Polyploidy can be a critical factor for explaining plant trait variation, niche diversification, or speciation. However, inferring ploidy from silica-dried or historical samples using chromosome counts or flow cytometry is not possible, and scaling up ploidy estimation to population-level fresh contemporary samples can be challenging as well. Thus, we present a new method for estimating ploidy levels directly from sequencing data using machine learning; the Polyploid Population Genomics Tool Kit (PPGTK). The machine-learning approach is advantageous as it relaxes the assumptions of previous probabilistic methods and provides per-sample probabilities, allowing investigators to evaluate uncertainty in their system of interest.. We demonstrate performance and accuracy of the method on simulated and empirical data. Simulations showed above 99% accuracy, even for low coverage data, as long reads were mappable to the reference genome. For empirical analyses, we used target enrichment data from blueberry wild relatives (Vaccinium sect. Cyanococcus) and whole-genome data from sweetpotato wild relatives (Ipomoea ser. Batatas). Ploidy was recovered with 99% accuracy across 70 Vaccinium individuals and 97% across 82 Ipomoea individuals. Analysis of many individuals is fast and requires only a multisample VCF, which is presumably generated for the research anyway, and some samples of known ploidy for training the classifier. The approach implemented in PPGTK is promising for collections-based research as well, enabling ploidy classification of historical specimens based on present-day observations. The method is implemented in a new Python package as a single command that can run on a conventional laptop.
Why it matches plant phenotyping methods植物の倍数性という状態をシーケンスデータから推定する機械学習手法を開発し、シミュレーションおよび実データで精度検証している。Pythonパッケージとして実装され、手法自体が中心である。
abstractwe present a new method for estimating ploidy levels directly from sequencing data using machine learning; the Polyploid Population Genomics Tool Kit (PPGTK).
Reproduction assets foundThe paper's ploidy-classification method is implemented in the authors' public Python package PPGTK, with a specific release (v0.1.0-alpha) used for the manuscript's analyses. The empirical VCF/metadata datasets are promised on Dryad only 'upon acceptance' and thus are not yet actionable.Code · public11
VCFs and metadata needed to reproduce Vaccinium sect. Cyanococcus and Ipomoea ser.
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Batatas analyses with PPGTK will be made available via Dryad upon acceptance. PPGTK is
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available on GitHub, and release v0.1.0-alpha was the version used for analyses in this
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manuscript (https://github.com/tileylab/PPGTK/releases/tag/v0.1.0-alpha). PPGTK currently has
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other functions for calculating population genetic summary statistics, but the classify-ploidy
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function implements the machine-learning method described in the manuscript.
377
378
.
CC-BY 4.0 International license
is made available under a
preprint (which was not certified by peer review) is the auOpen asset ↗tileylab/PPGTK · v0.1.0-alphapdf-raw-page:11 lines:1-24Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Abstract Quantifying blueberry fruit yield and maturity is important for evaluating yield potential in breeding trials, but manual measurement remains slow, labor-intensive, and costly. Object detection and classification networks offer a high-throughput solution, yet few studies validate image-based counts against hand-harvested ground truth while explicitly accounting for canopy occlusion. Hence, this study developed a multi-class berry detection pipeline for immature and mature berries and validated image-based estimates against hand-harvest counts across 32 diverse southern highbush blueberry genotypes. Among the models evaluated, YOLOv8x achieved the highest detection performance, with an mAP50 of 0.82 and an mAP50–95 of 0.66. External validation produced F1 scores ranging from 0.74 to 0.91 for berry maturity classes. However, image-based detections systematically underestimated hand-harvested fruit counts, with R² values ranging from 0.40 to 0.61. Fruit occlusion varied widely among genotypes, from 42% to 90%, indicating that canopy structure strongly affects berry visibility. Incorporating image-derived canopy architecture, color, and texture features improved predictions of berry counts and maturity. Partial Least Squares regression provided the best performance, increasing R² values of hand-harvested fruit counts, ranging from 0.52 to 0.74. These results show that accounting for canopy occlusion improves image-based estimation of blueberry yield and supports more accurate high-throughput phenotyping.
Why it matches plant phenotyping methodsブルーベリーの収量・成熟度を画像から推定する検出パイプラインを開発し、手収穫値との外部検証および樹冠遮蔽・構造を考慮した改良を行っており、植物表現型取得法が中心である。
abstractthis study developed a multi-class berry detection pipeline for immature and mature berries and validated image-based estimates against hand-harvest counts
Abstract Fruit size, shape, color, and percent fruit rot are important quality traits for breeding cranberry ( Vaccinium macrocarpon Ait.). Image analysis can be used to measure these traits, but affordable hardware for standardized image capture and integrated user‐friendly software pipelines are lacking. Additionally, no image‐based method exists to estimate percent fruit rot, an otherwise tediously and subjectively measured trait. We created the BerryBox, a simple and inexpensive lightbox, camera mount, and accompanying software pipeline to standardize the capture and analysis of postharvest fruit images. Trained deep neural network models were highly accurate for segmenting sound fruit (F1 score: 99.4%) and detecting rotten fruit (F1 score: 98.5%). We applied the BerryBox to images of cranberries harvested across 3 years from a 156‐clone breeding population. Narrow‐sense heritability estimates of image‐based fruit color, shape, size, and percent fruit rot ranged from 0.37 to 0.95. Random subsampling showed that 25–30 berries per genotype were sufficient to describe the variation in the full dataset. We demonstrated the utility of BerryBox traits in a small‐scale genetic linkage mapping analysis, detecting significant marker–trait associations that coincided with those of traditionally measured traits. The BerryBox software was able to accurately segment fruit from images of blueberries without model retraining, showing its applicability to other similarly shaped fruits. The software pipeline and BerryBox materials and assembly instructions are publicly available for others to adopt for low‐cost image‐based phenotyping.
Why it matches plant phenotyping methodsクランベリー等の果実形質と腐敗率を画像から抽出する低コスト撮像装置・ソフトウェアパイプラインを開発し、精度検証と他果実への適用性評価を行った、中心的な植物フェノタイピング手法研究である。
abstractWe created the BerryBox, a simple and inexpensive lightbox, camera mount, and accompanying software pipeline to standardize the capture and analysis of postharvest fruit images.
Reproduction assets foundThe paper explicitly states public availability of the annotated image datasets (USDA Ag Data Commons DOI), R analysis scripts, the BerryBox Python software package with pre-trained models, and the model training code, all with author-provided public URLs.Dataset · publics (LOD)
score at a particular marker exceeded that computed at the
α = 0.05 level under null models generated via 1000 random
permutations.
2.8 Data, software, and equipment
instruction availability
The image datasets, along with annotations, are publicly
available through the USDA National Agricultural Library
Ag Data Commons (https://doi.org/10.15482/USDA.ADC/29853332). All analyses in this study were performed in R (v.
4.5.0; R Core Team, 2025). Scripts to replicate the analyses,
along with a list of materials for recreating the Berry-
Box, are available from the GitHub repository https://github.com/neyhartj/BerryBox_FruitPhenotyping. Software for run-
ning the image capture and analysis Open asset ↗10.15482/USDA.ADC/29853332pdf-raw-page:8 lines:1-125Code · publicable through the USDA National Agricultural Library
Ag Data Commons (https://doi.org/10.15482/USDA.ADC/29853332). All analyses in this study were performed in R (v.
4.5.0; R Core Team, 2025). Scripts to replicate the analyses,
along with a list of materials for recreating the Berry-
Box, are available from the GitHub repository https://github.com/neyhartj/BerryBox_FruitPhenotyping. Software for run-
ning the image capture and analysis software pipeline is
available as a Python package from the GitHub reposi-
tory https://github.com/NeyhartLab/berryboxai. The package
includes pre-trained models for berry segmentation and fruit
rot detection, and the code is available from https://github.com/NOpen asset ↗github.com/neyhartj/BerryBox_FruitPhenotypingpdf-raw-page:8 lines:1-125Code · public). Scripts to replicate the analyses,
along with a list of materials for recreating the Berry-
Box, are available from the GitHub repository https://github.com/neyhartj/BerryBox_FruitPhenotyping. Software for run-
ning the image capture and analysis software pipeline is
available as a Python package from the GitHub reposi-
tory https://github.com/NeyhartLab/berryboxai. The package
includes pre-trained models for berry segmentation and fruit
rot detection, and the code is available from https://github.com/NeyhartLab/berryboxai_training_public for training a
custom model using high-performance computing resources
or the widely available Google Colab environment (Rippner
et al., 2022).
3 RESULTOpen asset ↗github.com/NeyhartLab/berryboxaipdf-raw-page:8 lines:1-125Code · publiceyhartj/BerryBox_FruitPhenotyping. Software for run-
ning the image capture and analysis software pipeline is
available as a Python package from the GitHub reposi-
tory https://github.com/NeyhartLab/berryboxai. The package
includes pre-trained models for berry segmentation and fruit
rot detection, and the code is available from https://github.com/NeyhartLab/berryboxai_training_public for training a
custom model using high-performance computing resources
or the widely available Google Colab environment (Rippner
et al., 2022).
3 RESULTS
3.1 Deep learning model training
The trained berry segmentation model achieved an overall
accuracy of 98.9% and an F1 score of 99.4%. The fruit rot
detection mOpen asset ↗github.com/NeyhartLab/berryboxai_training_publicpdf-raw-page:8 lines:1-125Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 14 Sept 2026
BlueberryFlowerGrowth / time-series analysisGrowth / development / phenology
Flowering in perennial crops is a trait influenced by genetics, environmental cues and plant vigor. Here, we studied the genetic and environmental control of repeat flowering (RF) in blueberry ( Vaccinium corymbosum ). RF was measured in a full-sib population from a cross between repeat and non-repeat flowering cultivars (‘Hortblue Petite’ and ‘Nui’, respectively). Longitudinal phenotypes were used to model the area under the curve for the first and second flowering peaks. We found that RF was strongly influenced by bush size and vigor, which we then incorporated into the area under the flowering curve models. Quantitative trait loci linked to both first bloom and RF were detected at three hotspots on chromosomes 4 and 10, and genes of interest known to regulate flowering under both temperature and photoperiod control were discussed. The phenotyping protocol and statistical modelling method reported here are an effective strategy for the investigation of complex interactions between multiple genetic loci and environmental variables on developmental traits, such as flowering. Experimental designs with replicated multi-environment and multi-year measurements are now needed to corroborate our results and further elucidate the determinism of RF in blueberry.
