ=6.5m, σ=1.2m). We conclude that the variation in sun elevation angle at time of stereopair acquisition can create illumination conditions conducive for capturing elevations of surfaces either near the ground or associated with vegetation canopy. Knowledge of HRSI acquisition solar geometry and snow cover can be used to understand and combine stereogrammetric surface elevation estimates to co-register and difference overlapping DSMs, providing a means to map forest height at fine scales, resolving the vertical structure of groups of trees from spaceborne platforms in open canopy forests.
Why it matches plant phenotyping methods衛星ステレオ画像と太陽高度を利用して森林の植生・樹高を推定し、LiDARおよびプロット平均樹高と比較検証する測定手法が研究の中心であるため。
abstractUsing the two DSM types together, the distribution of DSM-differenced heights in forests (μ=6.0m, σ=1.4m) was consistent with the distribution of plot-level mean tree heights (μ=6.5m, σ=1.2m).
Crop surface models (CSMs) representing plant height above ground level are a useful tool for monitoring in-field crop growth variability and enabling precision agriculture applications. A semiautomated system for generating CSMs was implemented. It combines an Android application running on a set of smart cameras for image acquisition and transmission and a set of Python scripts automating the structure-from-motion (SfM) software package Agisoft Photoscan and ArcGIS. Only ground-control-point (GCP) marking was performed manually. This system was set up on a barley field experiment with nine different barley cultivars in the growing period of 2014. Images were acquired three times a day for a period of two months. CSMs were successfully generated for 95 out of 98 acquisitions between May 2 and June 30. The best linear regressions of the CSM-derived plot-wise averaged plant-heights compared to manual plant height measurements taken at four dates resulted in a coefficient of determination R2 of 0.87 and a root-mean-square error (RMSE) of 0.08 m, with Willmott’s refined index of model performance dr equaling 0.78. In total, 103 mean plot heights were used in the regression based on the noon acquisition time. The presented system succeeded in semiautomatedly monitoring crop height on a plot scale to field scale.
Why it matches plant phenotyping methods低コストのステレオ画像とSfMを用いて植物高を自動抽出するシステムを開発し、実測値との技術検証も行っているため、植物フェノタイピング手法が中心である。
abstractA semiautomated system for generating CSMs was implemented.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 11 Sept 2026
A plant phenotyping approach was applied to evaluate growth rate of containerized tree seedlings during the precultivation phase following seed germination. A simple and affordable stereo optical system was used to collect stereoscopic red-green-blue (RGB) images of seedlings at regular intervals of time. Comparative analysis of these images by means of a newly developed software enabled us to calculate (a) the increments of seedlings height and (b) the percentage greenness of seedling leaves. Comparison of these parameters with destructive biomass measurements showed that the height traits can be used to estimate seedling growth for needle-leaved plant species whereas the greenness trait can be used for broad-leaved plant species. Despite the need to adjust for plant type, growth stage and light conditions this new, cheap, rapid, and sustainable phenotyping approach can be used to study large-scale phenome variations due to genome variability and interaction with environmental factors.
Why it matches plant phenotyping methods自動ステレオビジョンによる植物形質取得と新規ソフトウェアを開発し、樹木苗の高さ・葉の緑色度を破壊的バイオマス測定と比較検証しており、フェノタイピング手法が研究の中心である。
abstractA plant phenotyping approach was applied to evaluate growth rate of containerized tree seedlings during the precultivation phase following seed germination.
Three-dimensional (3D) reconstruction of a tree canopy is an important step in order to measure canopy geometry, such as height, width, volume, and leaf cover area. In this research, binocular stereo vision was used to recover the 3D information of the canopy. Multiple images were taken from different views around the target. The Structure-from-motion (SfM) method was employed to recover the camera calibration matrix for each image, and the corresponding 3D coordinates of the feature points were calculated and used to recover the camera calibration matrix. Through this method, a sparse projective reconstruction of the target was realized. Subsequently, a ball pivoting algorithm was used to do surface modeling to realize dense reconstruction. Finally, this dense reconstruction was transformed to metric reconstruction through ground truth points which were obtained from camera calibration of binocular stereo cameras. Four experiments were completed, one for a known geometric box, and the other three were: a croton plant with big leaves and salient features, a jalapeno pepper plant with median leaves, and a lemon tree with small leaves. A whole-view reconstruction of each target was realized. The comparison of the reconstructed box’s size with the real box’s size shows that the 3D reconstruction is in metric reconstruction.