Why it matches plant phenotyping methodsブルーベリーの反復開花を連続的・定量的に測定し、開花曲線のモデリングを組み合わせた新規フェノタイピング手法とプロトコルが明示され、開花形質の遺伝解析における方法論的貢献が中心的です。
titlewith a novel continuous quantitative phenotyping approach
Blueberry production has rapidly expanded over the past decade, accompanied by growing demand for efficient and accurate methods to monitor the flowering and fruiting phases of blueberry development, which has a direct impact on yield potential. Accurate determination of blueberry phenology enables growers to make data-driven decisions on freeze protection applications and harvest windows. In addition, objective phenology data of blueberry mapping populations will provide high-quality phenotype data for the discovery of genetic mechanisms regulating blueberry flowering and fruiting times. Traditional approaches, such as manual counting and visual ratings, are labor-intensive and subjective in capturing variation across genotypes. Recent progress in computer vision and deep learning has enabled automated flower detection, but most existing studies on blueberries remain restricted to narrow flowering windows or close-up images, limiting their application at the bush level and across the seasonal development. In this study, we developed BerryFlowerNet, a customized YOLO-based model to detect and count blueberry flower clusters from bud to green fruit stages. A comprehensive dataset was collected on three dates using a field phenotyping robot, covering five flowering stages. The integration of CFNet, a custom module fusing shallow spatial features, and PIoU loss improved the detection performance. Additionally, the Slicing Aided Hyper Inference algorithm was employed to address small-object detection in bush-level images. Experimental results demonstrated that BerryFlowerNet outperformed the baseline YOLO model and three additional detectors, achieving an average mAP0.5 of 0.644 across five independent training runs. The model achieved an accuracy of 0.88 when predicting blueberry flowering stages, indicating its effectiveness and accuracy. Additionally, the results of the bush-level image analysis showed the capability of the model to capture genotype-level differences in flowering dynamics. Overall, this approach offers new opportunities for growers and breeders to determine blueberry phenological development that is critical for optimizing on-farm management strategies and advancing precision phenotyping to facilitate the development of climate-resilient blueberries.
Why it matches plant phenotyping methodsブルーベリーの花房検出・計数と開花ステージ推定を行うCNNおよびフィールド表現型ロボットの開発・評価が研究の中心であり、植物の生育状態を直接推定する実質的な表現型手法である。
abstractIn this study, we developed BerryFlowerNet, a customized YOLO-based model to detect and count blueberry flower clusters from bud to green fruit stages.
Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection. This perspective paper synthesizes current advances, identifies major barriers, and proposes future directions to realize the transformative potential of AI‐enabled plant phenomics. We first provide an overview of AI technologies with the potential to address key challenges in phenomics, from data collection to phenotypic trait extraction and environmental sensing. We then present three case studies focusing on specialty crops (blueberry [ Vaccinium corymbosum L.] mechanical harvestability traits, strawberry [ Fragaria × ananassa (Duchesne ex Weston)] production, and citrus [ Citrus L.] disease) to illustrate practical applications of AI‐driven phenomics. Moreover, we highlight future perspectives and opportunities for further research and innovation. These include large foundation models, real‐time inference on edge devices, explainable AI, generative AI and digital twins, AI‐enhanced multi‐omics, agentic AI, and knowledge‐guided and data‐driven hybrid approaches. Finally, we discuss key challenges and limitations of applying AI to plant phenomics, including data curation, model generalization and bias, and ethical considerations related to equitable access to AI tools.
Why it matches plant phenotyping methods植物フェノミクスにおけるAIセンシング・形質抽出を主題とする展望論文であり、方法論のレビューとして中心的に扱っている。
abstractWe first provide an overview of AI technologies with the potential to address key challenges in phenomics, from data collection to phenotypic trait extraction and environmental sensing.
This research addresses the critical need for efficient image annotation in precision agriculture, using the wild blueberry ( Vaccinium angustifolium Ait.) cropping system as a representative application to enable data-driven crop management. Tasks such as automated berry ripeness detection, plant disease identification, plant growth stage monitoring, and weed detection rely on extensive annotated datasets. However, manual annotation is labor-intensive, time-consuming, and impractical for large-scale agricultural systems. To address this challenge, this study evaluates an automated annotation pipeline that integrates zero-shot detection models from two frameworks (Grounding DINO and YOLO-World) with the Segment Anything Model version 2 (SAM2). The models were tested on detecting and segmenting ripe wild blueberries, developmental wild blueberry buds, hair fescue ( Festuca filiformis Pourr.), and red leaf disease ( Exobasidium vaccinii ). Grounding DINO consistently outperformed YOLO-World, with its Swin-T achieving mean Intersection over Union (mIoU) scores of 0.694 ± 0.175 for fescue grass and 0.905 ± 0.114 for red leaf disease when paired with SAM2-Large. For ripe wild blueberry detection, Swin-B with SAM2-Small achieved the highest performance (mIoU of 0.738 ± 0.189). Whereas for wild blueberry buds, Swin-B with SAM2-Large yielded the highest performance (0.751 ± 0.154). Processing times were also evaluated, with SAM2-Tiny, Small, and Base demonstrating the shortest durations when paired with Swin-T (0.30-0.33 s) and Swin-B (0.35-0.38 s). SAM2-Large, despite higher segmentation accuracy, had significantly longer processing times (significance level α = 0.05), making it less practical for real-time applications. This research offers a scalable solution for rapid, accurate annotation of agricultural images, improving targeted crop management. Future research should optimize these models for different cropping systems, such as orchard-based agriculture, row crops, and greenhouse farming, and expand their application to diverse crops to validate their generalizability.
Why it matches plant phenotyping methods植物の果実・芽・病徴を対象に、ゼロショット検出とSAM2による検出・セグメンテーション注釈パイプラインを開発・評価しており、植物表現型の画像取得・抽出手法が研究の中心である。
abstractThe models were tested on detecting and segmenting ripe wild blueberries, developmental wild blueberry buds, hair fescue ( Festuca filiformis Pourr.), and red leaf disease ( Exobasidium vaccinii ).
Anthracnose, caused by Colletotrichum gloeosporioides, poses a significant threat to blueberries, necessitating a deeper understanding of the genetic mechanisms underlying resistance to develop efficient breeding strategies. Here, we conducted a genome-wide association study on 355 advanced selections of southern highbush blueberry from the University of Florida Blueberry Breeding and Genomics Program. Visual scores and image analyses were used for assessing disease severity. The population was genotyped using Capture-Seq, detecting 38,379 single nucleotide polymorphisms. The study revealed a moderate narrow-sense heritability estimate (∼0.5) for anthracnose resistance in blueberries. Minor additive loci contributing to anthracnose resistance were identified on chromosomes 2, 3, 5, 6, 9, 10, and 12, using 2 different phenotyping approaches. Visual and image-based phenotyping captured complementary aspects of anthracnose resistance, identifying distinct, non-overlapping SNP associations. Candidate gene mining flanking significant associations unveiled key defense-related proteins, such as serine/threonine protein kinases, pentatricopeptide repeat-containing proteins, E3 ubiquitin ligases that have been well-known for their roles in plant defense signaling pathways. Our findings highlight the complex and quantitative resistance mechanism for anthracnose in blueberry, providing insights for breeding strategies and sustainable disease management.
Why it matches plant phenotyping methodsブルーベリーの炭疽病抵抗性を対象に、視覚評価と画像解析による病害重症度推定を2つの表現型評価法として比較・適用し、相補的な抵抗性情報を抽出しているため、画像ベース表現型取得が研究の実質的要素です。
abstractVisual scores and image analyses were used for assessing disease severity.
This paper presents a robotic system designed to replace manual labour in three prevalent activities related to blueberry management: soil sampling and analysis, weed spraying, and plant health status monitoring. A complex system for automating field activities in blueberry orchards that involves the use of ground and aerial robots, along with integrated route optimisation software, autonomous driving, image recognition based on AI, and others, was developed. The ground robot is made in a modular way, having three different add-on modules for the three distinct use cases it covers. A modular system guarantees the year-round utilisation of the robot across various growth stages of plants. This stands out as its major advantage, considering that many robotic systems are typically tailored to address a singular task. Robot modules utilise custom-made hardware for in-field sampling and real-time soil analysis, an industrial robotic arm with a custom-made system for spraying, and a plant health monitoring module consisting of two Plant-O-Meter™ optical devices capable of capturing sixteen optical vegetation indices in real-time. The solutions are tested and deployed in the real-world environment of the blueberry orchard. We have achieved 50–60 % savings on herbicides compared to blanket spraying, and the costs related to sub-optimal herbicide application are reduced up to 25 %. The costs of soil analysis have been significantly reduced to about 70 %.
Why it matches plant phenotyping methodsブルーベリー園向けロボット基盤の主要モジュールとして、植物健康状態を光学センサーでリアルタイム測定する方法を実環境で展開・評価しており、植物状態の取得が技術的構成の中心的要素の一つである。
abstracta plant health monitoring module consisting of two Plant-O-Meter™ optical devices capable of capturing sixteen optical vegetation indices in real-time
(1) Background: Monilinia and Botrytis blight are significant diseases affecting wild blueberry fields, leading to substantial yield losses. Traditional methods for disease assessment rely on destructive sampling, which is labor-intensive and subjective. This study explored the use of multispectral and hyperspectral sensors through simple and machine learning approaches to detect and assess Monilinia and Botrytis blight diseases. (2) Methods: In this study, we adopted two experimental approaches: plot and patch assessment trials. These were conducted using a randomized complete block design at three locations in Nova Scotia. Disease detection was performed using vegetative indices (VIs) and spectral reflectance analysis, with destructive samples also assessed. Analysis of variance, correlations and classification approaches were used in the analysis. (3) Results: Significant spectral differences were observed between healthy and diseased plants, particularly in the near-infrared region (715–1050 nm). Nine significant wavelength bands were identified for blight disease detection. Classifier analysis revealed that support vector machines (SVM) and random forests (RF) outperformed k-nearest neighbors (KNN), achieving an overall accuracy of 96.6% and 76.8% in the broad and severity disease level classifications. (4) Conclusions: Despite some limitations, these findings underscore the potential of remote sensing tools for efficient, non-destructive disease management in wild blueberry fields.
Why it matches plant phenotyping methods野生ブルーベリーの病徴・重症度をマルチスペクトル／ハイパースペクトルセンサーと分類手法で非破壊的に検出・評価する方法が研究の中心であり、植物病害状態の表現型計測に該当する。
abstractThis study explored the use of multispectral and hyperspectral sensors through simple and machine learning approaches to detect and assess Monilinia and Botrytis blight diseases.
Fruit shape and stomatal distribution influence fruit water status and postharvest quality in southern highbush blueberry ( Vaccinium corymbosum hybrids). This study developed scalable phenotyping methods to quantify fruit morphology and stomatal traits in cultivars Colossus and Optimus across four developmental stages. Fruit volume was estimated using geometric models validated against three-dimensional (3D) scans, with the spheroid model offering the best compromise between accuracy and efficiency ( R 2 = 0.96). StoManager1 software was validated for automated stomatal phenotyping, showing strong concordance with manual counts ( R 2 = 0.96). Results revealed cultivar-specific differences in shape development and stomatal distribution. As fruits matured, both cultivars exhibited increases in volume and surface area with decreasing sphericity. Stomata were localized primarily to distal regions of the fruit, particularly the calyx, suggesting heterogeneous water loss pathways across the fruit surface. These findings establish a framework for integrating morphometric and anatomical traits into high-throughput phenotyping pipelines and future studies on fruit water relations.
Why it matches plant phenotyping methods果実形態と気孔形質を定量化するスケーラブルな表現型解析法を開発し、3Dスキャンおよび手動計数で検証しており、方法開発・検証が研究の中心である。
abstractThis study developed scalable phenotyping methods to quantify fruit morphology and stomatal traits
We present an AI pipeline that involves using smart drones equipped with computer vision to obtain a more accurate fruit count and yield estimation of the number of blueberries in a field. The core components are two object-detection models based on the YOLO deep learning architecture: a Bush Model that is able to detect blueberry bushes from images captured at low altitudes and at different angles, and a Berry Model that can detect individual berries that are visible on a bush. Together, both models allow for more accurate crop yield estimation by allowing intelligent control of the drone's position and camera to safely capture side-view images of bushes up close. In addition to providing experimental results for our models, which show good accuracy in terms of precision and recall when captured images are cropped around the foreground center bush, we also describe how to deploy our models to map out blueberry fields using different sampling strategies, and discuss the challenges of annotating very small objects (blueberries) and difficulties in evaluating the effectiveness of our models.
Why it matches plant phenotyping methodsドローン画像とYOLOモデルによりブルーベリー果実数および収量を推定する手法を開発・評価しており、植物形質の取得・推定が研究の中心である。
abstractWe present an AI pipeline that involves using smart drones equipped with computer vision to obtain a more accurate fruit count and yield estimation of the number of blueberries in a field.
Accurate blueberry fruit phenotyping, including yield, fruit maturity, and cluster compactness, is crucial for optimizing crop breeding and management practices. Recent advances in machine vision and deep learning have shown promising potential to automate phenotyping and replace manual sampling. This paper presented a robotic blueberry phenotyping system, called MARS-Phenobot, that collects data in the field and measures fruit-related phenotypic traits such as fruit number, maturity, and compactness. Our workflow comprised four components: a robotic multi-view imaging system for high-throughput data collection, a vision foundation model (Segment Anything Model, SAM) for mask-free data labeling, a customized BerryNet deep learning model for detecting blueberry clusters and segmenting fruit, as well as a post-processing module for estimating yield, maturity, and cluster compactness. A customized deep learning model, BerryNet, was designed for detecting fruit clusters and segmenting individual berries by integrating low-level pyramid features, rapid partial convolutional blocks, and BiFPN feature fusion. It outperformed other networks and achieved mean average precision (mAP50) of 54.9 % in cluster detection and 85.8 % in fruit segmentation with fewer parameters and fewer computation requirements. We evaluated the phenotypic traits derived from our methods and the ground truth on 26 individual blueberry plants across 17 genotypes. The results demonstrated that both the fruit count and cluster count extracted from images were strongly correlated with the yield. Integrating multi-view fruit counts enhanced yield estimation accuracy, achieving a Mean Absolute Percentage Error (MAPE) of 23.1 % and the highest R² value of 0.73, while maturity level estimations closely aligned with manual calculations, exhibiting a Mean Absolute Error (MAE) of approximately 5 %. Furthermore, two metrics related to fruit compactness were introduced, including cluster compactness and fruit distance, which could be useful for breeders to assess the machine and hand harvestability across genotypes. Finally, we evaluated the proposed robotic blueberry fruit phenotyping pipeline on eleven blueberry genotypes, proving the potential to distinguish the high-yield, early-maturity, and loose-clustering cultivars. Our methodology provides a promising solution for automated in-field blueberry fruit phenotyping, potentially replacing labor-intensive manual sampling. Furthermore, this approach could advance blueberry breeding programs, precision management, and mechanical/robotic harvesting.
Why it matches plant phenotyping methodsロボット多視点撮像、深層学習による果実抽出、収量・成熟度・房密集度の推定を統合した植物表現型計測パイプラインが研究の中心であり、精度検証も実施している。
abstractThis paper presented a robotic blueberry phenotyping system, called MARS-Phenobot, that collects data in the field and measures fruit-related phenotypic traits such as fruit number, maturity, and compactness.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 14 Sept 2026
SUMMARY Successful fertilization of insect‐pollinated crops hinges on a delicate interplay of olfactory and visual signals of pollinator attraction, the chemical complexity of nectar and pollen rewards, and the physical interaction between insects and flower anatomy for efficient pollen transfer. These traits, which are controlled genetically and exhibit phenotypic variance even within species, present opportunities for breeding technologies to map and select genotypes with floral traits that actively guide pollinator preferences. Recent technological advancements and automation have enabled high‐throughput metabolic phenotyping of floral chemical traits of pollinator attraction and rewards. These measurements, when integrated with computed tomography (CT) scans of flower shape analysis and video tracking of pollinator behavior, can guide the selection of genotypes with enhanced insect visitation rates and effective cross‐pollination. In this perspective article, we highlight the potential of this strategy for blueberry ( Vaccinium corymbosum L.), a crop heavily reliant on bee pollination for fruit production and with flowers that display considerable variance of chemical and morphological traits and pollinator visitation rates. Leveraging blueberry's genetic diversity can address pollination issues exacerbated by global warming and declining health of managed bees, thus contributing to a more sustainable agricultural production.
Why it matches plant phenotyping methods花の化学・形態・訪花行動を高スループットに測定し、CT解析と動画追跡を統合する植物表現型評価戦略を中心に論じる展望論文であり、方法論的役割が明確。
abstractRecent technological advancements and automation have enabled high‐throughput metabolic phenotyping of floral chemical traits of pollinator attraction and rewards.
Blueberry scorch virus (BIScV), transmitted by aphids, causes a serious disease in highbush blueberries with a significant economic impact. Early detection and mapping of the distribution of BIScV infected plants in fields are critical to implementing effective disease management practices, such as the timely removal of infected bushes and control of aphid vectors. The conventional visual plant assessment for symptoms remains dominant in BIScV detections, though it is labor-intensive, time-consuming, and costly. In recent years, the use of remote sensing techniques has become popular for in-field assessments of crop diseases and insect pests incidence, and thus provides an effective approach for detecting and mapping BIScV infections. Convolutional Neural Networks (CNNs) are among the most widely employed algorithms in remote sensing image classification. However, CNNs have some limitations in their ability to obtain global information dependency due to the convolution’s constrained receptive field in each layer. To address this challenge, the self-attention mechanism utilized in Vision Transformers (ViTs) was suggested in previous studies for achieving flexible global information dependency through facilitating communication among arbitrary pixels in images. As such, we developed a CNN-ViT-based deep learning algorithm (named “Scorch Mapper”), a pixel-based classifier, that utilizes both the functionality and capabilities of CNNs in capturing local visual characteristics and ViTs for acquiring long-range information dependency for the mapping of BIScV. We also compared the developed Scorch Mapper to several other CNN– and ViT-based algorithms, including a 2D CNN, ResNet, HybridSN, Swin Transformer, Efficient Net, CMT, InFormer, and Efficient Former. Our results demonstrated the superiority of the Scorch Mapper compared to other CNN– and ViT-based algorithms. Research findings also show that the Scorch Mapper is effective and can be applied over a wide area to support BIScV mapping and monitoring. Furthermore, the developed model opens a new window for future automatic BIScV mapping utilizing cutting-edge remote sensing algorithms and technologies.
Why it matches plant phenotyping methodsUAV画像から感染植物の病害状態を推定・マッピングする深層学習手法を開発し、複数モデルと比較検証しており、植物フェノタイピング手法が中心である。
abstractwe developed a CNN-ViT-based deep learning algorithm (named “Scorch Mapper”), a pixel-based classifier, that utilizes both the functionality and capabilities of CNNs in capturing local visual characteristics and ViTs for acquiring long-range information dependency for the mapping of BIScV.