Why it matches plant phenotyping methods植物キャノピーの3D再構成手法を開発・検証し、高さ、幅、体積、葉面積などの形態形質の計測を目的としているため、植物フェノタイピング手法が中心である。
abstractThree-dimensional (3D) reconstruction of a tree canopy is an important step in order to measure canopy geometry, such as height, width, volume, and leaf cover area.
Abstract. Accurate full 3D reconstruction of plants is a crucial step in phenotyping the canopy geometry of living plants to measure features, such as plant height, plant volume, leaf count, leaf size, and internode distance. In this work, an in-field, full 3D reconstruction system for plant phenotyping is described. The system utilized simultaneous, multi-view, high-resolution color digital imagery for true 3D reconstruction of the crop. The cameras were mounted on an arc-shaped superstructure and organized into 16 stereo pairs in four separate arcs. In this study, a comparison of structure-from-motion (SfM) and stereo vision (using stereo cameras) techniques in terms of full 3D reconstruction and measurement of plant features is introduced. System performance was verified in outdoor, on-farm experiments conducted at multiple time points in the growth cycle of sunflower. Results show, that both SfM and stereovision techniques can yield satisfactory 3D reconstruction models and are suited for high-throughput phenotyping without destroying any leaves or stems of the plant. By taking into account small details in the plant and edge preservation for leaves, the custom stereovision algorithms for this system could outperform the SfM technique and provided superior 3D reconstruction results.
Why it matches plant phenotyping methods植物の3D再構成と形質抽出を目的とする圃場フェノタイピングシステムを開発し、SfMとステレオビジョンを比較・検証しているため、方法が中心的である。
abstractIn this work, an in-field, full 3D reconstruction system for plant phenotyping is described.
Published23 Jun 2016The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 0 · OpenAlex ↗
Abstract. Traditional field methods for measuring tree heights are often too costly and time consuming. An alternative remote sensing approach is to measure tree heights from digital stereo photographs which is more practical for forest managers and less expensive than LiDAR or synthetic aperture radar. This work proposes an estimation of stand height and forest volume(m3/ha) using normalized digital surface model (nDSM) from high resolution stereo photography (25cm resolution) and forest type map. The study area was located in Mt. Maehwa model forest in Hong Chun-Gun, South Korea. The forest type map has four attributes such as major species, age class, DBH class and crown density class by stand. Overlapping aerial photos were taken in September 2013 and digital surface model (DSM) was created by photogrammetric methods(aerial triangulation, digital image matching). Then, digital terrain model (DTM) was created by filtering DSM and subtracted DTM from DSM pixel by pixel, resulting in nDSM which represents object heights (buildings, trees, etc.). Two independent variables from nDSM were used to estimate forest stand volume: crown density (%) and stand height (m). First, crown density was calculated using canopy segmentation method considering live crown ratio. Next, stand height was produced by averaging individual tree heights in a stand using Esri’s ArcGIS and the USDA Forest Service’s FUSION software. Finally, stand volume was estimated and mapped using aerial photo stand volume equations by species which have two independent variables, crown density and stand height. South Korea has a historical imagery archive which can show forest change in 40 years of successful forest rehabilitation. For a future study, forest volume change map (1970s–present) will be produced using this stand volume estimation method and a historical imagery archive.
Why it matches plant phenotyping methods高解像度ステレオ画像から樹高・林分体積を推定する測定手法を提案しており、植物形質の取得・抽出が研究の中心である。
abstractThis work proposes an estimation of stand height and forest volume(m3/ha) using normalized digital surface model (nDSM) from high resolution stereo photography (25cm resolution) and forest type map.
Published23 Jun 2016The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 0 · OpenAlex ↗
Traditional field methods for measuring tree heights are often too costly and time consuming. An alternative remote sensing approach is to measure tree heights from digital stereo photographs which is more practical for forest managers and less expensive than LiDAR or synthetic aperture radar. This work proposes an estimation of stand height and forest volume(m3/ha) using normalized digital surface model (nDSM) from high resolution stereo photography (25cm resolution) and forest type map. The study area was located in Mt. Maehwa model forest in Hong Chun-Gun, South Korea. The forest type map has four attributes such as major species, age class, DBH class and crown density class by stand. Overlapping aerial photos were taken in September 2013 and digital surface model (DSM) was created by photogrammetric methods(aerial triangulation, digital image matching). Then, digital terrain model (DTM) was created by filtering DSM and subtracted DTM from DSM pixel by pixel, resulting in nDSM which represents object heights (buildings, trees, etc.). Two independent variables from nDSM were used to estimate forest stand volume: crown density (%) and stand height (m). First, crown density was calculated using canopy segmentation method considering live crown ratio. Next, stand height was produced by averaging individual tree heights in a stand using Esri’s ArcGIS and the USDA Forest Service’s FUSION software. Finally, stand volume was estimated and mapped using aerial photo stand volume equations by species which have two independent variables, crown density and stand height. South Korea has a historical imagery archive which can show forest change in 40 years of successful forest rehabilitation. For a future study, forest volume change map (1970s–present) will be produced using this stand volume estimation method and a historical imagery archive.
Why it matches plant phenotyping methods高解像度ステレオ画像から樹木高、林分高、冠密度、林分蓄積を推定する画像解析・測定ワークフローが研究の中心であり、森林植物の形態・構造形質を直接推定している。
abstractThis work proposes an estimation of stand height and forest volume(m3/ha) using normalized digital surface model (nDSM) from high resolution stereo photography (25cm resolution) and forest type map.
Published16 Jun 2016The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 4 · OpenAlex ↗
Crop Surface Models (CSMs) are 2.5D raster surfaces representing absolute plant canopy height. Using multiple CMSs generated from data acquired at multiple time steps, a crop surface monitoring is enabled. This makes it possible to monitor crop growth over time and can be used for monitoring in-field crop growth variability which is useful in the context of high-throughput phenotyping. This study aims to evaluate several software packages for dense 3D reconstruction from multiple overlapping RGB images on field and plot-scale. A summer barley field experiment located at the Campus Klein-Altendorf of University of Bonn was observed by acquiring stereo images from an oblique angle using consumer-grade smart cameras. Two such cameras were mounted at an elevation of 10 m and acquired images for a period of two months during the growing period of 2014. The field experiment consisted of nine barley cultivars that were cultivated in multiple repetitions and nitrogen treatments. Manual plant height measurements were carried out at four dates during the observation period. The software packages Agisoft PhotoScan, VisualSfM with CMVS/PMVS2 and SURE are investigated. The point clouds are georeferenced through a set of ground control points. Where adequate results are reached, a statistical analysis is performed.
Why it matches plant phenotyping methods複数のソフトウェアによるRGB画像からの密な3D再構成を評価し、作物キャノピー高を推定・検証する研究であり、植物表現型取得手法が中心です。
abstractThis study aims to evaluate several software packages for dense 3D reconstruction from multiple overlapping RGB images on field and plot-scale.
Published16 Jun 2016The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 2 · OpenAlex ↗
Abstract. Crop Surface Models (CSMs) are 2.5D raster surfaces representing absolute plant canopy height. Using multiple CMSs generated from data acquired at multiple time steps, a crop surface monitoring is enabled. This makes it possible to monitor crop growth over time and can be used for monitoring in-field crop growth variability which is useful in the context of high-throughput phenotyping. This study aims to evaluate several software packages for dense 3D reconstruction from multiple overlapping RGB images on field and plot-scale. A summer barley field experiment located at the Campus Klein-Altendorf of University of Bonn was observed by acquiring stereo images from an oblique angle using consumer-grade smart cameras. Two such cameras were mounted at an elevation of 10 m and acquired images for a period of two months during the growing period of 2014. The field experiment consisted of nine barley cultivars that were cultivated in multiple repetitions and nitrogen treatments. Manual plant height measurements were carried out at four dates during the observation period. The software packages Agisoft PhotoScan, VisualSfM with CMVS/PMVS2 and SURE are investigated. The point clouds are georeferenced through a set of ground control points. Where adequate results are reached, a statistical analysis is performed.