The increasing popularity of blueberry has led to expanded blueberry production in many parts of the world. Berry size and average berry weight are key factors in determining the price and marketability of blueberries and therefore are important traits for breeders and researchers to evaluate. Manual measurement of berry size and average berry weight is labor-intensive and prone to human error. This study developed an automated algorithm and smartphone application for accurate blueberry count and size estimation. Two different computer vision pipelines based on traditional methods and deep neural networks were implemented to detect and segment individual blueberries from Red-Green-Blue (RGB) images. The first pipeline used traditional algorithms such as Hough Transform, Watershed, and filtering. The second pipeline deployed YOLOv5 models with additional modifications using the Ghost module and bi-Feature Pyramid Network (biFPN). A total of 198 images of blueberries, together with manually measured berry count and average berry weight, were used to train and test the model performance. The YOLOv5-based model miscounted four berries in 4,604 total berries across the 198 images. The mean average precision was 92.3%, averaged across an intersection-over-union threshold between 0.50−0.95. The model-derived average berry size was highly correlated with measured average berry weight (R2 > 0.93), which translated to a mean absolute error of around 0.14 g (8.3%). An Android application was also developed in this study to allow easier access to implemented models for berry size and weight phenotyping.
Why it matches plant phenotyping methodsブルーベリーの個数・サイズ・重量を画像から推定するコンピュータビジョン手法とスマートフォンアプリを開発・検証しており、植物形質取得が研究の中心である。
abstractThis study developed an automated algorithm and smartphone application for accurate blueberry count and size estimation.
Drought-induced stress significantly impacted blueberry production due to the plants’ inefficient water regulation mechanisms to maintain yield and fruit quality under drought stress. Traditional methods of manual phenotyping for drought stress are not only time-consuming but also labor-intensive. To address the need for accurate and large-scale assessment of drought tolerance, we developed a high-throughput phenotyping (HTP) system to capture hyperspectral images of blueberry plants under drought conditions. A novel transformer-based model, LWC-former was introduced to predict leaf water content (LWC) utilizing spectral reflectance from hyperspectral images obtained from the developed HTP system. The LWC-former transformed the spectral reflectance into patch representations and embedded these patches into a lower dimensional to address multicollinearity issues. These patches were then passed to the transformer encoder to learn distributed features, followed by a regression head to predict LWC. To train the model, spectral reflectance data were extracted from hyperspectral images and pre-processed using log(1/R), mean scatter correction (MSC), and mean centering (MC). The results showed that our model achieved a coefficient of determination (R²) of 0.81 on the test dataset. The performance of the proposed model was also compared with TabTransformer, DeepRWC, multilayer perceptron (MLP), partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF), achieving R² values of 0.65, 0.73, 0.71, 0.47, and 0.58, respectively. The results demonstrated that LWC-former outperformed other deep learning and statistical-based models. The high-throughput phenotyping system effectively facilitated large-scale data collection, while the LWC-former model addressed multicollinearity issues, significantly improving the prediction of LWC. These results demonstrate the potential of our approach for large-scale drought tolerance assessment in blueberries.
Why it matches plant phenotyping methodsブルーベリーの高スループット hyperspectral imaging と、画像スペクトルから葉水分含量を推定するTransformerモデルを開発・比較評価しており、植物表現型取得・抽出手法が研究の中心である。
abstractwe developed a high-throughput phenotyping (HTP) system to capture hyperspectral images of blueberry plants under drought conditions.
• Developed a high-throughput phenotyping platform with hyperspectral imaging. • Introduced Plant-GCN, for estimating physiological traits in blueberries plants. • Plant-GCN achieved high accuracy (R² > 0.90) for predicting physiological traits. • Outperformed traditional statistical and deep learning models. Blueberries are extremely susceptible to drought due to their shallow root systems and limited water regulation capabilities. Climate change exacerbates drought stress in major blueberry production regions, which affect key physiological traits, such as leaf water content (LWC), photosynthesis (A), stomatal conductance (g s ), electron transport rate (ETR), photosystem II efficiency (φPSII) and transpiration rate (E). Current phenotyping methods for measuring these physiological traits are time-consuming and labor-intensive as well as limited by the need for specialized equipment. To address this, a high-throughput phenotyping (HTPP) platform integrated with hyperspectral camera and a novel graph convolutional network (GCN)-based model, Plant-GCN, was developed to predict physiological traits of blueberry plants under drought stress. Spectral reflectance obtained from the hyperspectral images were transformed into a graph representation, with each plant represented as a node, spectral reflectance as node features, and edges defined by spectral similarities. The Plant-GCN model utilizes graph convolutional layers that aggregate information from neighboring nodes, effectively capturing complex interactions in the spectral signature and enhancing the prediction of physiological traits. Plant-GCN achieved a coefficient of determination (R²) of 0.89 for LWC, 0.94 for A, 0.89 for g s , 0.92 for ETR, 0.93 for φPSII and 0.89 for E on the test dataset. The performance of the proposed Plant-GCN model was compared with multilayer perceptron (MLP), partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF), and it consistently outperformed all these models as well as data published in other reports. The high-throughput phenotyping system enabled efficient large-scale data collection, while the Plant-GCN model captured long-range spectral relationships significantly improved the prediction of physiological traits. The high predictability of the models could facilitate the screening of blue-berry cultivars for the specified traits allowing the selection and breeding of new drought tolerant cultivars in the future.
Why it matches plant phenotyping methods高スループット・ハイパースペクトル画像基盤とPlant-GCNによるブルーベリー生理形質推定を開発・比較検証しており、植物フェノタイピング手法が中心である。
abstractDeveloped a high-throughput phenotyping platform with hyperspectral imaging.
In precision agriculture (PA), monitoring individual plant health is crucial for optimizing yields and minimizing resources. The normalized difference vegetation index (NDVI), a widely used health indicator, typically relies on expensive multispectral cameras. This study introduces a method for predicting the NDVI of blueberry plants using RGB images and deep learning, offering a cost-effective alternative. To identify individual plant bushes, K-means and Gaussian Mixture Model (GMM) clustering were applied. RGB images were transformed into the HSL (hue, saturation, lightness) color space, and the hue channel was constrained using percentiles to exclude extreme values while preserving relevant plant hues. Further refinement was achieved through adaptive pixel-to-pixel distance filtering combined with the Davies–Bouldin Index (DBI) to eliminate pixels deviating from the compact cluster structure. This enhanced clustering accuracy and enabled precise NDVI calculations. A convolutional neural network (CNN) was trained and tested to predict NDVI-based health indices. The model achieved strong performance with mean squared losses of 0.0074, 0.0044, and 0.0021 for training, validation, and test datasets, respectively. The test dataset also yielded a mean absolute error of 0.0369 and a mean percentage error of 4.5851. These results demonstrate the NDVI prediction method’s potential for cost-effective, real-time plant health assessment, particularly in agrobotics.
Why it matches plant phenotyping methodsRGB画像からブルーベリー個体のNDVIベース健康指標を推定する画像処理・深層学習手法の開発であり、植物状態の取得・推定が研究の中心です。
abstractThis study introduces a method for predicting the NDVI of blueberry plants using RGB images and deep learning, offering a cost-effective alternative.
Time to maturity and yield are important traits for highbush blueberry (Vaccinium corymbosum) breeding. Proper determination of the time to maturity of blueberry varieties and breeding lines informs the harvest window, ensuring that the fruits are harvested at optimum maturity and quality. On the other hand, high-yielding crops bring in high profits per acre of planting. Harvesting and quantifying the yield for each blueberry breeding accession are labor-intensive and impractical. Instead, visual ratings as an estimation of yield are often used as a faster way to quantify the yield, which is categorical and subjective. In this study, we developed and shared a high-throughput phenotyping method using neural networks to predict blueberry time to maturity and to provide a proxy for yield, overcoming the labor constraints of obtaining high-frequency data. We aim to facilitate further research in computer vision and precision agriculture by publishing the labeled image dataset and the trained model. In this research, true-color images of blueberry bushes were collected, annotated, and used to train a deep neural network object detection model [You Only Look Once (YOLOv11)] to detect mature and immature berries. Different versions of YOLOv11 were used, including nano, small, and medium, which had similar performance, while the medium version had slightly higher metrics. The YOLOv11m model shows strong performance for the mature berry class, with a precision of 0.90 and an F1 score of 0.90. The precision and recall for detecting immature berries were 0.81 and 0.79. The model was tested on 10 blueberry bushes by hand harvesting and weighing blueberries. The results showed that the model detects approximately 25% of the berries on the bushes, and the correlation coefficients between model-detected and hand-harvested traits were 0.66, 0.86, and 0.72 for mature fruit count, immature fruit count, and mature ratio, respectively. The model applied to 91 blueberry advance selections and categorized them into groups with diverse levels of maturity and productivity using principal component analysis (PCA). These results inform the harvest window and yield of these breeding lines with precision and objectivity through berry classification and quantification. This model will be helpful for blueberry breeders, enabling more efficient selection, and for growers, helping them accurately estimate optimal harvest windows. This open-source tool can potentially enhance research capabilities and agricultural productivity.
Why it matches plant phenotyping methodsブルーベリーの成熟度・収量 proxy を画像とニューラルネットワークで推定する高スループット表現型計測法を開発・検証し、モデルとラベル付きデータセットを共有しているため、方法が研究の中心である。
abstractwe developed and shared a high-throughput phenotyping method using neural networks to predict blueberry time to maturity and to provide a proxy for yield
Reproduction assets foundThe paper publishes its labeled blueberry image dataset on Zenodo (record 14014858) and its trained YOLOv11-based blueberry fruit counting model/code on GitHub (jeromemaleski/blueberry), both directly supporting the paper's phenotyping measurements and analysis.Dataset · public32. Zhang, J. Blueberry Images and Labels for YOLO Model Training. Zenodo. 2024. Available online: https://zenodo.org/records/Open asset ↗zenodopdf-page:14 lines:1-36Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Introduction Mechanical damage significantly reduces the market value of fruits, making the early detection of such damage a critical aspect of agricultural management. This study focuses on the early detection of mechanical damage in blueberries (variety: Sapphire) through a non-destructive method. Methods The proposed method integrates hyperspectral image fusion with a multi-strategy improved support vector machine (SVM) model. Initially, spectral features and image features were extracted from the hyperspectral information using the successive projections algorithm (SPA) and Grey Level Co-occurrence Matrix (GLCM), respectively. Different models including SVM, RF (Random Forest), and PLS-DA (Partial Least Squares Discriminant Analysis) were developed based on the extracted features. To refine the SVM model, its hyperparameters were optimized using a multi-strategy improved Beluga Whale Optimization (BWO) algorithm. Results The SVM model, upon optimization with the multi-strategy improved BWO algorithm, demonstrated superior performance, achieving the highest classification accuracy among the models tested. The optimized SVM model achieved a classification accuracy of 95.00% on the test set. Discussion The integration of hyperspectral image information through feature fusion proved highly efficient for the early detection of bruising in blueberries. However, the effectiveness of this technology is contingent upon specific conditions in the detection environment, such as light intensity and temperature. The high accuracy of the optimized SVM model underscores its potential utility in post-harvest assessment of blueberries for early detection of bruising. Despite these promising results, further studies are needed to validate the model under varying environmental conditions and to explore its applicability to other fruit varieties.
Why it matches plant phenotyping methodsブルーベリー果実の打撲状態を、ハイパースペクトル画像から特徴抽出・情報融合し、最適化SVMで非破壊的に判定する手法が研究の中心であるため、植物状態の画像ベース表現型計測に該当する。
abstractThis study focuses on the early detection of mechanical damage in blueberries (variety: Sapphire) through a non-destructive method.
Introduction Mummy berry is a serious disease that may result in up to 70 percent of yield loss for lowbush blueberries. Practical mummy berry disease detection, stage classification and severity estimation remain great challenges for computer vision-based approaches because images taken in lowbush blueberry fields are usually a mixture of different plant parts (leaves, bud, flowers and fruits) with a very complex background. Specifically, typical problems hindering this effort included data scarcity due to high manual labelling cost, tiny and low contrast disease features interfered and occluded by healthy plant parts, and over-complicated deep neural networks which made deployment of a predictive system difficult. Methods Using real and raw blueberry field images, this research proposed a deep multi-task learning (MTL) approach to simultaneously accomplish three disease detection tasks: identification of infection sites, classification of disease stage, and severity estimation. By further incorporating novel superimposed attention mechanism modules and grouped convolutions to the deep neural network, enabled disease feature extraction from both channel and spatial perspectives, achieving better detection performance in open and complex environments, while having lower computational cost and faster convergence rate. Results Experimental results demonstrated that our approach achieved higher detection efficiency compared with the state-of-the-art deep learning models in terms of detection accuracy, while having three main advantages: 1) field images mixed with various types of lowbush blueberry plant organs under a complex background can be used for disease detection; 2) parameter sharing among different tasks greatly reduced the size of training samples and saved 60% training time than when the three tasks (data preparation, model development and exploration) were trained separately; and 3) only one-sixth of the network parameter size (23.98M vs. 138.36M) and one-fifteenth of the computational cost (1.13G vs. 15.48G FLOPs) were used when compared with the most popular Convolutional Neural Network VGG16. Discussion These features make our solution very promising for future mobile deployment such as a drone carried task unit for real-time field surveillance. As an automatic approach to fast disease diagnosis, it can be a useful technical tool to provide growers real time disease information that can prevent further disease transmission and more severe effects on yield due to fruit mummification.
Why it matches plant phenotyping methodsブルーベリーの病害感染部位、病期、重症度を圃場画像から推定する深層マルチタスク手法を開発・評価しており、植物病態の表現型取得が研究の中心である。
abstractExperimental results demonstrated that our approach achieved higher detection efficiency compared with the state-of-the-art deep learning models in terms of detection accuracy
Precise yield estimation is important for phenotyping and crop management but is challenging for large-scale and high-density crops, such as wild blueberries. Machine learning (ML) technology and unmanned aerial vehicle (UAV) platforms created opportunities for more accurate and efficient yield estimation. RGB cameras are widely used in image processing due to their low cost, easy operation, and data analysis advantages. Here, we aimed to identify an effective, accurate, low-cost, and practical yield prediction method that can be applied to wild blueberry fields and potentially other high-density crops. Deep learning (DL) was overlooked due to its data-intensive training requirements, and the acquisition of image and yield data for wild blueberries is time-consuming and difficult. We evaluated the feasibility of using RF (Random Forest) and XGBoost (Extreme Gradient Boosting) models based on color and texture feature data obtained from UAV RGB images to predict wild blueberry yield, and tested the effect of flight altitude, sampling method, and ML model. We found that the XGBoost model trained on a systematically sampled dataset built from high-altitude color and texture features achieved the best prediction performance (R² = 0.89, RMSE = 542.01, and MAE = 379.94). Interestingly, images captured at a higher altitude (30 m) performed better compared to that at lower altitudes (5 and 15 m), which provides clear evidence supporting higher and more efficient UAV cruise operations. Our study provides a non-destructive, efficient, fast, and easy-to-use yield prediction method for large-scale and high-density crops, confirming its feasibility with wild blueberries.
Why it matches plant phenotyping methodsUAV画像から色・テクスチャ特徴を抽出し、機械学習で野生ブルーベリー収量を推定する手法の開発・評価が中心であり、植物表現型の取得方法を技術的に検証している。
abstractWe evaluated the feasibility of using RF (Random Forest) and XGBoost (Extreme Gradient Boosting) models based on color and texture feature data obtained from UAV RGB images to predict wild blueberry yield, and tested the effect of flight altitude, sampling method, and ML model.
We present a sensitive method for non-contact detection of chloroplast movements in leaves and other photosynthetic tissues, based on changes in the magnitude of cross-polarized reflectance. We examined changes in bidirectional red light reflectance during irradiation with blue light, known to trigger chloroplast relocations. Experiments on the model plant Arabidopsis thaliana , wild-type, and several mutants with disrupted chloroplast movements showed that the chloroplast avoidance response, induced by high blue light, led to a substantial increase in diffuse reflectance of unpolarized red light. The effects of the accumulation response in low blue light were the opposite. The specular reflectance of the leaf was unaffected by the chloroplast positioning. To further improve the specificity of the detection, we examined the effects of chloroplast relocations on the leaf reflectance of a linearly polarized incident beam. The greatest relative change associated with chloroplast movements was observed when the planes of polarization of the incident and detected beams were perpendicular. Further experiments revealed that the chloroplast positioning affected the magnitude of depolarization of light by the leaf. We applied the developed approach to examine chloroplast relocations in four angiosperm species collected in the field. The method allowed us to detect the chloroplast avoidance response in the green stems of bilberry, a sample not amenable to transmittance-based detection. Despite the importance of chloroplast movements for the optimization of photosynthetic efficiency and biomass production, high throughput reflectance-based methods are not routinely used for their detection. This method opens the possibility of non-invasive, non-contact detection of chloroplast relocations in a manner insensitive to the orientation of the leaf.
Why it matches plant phenotyping methods葉のクロロプラスト移動という植物状態を、偏光反射によって非接触・非侵襲的に検出する手法を開発し、複数種で適用しているため、植物フェノタイピング手法が研究の中心である。
abstractWe present a sensitive method for non-contact detection of chloroplast movements in leaves and other photosynthetic tissues, based on changes in the magnitude of cross-polarized reflectance.
Reproduction assets foundThe paper's Data availability statement openly deposits the paper's own reflectance/transmittance phenotype recordings (Arabidopsis WT/mutants and wild plants) on FigShare, and provides authors' public code: BeamJ (Java control software for the phenotyping setup) and openRayTracer (Mathematica ray-tracing package used,Dataset · publiced on the manuscript.
Conflict of interest
The authors declare no conflict of interest.
Funding
This study was supported by the National Science Centre Poland within the MINIATURA 4
project to P.H., number 2020/04/X/NZ4/01256.
Data availability
The data that support the findings of this study are openly available in FigShare at
https://doi.org/10.6084/m9.figshare.21082654 (reflectance and transmittance recordings for
Arabidopsis WT and mutants) and https://doi.org/10.6084/m9.figshare.24424843 (wild
plants). Java source code for the software is publicly available via GitHub at
https://github.com/pawelHerm/beamJ/tree/master/BeamJ. Wolfram Mathematica package for
ray tracing is available at httOpen asset ↗FigShare · 10.6084/m9.figshare.21082654pdf-raw-page:14 lines:1-47Dataset · publicthe National Science Centre Poland within the MINIATURA 4
project to P.H., number 2020/04/X/NZ4/01256.
Data availability
The data that support the findings of this study are openly available in FigShare at
https://doi.org/10.6084/m9.figshare.21082654 (reflectance and transmittance recordings for
Arabidopsis WT and mutants) and https://doi.org/10.6084/m9.figshare.24424843 (wild
plants). Java source code for the software is publicly available via GitHub at
https://github.com/pawelHerm/beamJ/tree/master/BeamJ. Wolfram Mathematica package for
ray tracing is available at https://github.com/plantPhotobiologyLab/openRayTracer.References
Banaś, A. K., Aggarwal, C., Łabuz, J., Sztatelman, O., Gabryś,Open asset ↗FigShare · 10.6084/m9.figshare.24424843pdf-raw-page:14 lines:1-47Code · publicthat support the findings of this study are openly available in FigShare at
https://doi.org/10.6084/m9.figshare.21082654 (reflectance and transmittance recordings for
Arabidopsis WT and mutants) and https://doi.org/10.6084/m9.figshare.24424843 (wild
plants). Java source code for the software is publicly available via GitHub at
https://github.com/pawelHerm/beamJ/tree/master/BeamJ. Wolfram Mathematica package for
ray tracing is available at https://github.com/plantPhotobiologyLab/openRayTracer.References
Banaś, A. K., Aggarwal, C., Łabuz, J., Sztatelman, O., Gabryś, H. (2012). Blue light
signalling in chloroplast movements. Journal of Experimental Botany, 63(4), 1559–
1574.