Why it matches plant phenotyping methods複数の画像から作成した3D再構成ソフトウェアを評価し、作物群落高を推定する手法の性能を実測値と比較して検証しているため、植物フェノタイピング手法が中心です。
abstractThis study aims to evaluate several software packages for dense 3D reconstruction from multiple overlapping RGB images on field and plot-scale.
A multi-view stereo vision system for true 3D reconstruction, modeling and phenotyping of plants was created that successfully resolves many of the shortcomings of traditional camera-based 3D plant phenotyping systems. This novel system incorporates several features including: computer algorithms, including camera calibration, excessive-green based plant segmentation, semi-global stereo block matching, disparity bilateral filtering, 3D point cloud processing, and 3D feature extraction, and hardware consisting of a hemispherical superstructure designed to hold five stereo pairs of cameras and a custom designed structured light pattern illumination system. This system is nondestructive and can extract 3D features of whole plants modeled from multiple pairs of stereo images taken at different view angles. The study characterizes the systems phenotyping performance for 3D plant features: plant height, total leaf area, and total leaf shading area. For plants having specified leaf spacing and size, the algorithms used in our system yielded satisfactory experimental results and demonstrated the ability to study plant development where the same plants were repeatedly imaged and phenotyped over the time.
Why it matches plant phenotyping methods植物の3D形質を取得する画像ベース表現型解析システムの開発と性能評価が研究の中心であるため。
abstractA multi-view stereo vision system for true 3D reconstruction, modeling and phenotyping of plants was created
In plant factories, plants are usually cultivated in nutrient solution under a controllable environment. Plant quality and growth are closely monitored and precisely controlled. For plant growth evaluation, plant weight is an important and commonly used indicator. Traditional plant weight measurements are destructive and laborious. In order to measure and record the plant weight during plant growth, an automated measurement system was designed and developed herein. The weight measurement system comprises a weight measurement device and an imaging system. The weight measurement device consists of a top disk, a bottom disk, a plant holder and a load cell. The load cell with a resolution of 0.1 g converts the plant weight on the plant holder disk to an analog electrical signal for a precise measurement. The top disk and bottom disk are designed to be durable for different plant sizes, so plant weight can be measured continuously throughout the whole growth period, without hindering plant growth. The results show that plant weights measured by the weight measurement device are highly correlated with the weights estimated by the stereo-vision imaging system; hence, plant weight can be measured by either method. The weight growth of selected vegetables growing in the National Taiwan University plant factory were monitored and measured using our automated plant growth weight measurement system. The experimental results demonstrate the functionality, stability and durability of this system. The information gathered by this weight system can be valuable and beneficial for hydroponic plants monitoring research and agricultural research applications.
Why it matches plant phenotyping methods植物重量という形質を連続・非破壊に取得する自動計測システムを開発し、ロードセルとステレオビジョンを比較検証しているため、植物フェノタイピング手法が中心である。
abstractIn order to measure and record the plant weight during plant growth, an automated measurement system was designed and developed herein.
Sorghum is known as a major potential feedstock for biofuel production. Being able to efficiently discover genetic control of many traits over a large number of genotypes, genome-wide association study (GWAS) has become a powerful tool for studying sorghum biomass yield components. However, automated high-throughput field-based plant phenotyping is now the bottleneck for scaling up such experiments. This paper presents an auto-guidance enabled utility tractor which navigates itself between crop rows with a predefined path while collecting stereo images of sorghum samples from both sides of the vehicle. Three levels of stereo camera heads were instrumented to capture images of plants up to 3 meters tall. The stereo images were processed offline to reconstruct 3D point clouds using Semi-Global Block Matching. A semi-automated software interface was developed to measure stem diameter due to the strict sampling strategy and the complexity of high-density crop canopy. An automated hedge-based feature extraction pipeline was proposed to quantify other variations in plant architecture traits such as plant height, leaf area index (LAI) and vegetation volume index (VVI). The stem diameter measured using the semiautomatic method showed high correlation (0.958) to hand measurement.