Baránková, B., LazáOpen asset ↗GitHub · pawelHerm/beamJpdf-raw-page:14 lines:1-47Code · publicuorescence. The filtered light was focused on a
photodetector (amplified silicon photodiode, PDA100A2, Thorlabs) with a plano-convex lens
(LA1074-A, Thorlabs). The angular size of the clear aperture of the collecting lens with
respect to the sample center was 0.019 steradian (calculated using our ray-tracing
Mathematica package https://github.com/plantPhotobiologyLab/openRayTracer). To control
the observation angle, the detector was mounted at the RBB300A/M rotation board
(Thorlabs). The experiments were performed with two angular positions of the polarizer: its
transmission axis was either parallel (transmits P) or perpendicular (transmits S component)
to the plane of incidence. The LEDs suOpen asset ↗GitHub · plantPhotobiologyLab/openRayTracerpdf-raw-page:6 lines:1-45Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Xylella fastidiosa , a gram-negative bacterium vectored to plants via feeding of infected insects, causes a number of notorious plant diseases throughout the world, such as Pierce's disease (grapes), olive quick decline syndrome, and coffee leaf scorch. Detection of Xf in infected plants can be challenging because the early foliar disease symptoms are subtle and may be attributed to multiple minor physiological stresses and/or borderline nutrient deficiencies. Furthermore, Xf may reside within an infected plant for one or more growing seasons before traditional visible diagnostic disease symptoms emerge. Any method that can identify infection during the latent period or pre-diagnostic disease progress state could substantially improve the outcome of disease control interventions. Because Xf locally and gradually impairs water movement through infected plant stems and leaves over time, infected plants may not be able to effectively dissipate heat through transpiration-assisted cooling, and this heat signature may be an important pre-diagnostic disease trait. Here, we report on the association between thermal imaging, the early stages of Xf infection, and disease development in blueberry plants, and discuss the benefits and limitations of using thermal imaging to detect bacterial leaf scorch of blueberries.
Why it matches plant phenotyping methods熱画像を用いてブルーベリーの感染初期状態と病害進展を検出する方法を評価しており、植物病態の表現型取得が研究の中心である。
abstractHere, we report on the association between thermal imaging, the early stages of Xf infection, and disease development in blueberry plants, and discuss the benefits and limitations of using thermal imaging to detect bacterial leaf scorch of blueberries.
Hyperspectral imaging combined with chemometric approaches is proven to be a powerful tool for the quality evaluation and control of fruits. In fruit defect-detection scenarios, developing an unsupervised anomaly detection framework is vital, as defect sample preparation is labor-intensive and time-consuming, especially for exploring potential defects. In this paper, a spectral-spatial, information-based, self-supervised anomaly detection (SSAD) approach is proposed. During training, an auxiliary classifier is proposed to identify the projection axes of principal component (PC) images that were transformed from the hyperspectral data cubes. In test time, the fully connected layer of the learned classifier was used as a 'spectral-spatial' feature extractor, and the feature similarity metric was adopted as the score function for the downstream anomaly evaluation task. The proposed network was evaluated with two fruit data sets: a strawberry data set with bruised, infected, chilling-injured, and contaminated test samples and a blueberry data set with bruised, infected, chilling-injured, and wrinkled samples as anomalies. The results show that the SSAD yielded the best anomaly detection performance (AUC = 0.923 on average) over the baseline methods, and the visualization results further confirmed its advantage in extracting effective 'spectral-spatial' latent representation. Moreover, the robustness of SSAD is verified with the data pollution experiment; it performed significantly better than the baselines when a portion of anomalous samples was involved in the training process.
Why it matches plant phenotyping methods果実の病害・損傷・低温障害などの状態をハイパースペクトル画像から検出する手法を開発・評価しており、植物器官の状態推定が研究の中心です。
abstractIn this paper, a spectral-spatial, information-based, self-supervised anomaly detection (SSAD) approach is proposed.
Reproduction assets foundThe paper's SSAD code implementation and learned models are publicly available on GitHub. The fruit hyperspectral datasets are paper-specific but only available on request from the corresponding author.Code · publicThe code implementation and learned models of SSAD are available at https://github.com/YisenLiu-Intelligent-Sensing/SSAD accessed on 18 May 2022.Open asset ↗YisenLiu-Intelligent-Sensing/SSADlines:57-72Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Mexico is the 6th. worldwide producer of blueberry with 48,999 tons; where Guanajuato contributes 11% of the national total, ranking 7th. producer of the country (Secretariat of Agrifood and Rural Development, 2020). One of the main physiopathies that occurs in blueberry plants is root suffocation, which limits the ability of plants to breathe through the roots (Generalitat Valencia, 2022). Identifying the severity of the damage caused by physiopathies is essential for timely treatment and the prevention of its transmission; however, it is complex due to the lack of classification criteria. In this context, an application was developed whose purpose is to detect the level of affectation by physiopathies in blueberry leaves, calculating the percentage of damage severity, using artificial intelligence (AI). The results allow us to conclude that AI provides an easy and incremental progression as it is applied to different varieties of physiopathies, through the analysis of photographs. The application is useful for blueberry farmers and greenhouse owners, providing convenient information for the prevention and spread of crop diseases and improving decision-making
Why it matches plant phenotyping methodsブルーベリー葉の生理障害による損傷重症度を写真とAIで定量推定するアプリケーション開発であり、植物状態の取得・抽出が中心的な方法貢献である。
abstractan application was developed whose purpose is to detect the level of affectation by physiopathies in blueberry leaves, calculating the percentage of damage severity, using artificial intelligence (AI).
An operator of a wild blueberry harvester faces the fatigue of manually adjusting the height of the harvester's head, considering spatial variations in plant height, fruit zone, and field topography affecting fruit yield. For stress-free harvesting of wild blueberries, a deep learning-supported machine vision control system has been developed to detect the fruit height and precisely auto-adjust the header picking teeth rake position. The OpenCV AI Kit (OAK-D) was used with YOLOv4-tiny deep learning model with code developed in Python to solve the challenge of matching fruit heights with the harvester's head position. The system accuracy was statistically evaluated with R 2 (coefficient of determination) and σ (standard deviation) measured on the difference in distances between the berries picking teeth and average fruit heights, which were 72, 43% and 2.1, 2.3 cm for the auto and manual head adjustment systems, respectively. This innovative system performed well in weed-free areas but requires further work to operate in weedy sections of the fields. Benefits of using this system include automated control of the harvester's head to match the header picking rake height to the level of the fruit height while reducing the operator's stress by creating safer working environments.
Why it matches plant phenotyping methods果実の高さという植物器官の形態形質を画像から推定し、収穫機ヘッダーを自動調整する機械視覚システムを開発・評価しており、単なる収穫対象の位置検出を超えた形質取得が中心である。
abstracta deep learning-supported machine vision control system has been developed to detect the fruit height and precisely auto-adjust the header picking teeth rake position.
Blueberries have become an important fruit crop around the world. Most blueberries are hand-harvested to maintain high quality before the packing process for fresh market distribution. However, in the last 10 years increasing acreage has been machine harvested to reduce labor costs although machine harvesting causes bruising in more than 20% of blueberries. Non-bruised blueberries remain firmer and can be cold stored longer than bruised blueberries, while the bruised fruit cannot be sold, leading to a substantial economic loss. Current packing line sorting technology can sort soft berries but is unable to detect and sort out bruised fruit. Blueberry bruise assessment is necessary for providing the means to improve the harvester efficiency and fruit sorting process in the packing house. The goal of this study was to develop a web browser-based application (Web App) that users can access easily and determine the blueberry bruises accurately and quickly. We annotated 1725 blueberries to train MobileNet SSD and MobileNet-UNet, two deep learning models, generating a berry detection model, a berry segmentation model, and a bruise segmentation model. The average precision (AP) for the berry detection model was 0.977. The mean intersection over union (IoU) for berry segmentation and bruise segmentation was 0.979 and 0.773, respectively. Bruises were assessed for 56 images with 50 sliced blueberries in each image using the trained models that implemented in the Web App we developed. The bruise ratio data obtained from three different hardware devices were compared with the results that were manually annotated. Linear regression analyses showed a high correlation between the results from the deep learning models and the ground truth. The bruise ratio prediction using three hardware devices achieved an accuracy of 78.7%, 79.0%, and 78.9%, respectively, indicating that the model performance was satisfactory regardless of the hardware configuration. The average processing time for each image under three hardware configurations revealed that our Web App was superior to the manual method. The Web App reduced the time needed for assessing and tabulating bruise damage for 50 fruit samples from about 15 min needed for manual visual method to less than 30 s with comparable accuracy. This Web App is a robust tool for blueberry breeders, farmers and packers for evaluating berry bruises.
Why it matches plant phenotyping methodsブルーベリー内部の打撲症状を画像からセグメンテーション・定量化する深層学習Webアプリを開発し、手動アノテーションおよび複数ハードウェアで精度検証しており、植物器官の状態計測法が中心である。
abstractThe goal of this study was to develop a web browser-based application (Web App) that users can access easily and determine the blueberry bruises accurately and quickly.
In near-infrared (NIR) spectroscopy analysis of blueberry often the differences between biological variability factors (cultivar, season, etc.) lead to variations in light propagation properties and cell structure. Therefore, non-invasive detection models for blueberry developed based on NIR spectroscopy are often limited and unstable. To accommodate diverse prediction scenarios and improve the accuracy of NIR models in blueberry SSC estimation, a combined model calibration strategy that integrates Global-modeling method and calibration transfer method is proposed in this study. The Global-modeling method and calibration transfer methods (DOP and SBC) were applied for datasets containing only seasonal or cultivar differences and for those containing both seasonal and cultivar differences, respectively. Compared with the prediction model without correction, both Global-modeling and calibration transfer strategies improved model performance in the face of seasonal/cultivar variation challenges, with higher Rₚ and lower RMSEP values. After the application of Global-modeling, DOP and SBC methods, for the correction of seasonal variables (Bluecrop-2014), the RMSEP values of the model were reduced by 55.9%, 44.7% and 44.5%; for the correction of cultivar variables (M2-2015), the RMSEP values of the model were reduced by 45.8%, 22.8% and 37.9%; for the correction of cultivar variables (Duke-2015), the RMSEP values of the model were reduced by 9.3%, 5.9% and 2.8%. The experimental results indicated that combining Global-modeling and calibration transfer methods can weaken the influence of external conditions on blueberry NIR spectra to a certain extent, enhance the robustness of the model to biological variation, and improve the detection accuracy of blueberry SSC.