Why it matches plant phenotyping methodsステレオ画像取得、3D再構成、半自動・自動特徴抽出を開発し、茎径・草丈・LAI・VVIなどの植物形質を測定・検証することが中心であるため。
abstractThis paper presents an auto-guidance enabled utility tractor which navigates itself between crop rows with a predefined path while collecting stereo images of sorghum samples from both sides of the vehicle.
: Automatically measuring the dynamics of plant phenotype is fundamental to the enhancement of our ability to dissect the agriculturally important traits and the understanding of plant development processes. This paper describes a high-throughput, automatic phenotyping platform to trace the phenotype of leafy plant and complete the screening function. First, a binocular stereo vision system is introduced to acquire images and transfer them to a host computer which processes, analyses and obtains some certain morphological parameters, such as leaf area and height. Second, according to the parameters obtained at different time points, a quantitative phenotype database and a prediction model of growth are established. Third, based on the models, a robotic arm executes the transplanting instructions to screen the plant with undesirable characteristics. The experiment results of leaf area show the measurement accuracy is higher than 90%, and this method can be applied in accurate measurement of plant phenotypic parameters. This work demonstrates how a high-throughput phenotyping equipment can construct an evaluation index system of plant growth during its whole cultivation period with high spatial and temporal resolution by machine vision, and offers an automated approach to the screening in plant breeding.
Why it matches plant phenotyping methods植物形態形質の画像取得・抽出、精度評価、成長予測、ロボット連携を中心とする高スループット表現型解析プラットフォームの開発研究である。
abstractThis paper describes a high-throughput, automatic phenotyping platform to trace the phenotype of leafy plant and complete the screening function.
Lidar has become an established tool for mapping forest structure attributes including those used as inputs for fire behavior and effects modelling. However, lidar is rarely available to document pre-fire conditions due to its sparse availability. In contrast, aerial imagery is regularly collected in many regions, and advances in stereo image matching have enabled the creation of dense photogrammetric point clouds similar to those from lidar. As part of a study of the physical and ecological impacts of the 2012 High Park Fire, we generated a photogrammetric point cloud from pre-fire aerial imagery collected in 2008 and calculated forest height using a digital terrain model generated from a 2013 post-fire lidar collection. A suite of canopy height and density metrics were created from both the pre-fire photogrammetry and the post-fire lidar point clouds. These metrics were compared to each other and to forest structure attributes measured in the field. For unburned areas, we found strong relationships between corresponding lidar and photogrammetry height and density metrics with biases that were consistent with known differences in each sensor’s method of sampling the canopy. Regressions models of field-measured forest structure attributes incorporating both lidar and photo metrics demonstrated that a single equation could estimate some forest structure attributes without significant intercept or slope bias due to the source of the metrics (i.e. photo or lidar). Models of aboveground biomass on unburned plots had similar root mean square errors for lidar (29.3%), photogrammetry (31.0%), and combined data sources (RMSE = 29.1% and source intercept bias = 34.64 Mg ha-1 and slope bias = -0.28). Similar results were obtained for Lorey's height, basal area, and canopy bulk density. Models of structure in burned areas derived from post-fire lidar had lower performance than photogrammetry due to the fire's consumption of canopy materials which generally reduced the explanatory power of lidar density metrics. Pre-fire forest structure information could aid assessments of contributing factors such as canopy fuels and fire effects such as loss of biomass. The wide spatial and temporal coverage of aerial photos and growing coverage of lidar could enable many other applications of combining photogrammetry with lidar, including assessments of changes in forest carbon storage.
Why it matches plant phenotyping methods航空写真からのステレオフォトグラメトリとLiDARで森林の高さ・密度・バイオマス等の植物構造形質を推定し、相互比較および現地測定で検証しているため、手法が中心的です。
abstractA suite of canopy height and density metrics were created from both the pre-fire photogrammetry and the post-fire lidar point clouds. These metrics were compared to each other and to forest structure attributes measured in the field.