Why it matches plant phenotyping methodsブルーベリー果実のSSCという植物形質をNIR分光で推定するモデルについて、季節・品種差に対する校正転移と頑健性を開発・検証しており、形質取得手法が中心である。
abstracta combined model calibration strategy that integrates Global-modeling method and calibration transfer method is proposed in this study.
Although they are staple foods in cuisines globally, many commercial fruit varieties have become progressively less flavorful over time. Due to the cost and difficulty associated with flavor phenotyping, breeding programs have long been challenged in selecting for this complex trait. To address this issue, we leveraged targeted metabolomics of diverse tomato and blueberry accessions and their corresponding consumer panel ratings to create statistical and machine learning models that can predict sensory perceptions of fruit flavor. Using these models, a breeding program can assess flavor ratings for a large number of genotypes, previously limited by the low throughput of consumer sensory panels. The ability to predict consumer ratings of liking, sweet, sour, umami, and flavor intensity was evaluated by a 10-fold cross-validation, and the accuracies of 18 different models were assessed. The prediction accuracies were high for most attributes and ranged from 0.87 for sourness intensity in blueberry using XGBoost to 0.46 for overall liking in tomato using linear regression. Further, the best-performing models were used to infer the flavor compounds (sugars, acids, and volatiles) that contribute most to each flavor attribute. We found that the variance decomposition of overall liking score estimates that 42% and 56% of the variance was explained by volatile organic compounds in tomato and blueberry, respectively. We expect that these models will enable an earlier incorporation of flavor as breeding targets and encourage selection and release of more flavorful fruit varieties.
Why it matches plant phenotyping methods果実の風味という植物器官形質を、メタボロームから予測する統計・機械学習モデルを開発し、交差検証で性能評価している。育種に利用可能な風味表現型推定法が中心であり、単なる代謝測定ではない。
abstractwe leveraged targeted metabolomics of diverse tomato and blueberry accessions and their corresponding consumer panel ratings to create statistical and machine learning models that can predict sensory perceptions of fruit flavor.
Reproduction assets foundThe paper provides public author analysis scripts on GitHub and paper-specific phenotype data (sensory panel ratings, metabolite concentrations, model accuracies) in Datasets S1–S7 within the PNAS supporting information. The caret R package is a generic library and excluded.Code · publicRelevant scripts are provided in the GitHub repository at https://github.com/Resende-Lab/metabolomic_selection_for_enhanced_fruit_flavor .Open asset ↗Resende-Lab/metabolomic_selection_for_enhanced_fruit_flavorlines:126-357Dataset · publicSensory panel ratings and metabolite concentrations are provided in Datasets S1 and S2 . Underlying data for Fig. 3 are provided in Dataset S3 . Model accuracies in Fig. 4 are provided in Datasets S4–S7 .Open asset ↗lines:126-357Plant phenotyping relevance match · UnverifiedarXiv · checked 15 Sept 2026
BlueberryStrawberryFruitClassificationGrowth / development / phenology
Automated technologies for quality inspection of fruits have attracted great interest in the food industry. The development of nondestructive mechanisms to assess the quality of individual fruit prior to sale may lead to an increase in overall product quality, value, and consequently, producer competitiveness. However, the existing methods have limitations. Herein, a texture sensor based on highly sensitive hair-like cilia receptors, to allow a quick quality evaluation of fruit is proposed. The texture sensor consists of up to 100 magnetized nanocomposite cilia attached to a chip with magnetoresistive sensors in a full Wheatstone bridge architecture. In this paper we demonstrate the use of ciliary sensors in scanning fruits (blueberries and strawberries) in different maturation stages. The contact of the cilia with the fruit skin provided qualitative information about its texture in terms of ripeness stage. Less mature fruits exhibited, on average, a highest peak voltage of 0.14 mV for blueberries and 0.12 mV for strawberries, while overripe fruits exhibited 0.58 mV and 0.56 mV, respectively. The results were confirmed by sensorial assessment of the fruit freshness, and therefore attesting the application potential of the sensing technology for fruit quality control.
Why it matches plant phenotyping methods果実の成熟度・テクスチャを直接推定する磁気式センサーを開発し、ブルーベリーとイチゴで検証しており、植物器官の状態取得が中心である。
abstractHerein, a texture sensor based on highly sensitive hair-like cilia receptors, to allow a quick quality evaluation of fruit is proposed.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
BlueberryLaboratory / benchtopMRI / PETTissueObject detectionCalibration / preprocessingStress / disease detectionVisualization / data managementStress response / toleranceWater status / transpiration
Abstract Background Investigating plant mechanisms to tolerate freezing temperatures is critical to developing crops with superior cold hardiness. However, the lack of imaging methods that allow the visualization of freezing events in complex plant tissues remains a key limitation. Magnetic resonance imaging (MRI) has been successfully used to study many different plant models, including the study of in vivo changes during freezing. However, despite its benefits and past successes, the use of MRI in plant sciences remains low, likely due to limited access, high costs, and associated engineering challenges, such as keeping samples frozen for cold hardiness studies. To address this latter need, a novel device for keeping plant specimens at freezing temperatures during MRI is described. Results The device consists of commercial and custom parts. All custom parts were 3D printed and made available as open source to increase accessibility to research groups who wish to reproduce or iterate on this work. Calibration tests documented that, upon temperature equilibration for a given experimental temperature, conditions between the circulating coolant bath and inside the device seated within the bore of the magnet varied by less than 0.1 °C. The device was tested on plant material by imaging buds from Vaccinium macrocarpon in a small animal MRI system, at four temperatures, 20 °C, − 7 °C, − 14 °C, and − 21 °C. Results were compared to those obtained by independent controlled freezing test (CFT) evaluations. Non-damaging freezing events in inner bud structures were detected from the imaging data collected using this device, phenomena that are undetectable using CFT. Conclusions The use of this novel cooling and freezing device in conjunction with MRI facilitated the detection of freezing events in intact plant tissues through the observation of the presence and absence of water in liquid state. The device represents an important addition to plant imaging tools currently available to researchers. Furthermore, its open-source and customizable design ensures that it will be accessible to a wide range of researchers and applications.
Why it matches plant phenotyping methods植物組織の凍結イベントをMRIで可視化するための冷却・凍結デバイスを開発し、温度校正と植物試料での検証を行った、中心的なフェノタイピング手法研究である。
abstracta novel device for keeping plant specimens at freezing temperatures during MRI is described.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
Abstract Bogs are nutrient poor, acidic ecosystems that receive their water and nutrients entirely from precipitation (= ombrogenous) and as a result are sensitive to nutrient loading from atmospheric sources. Bogs occur frequently on the northern Alberta landscape, estimated to cover 6% of the Athabasca Oil Sands Area. As a result of oil sand extraction and processing, emissions of nitrogen (N) and sulfur (S) to the atmosphere have led to increasing N and S deposition that have the potential to alter the structure and function of these traditionally nutrient-poor ecosystems. At present, no detailed protocol is available for monitoring potential change of these sensitive ecosystems. We propose a user-friendly protocol that will monitor potential plant and lichen responses to future environmental inputs of nutrients and provide a structured means for collecting annual data. The protocol centers on measurement of five key plant/lichen attributes, including changes in (1) plant abundances, (2) dominant shrub annual growth and primary production, (3) lichen health estimated through chlorophyll/phaeophytin concentrations, (4) Sphagnum annual growth and production, and (5) annual growth of the dominant tree species ( Picea mariana ). We placed five permanent plots in each of six bogs located at different distances from the center of oil sand extraction and sampled these for 2 years (2018 and 2019). We compared line intercept with point intercept plant assessments using NMDS ordination, concluding that both methods provide comparable data. These data indicated that each of our six bog sites differ in key species abundances. Structural differences were apparent for the six sites between years. These differences were mostly driven by changes in Vaccinium oxycoccos , not the dominant shrubs. We developed allometric growth equations for the dominant two shrubs ( Rhododendron groenlandicum and Chamaedaphne calyculata ). Equations developed for each of the six sites produced growth values that were not different from one another nor from one developed using data from all sites. Annual growth of R. groenlandicum differed between sites, but not years, whereas growth of C. calyculata differed between the 2 years with more growth in 2018 compared with 2019. In comparison, Sphagnum plant density and stem bulk density both had strong site differences, with stem mass density higher in 2019. When combined, annual production of S. fuscum was greater in 2019 at three sites and not different at three of the sites. Chlorophyll and phaeophytin concentrations from the epiphytic lichen Evernia mesomorpha also differed between sites and years. This protocol for field assessments of five key plant/lichen response variables indicated that both site and year are factors that must be accounted for in future assessments. A portion of the site variation was related to patterns of N and S deposition.
Why it matches plant phenotyping methods植物・地衣類の応答を継続的に測定するフィールド評価プロトコルを中心に扱い、植生評価法の比較と成長推定式の開発も行っているため、植物表現型測定法として採用する。
abstractWe propose a user-friendly protocol that will monitor potential plant and lichen responses to future environmental inputs of nutrients and provide a structured means for collecting annual data.
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 9 Sept 2026
Although they are staple foods in cuisines globally, commercial fruit varieties have become progressively less flavorful over time. Due to the cost and difficulty associated with flavor phenotyping, many breeding programs have long been challenged in selecting for this complex trait. To address this issue, we leveraged targeted metabolomics of diverse tomato and blueberry accessions and their corresponding consumer panel ratings to create statistical and machine learning models that can predict sensory perceptions of fruit flavor. Using these models, a breeding program can assess flavor ratings for a large number of varieties, previously limited by the low-throughput and high cost of consumer sensory panels. The ability to predict consumer ratings of liking, sweet, sour, umami, and flavor intensity was evaluated by a 10-fold cross-validation and the accuracies of 18 different models are assessed. The best performing models were used to infer the flavor compounds (sugars, acids, and volatiles) that contribute most to each flavor attribute. The prediction accuracies were high for most attributes in both blueberries and tomatoes. We expect that these models will enable an earlier incorporation of flavor as breeding targets and encourage selection and release of more flavorful fruit varieties.