Digital photogrammetry has advanced to the point where digital elevation models (DEMs) can be derived in full automation from stereo images, offering new opportunities in various fields including forestry. However, the performance and limitations of digital photogrammetry need to be carefully investigated in forest environments where both scientific studies and forest management depend on accurate information. We evaluated the performance of a photogrammetric digital surface model (photo-DSM) derived from small-format aerial photographs over approximately 2000 ha of tropical montane forest in northern Borneo, Malaysia. The accuracy of the photo-DSM was evaluated by using a reference dataset derived from airborne laser scanning (ALS) with an approximate density of 15 pulses/m2. The vertical accuracy over the total area (18,349,288 pixels) was represented by a mean error of 0.006 m and RMSE of 3.003 m, with 61.1% of all measured heights accurate to within ±1 m, 81.9% accurate to within ±2 m, and 88.7% accurate to within ±3 m. More detailed local accuracy evaluation was conducted at block level: 31 1-ha blocks and one 0.25-ha block located over different forest types and characterized by the mean canopy height (range=8.441.1 m) and standard deviation (range=2.09.8 m) of the ALS-canopy height model (ALS-CHM). RMSE of the forest blocks ranged from 1.01 to 4.19 m, and this variance in RMSE could be explained by 78.6% of standard deviation of the ALS-CHM. Canopy slope and dark areas also had an effect on the RMSE: in areas of higher canopy slope and in darker areas within the forest blocks, the RMSE increased by up to 8.6 and 5.8 m, respectively. No-data areas accounted for 3.24% in the forest blocks and were also influenced by canopy slope and darker areas. RMSE of non-forest areas was 0.39 m (n=5243 pixels). Research and development on image-matching algorithms (which achieved 86.1% successful alignment of the aerial photographs in our study), cameras, unmanned aerial vehicles, and flight parameters are ongoing; as a result, digital photogrammetry and its capacity for use in various forestry applications are also continuing to improve.
Why it matches plant phenotyping methods熱帯林の樹冠高を推定する写真測量DSMについて、ALSを基準に精度・誤差・適用条件を評価しており、植物キャノピー形態の取得手法の検証が中心である。
abstractWe evaluated the performance of a photogrammetric digital surface model (photo-DSM) derived from small-format aerial photographs over approximately 2000 ha of tropical montane forest
U radu je ispitana tocnost fotogrametrijske procjene srednje sastojinske visine odvojeno po glavnim vrstama drveca (hrast kitnjak, obicna bukva, obicni grab) na brežuljkastom podrucju sumoposjednickih suma sredisnje Hrvatske. U istraživanju su koristene digitalne stereoaerosnimke prostorne rezolucije GSD 30 cm te digitalni vektorski podaci za izradu digitalnoga modela reljefa (DMR-a). Stereofotogrametrijska izmjera visine stabala provedena je na fotogrametrijskim plohama postavljenim na aerosnimkama na temelju GPS-om snimljenih prostornih koordinata (x, y) sredista terenskih. Fotogrametrijska visina svakoga stabla na plohi izracunata je kao razlika nadmorske visine vrha stabla određena na aerosnimkama i podnožja stabla dobivena iz DMR-a. Tocnost fotogrametrijski procijenjene visine pojedinih vrsta drveca ispitana je usporedbom s referentnom terenski procijenjenom visinom. Radi toga je za svaki odsjek izracunata fotogrametrijski i terenski srednja sastojinska visina odvojeno po vrstama drveca. Srednja sastojinska visina izracunata je kao aritmeticka sredina svih izmjerenih visina unutar odsjeka. Za sve tri promatrane vrste drveca (hrast kitnjak, obicna bukva, obicni grab) dobivena su vrlo slicna odstupanja fotogrametrijski procijenjene srednje sastojinske visine u odnosu na referentne terenske vrijednosti. Ipak, najtocniji rezultati dobiveni su za obicni grab (MD = –1, 97 %, RMSE% = 8, 29 %), a nesto slabiji za obicnu bukvu (MD = 2, 16 %, RMSE% = 10, 16 %) i hrast kitnjak (MD = 3, 06 %, RMSE% = 10, 27 %). Rezultati su istraživanja potvrdili veliku mogucnost primjene digitalnih aerosnimaka visoke prostorne rezolucije u inventuri suma, odnosno pri procjeni srednje sastojinske visine.
Why it matches plant phenotyping methods航空ステレオ画像から樹高・林分平均樹高を推定し、地上測定値と比較して精度検証しており、植物形質の取得手法が研究の中心です。
titlePhotogrammetric Estimation of the Mean Stand Heights Separated by Tree Species in Mixed Forests of Central Croatia