Why it matches plant phenotyping methods果実フレーバーという植物形質を、標的メタボロームと機械学習で予測する手法を開発し、交差検証で性能評価しており、表現型取得・推定が研究の中心である。
abstractDue to the cost and difficulty associated with flavor phenotyping, many breeding programs have long been challenged in selecting for this complex trait.
Improved fruit quality and prolonged storage capability are key breeding traits for blueberry ( Vaccinium spp.) fruit. Until now, breeding selection was mostly oriented on the amelioration of agronomic traits, such as flowering time, chilling requirement, or plant structure. Up until now, however, the storage effect on fruit quality has not been extensively studied, mostly because objective and handy phenotyping tools to evaluate quality traits were not available. In this study we are proposing a novel phenotyping protocol to support breeding selection and quality control within the entire blueberry production chain. Volatile organic compounds (VOCs) and texture traits, were measured by Proton Transfer Reaction- Time of Flight- Mass Spectrometry (PTR-ToF-MS) and a texture analyzer respectively, taking into consideration the influence of prolonged storage. The exploitation of the genetic variability existing within the investigated blueberry germplasm collection (including both southern and northern highbush, hybrids, and rabbiteyes) allowed the identification of the best performing cultivars, based on texture and VOCs variability, to be used as superior parental lines for future breeding programs. The comprehensive characterization of blueberry aroma allowed the identification of a wide array of spectrometric features, mostly related to aldehydes, alcohols, terpenoids, and esters, that can be used as putative biomarkers to rapidly evaluate the blueberry aroma variations related to genetic differences and storability. In addition, this study revealed a lack of straightforward relationship between harvest and postharvest quality features, that might be genotype-dependent.
Why it matches plant phenotyping methodsブルーベリー果実の品質・貯蔵性を評価する新規フェノタイピングプロトコルを提案し、PTR-ToF-MSとテクスチャ解析を育種選抜・品質管理に適用しているため、表現型取得法が中心的です。
abstractwe are proposing a novel phenotyping protocol to support breeding selection and quality control within the entire blueberry production chain.
Fruit traits such as cluster compactness, fruit maturity, and berry number per clusters are important to blueberry breeders and producers for making informed decisions about genotype selection related to yield traits and harvestability as well as for plant management. The goal of this study was to develop a data processing pipeline to count berries, to measure maturity, and to evaluate compactness (cluster tightness) automatically using a deep learning image segmentation method for four southern highbush blueberry cultivars ('Emerald', 'Farthing', 'Meadowlark', and 'Star'). An iterative annotation strategy was developed to label images that reduced the annotation time. A Mask R-CNN model was trained and tested to detect and segment individual blueberries with respect to maturity. The mean average precision for the validation and test dataset was 78.3% and 71.6% under 0.5 intersection over union (IOU) threshold, and the corresponding mask accuracy was 90.6% and 90.4%, respectively. Linear regression of the detected berry number and the ground truth showed an R 2 value of 0.886 with a root mean square error (RMSE) of 1.484. Analysis of the traits collected from the four cultivars indicated that 'Star' had the fewest berries per clusters, 'Farthing' had the least mature fruit in mid-April, 'Farthing' had the most compact clusters, and 'Meadowlark' had the loosest clusters. The deep learning image segmentation technique developed in this study is efficient for detecting and segmenting blueberry fruit, for extracting traits of interests related to machine harvestability, and for monitoring blueberry fruit development.
Why it matches plant phenotyping methodsブルーベリー果実の成熟度、果数、房の密集度という植物形質を、Mask R-CNN画像セグメンテーションで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中心である。
abstractThe goal of this study was to develop a data processing pipeline to count berries, to measure maturity, and to evaluate compactness (cluster tightness) automatically using a deep learning image segmentation method
This study investigated visible foliar ozone (O3) injury in three deciduous tree species with different growth patterns (indeterminate, Alnus glutinosa (L.) Gaertn.; intermediate, Sorbus aucuparia L.; and determinate, Vaccinium myrtillus L.) from May to August 2018. Ozone effects on the timing of injury onset and a plant injury index (PII) were investigated using two O3 indices, i.e., AOT40 (accumulative O3 exposure over 40 ppb during daylight hours) and PODY (phytotoxic O3 dose above a flux threshold of Y nmol m−2 s−1). A new parameterization for PODY estimation was developed for each species. Measurements were carried out in an O3 free-air controlled exposure (FACE) experiment with three levels of O3 treatment (ambient, AA; 1.5 × AA; and 2.0 × AA). Injury onset was found in May at 2.0 × AA in all three species and the timing of the onset was determined by the amount of stomatal O3 uptake. It required 4.0 mmol m−2 POD0 and 5.5 to 9.0 ppm·h AOT40. As a result, A. glutinosa with high stomatal conductance (gs) showed the earliest emergence of O3 visible injury among the three species. After the onset, O3 visible injury expanded to the plant level as confirmed by increased PII values. In A. glutinosa with indeterminate growth pattern, a new leaf formation alleviated the expansion of O3 visible injury at the plant level. V. myrtillus showed a dramatic increase of PII from June to July due to higher sensitivity to O3 in its flowering and fruiting stage. Ozone impacts on PII were better explained by the flux-based index, PODY, as compared with the exposure-based index, AOT40. The critical levels (CLs) corresponding to PII = 5 were 8.1 mmol m−2 POD7 in A. glutinosa, 22 mmol m−2 POD0 in S. aucuparia, and 5.8 mmol m−2 POD1 in V. myrtillus. The results highlight that the CLs for PII are species-specific. Establishing species-specific O3 flux-effect relationships should be key for a quantitative O3 risk assessment.
Why it matches plant phenotyping methods種別ごとのPODY推定パラメータを新規開発し、可視的な葉・植物体のオゾン傷害指数（PII）を定量化してフラックス指標との関係を評価しているため、植物表現型の取得・評価法が実質的に中心である。
abstractA new parameterization for PODY estimation was developed for each species.
Abstract Objectives Modern breeding strategies have advanced our knowledge of factors influencing phenolic content in plant foods such as highbush (HB) blueberries. However, potential traits related to phenolic bioavailability remain unknown due to limitations in phenotyping methods. In vitro digestion models have proven useful for estimation of phytochemical bioavailability, however, these models remain inefficient for screening large germplasm collections. High throughput (HT) models suitable for screening of larger HB blueberry populations are needed to advance our knowledge of genotypes and genetic factors influencing phenolic density and bioavailability. Methods An established low throughput (LT) three stage in vitro digestion model previously used for fruit phenolics was adapted to reduce tissue amounts, digestion volume and modified enzyme concentrations allowing for compatibility with a robotic fluid handling system (TECAN EVO) for HT. Bioaccessibility of phenolics from commercial HB blueberries was optimized for HT and validated against the LT method. The HT model was then used to screen phenolic bioaccessibility in a subset of 33 individual blueberry genotypes derived from a mapping population of Draper x Jewel (DxJ) genotypes. Results Bioaccessibility of anthocyanins (ANC), flavonols (FLAV), flavan-3-ols (F3L) and phenolic acids (PA) were well correlated (R = 0.92) between HT and LT methods. Calculated CV ranged from 2–14% for HT and for LT 2–20% respectively suggesting good reproducibility. Phenolic content within the DxJ subset ranged from 31.7–81.1 mg/100 g fw. Approximately, 60% of the phenolics were ANC with chlorogenic acid (28%), FLAV ( Conclusions A HT in vitro digestion model provides a novel tool for phenotyping phenolic bioaccessibility in blueberry fruit. Application to a subset of DxJ genotypes further suggest that variation in phenolic bioaccessibility exist and highlights the need for investigation of diversity panels and mapping populations. Funding Sources Foundation for Food and Agriculture Research.
Why it matches plant phenotyping methodsブルーベリー果実のフェノール類バイオアクセシビリティを測定する高スループット表現型解析法を開発し、低スループット法との相関・再現性を検証しているため、方法が中心的である。
Size and shape are important properties of shrub crops such as blueberries, and they can be particularly useful for evaluating bush architecture suited to mechanical harvesting. The overall goal of this study was to develop a 3D imaging approach to measure size-related traits and bush shape that are relevant to mechanical harvesting. 3D point clouds were acquired for 367 bushes from five genotype groups. Point cloud data were preprocessed to obtain clean bush points for characterizing bush architecture, including bush morphology (height, width, and volume), crown size, and shape descriptors (path curve λ and five shape indices). One-dimensional traits (height, width, and crown size) had high correlations ( R 2 = 0.88-0.95) between proposed method and manual measurements, whereas bush volume showed relatively lower correlations ( R 2 = 0.78-0.85). These correlations suggested that the present approach was accurate in measuring one-dimensional size traits and acceptable in estimating three-dimensional bush volume. Statistical results demonstrated that the five genotype groups were statistically different in crown size and bush shape. The differences matched with human evaluation regarding optimal bush architecture for mechanical harvesting. In particular, a visualization tool could be generated using crown size and path curve λ , which showed great potential of determining bush architecture suitable for mechanical harvesting quickly. Therefore, the processing pipeline of 3D point cloud data presented in this study is an effective tool for blueberry breeding programs (in particular for mechanical harvesting) and farm management.
Why it matches plant phenotyping methodsブルーベリー樹体のサイズ・形状形質を抽出する3D点群画像処理手法を開発し、手測定との相関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。
abstractThe overall goal of this study was to develop a 3D imaging approach to measure size-related traits and bush shape that are relevant to mechanical harvesting.
To overcome the environmental changes occurring now and predicted for the future, it is essential that fruit breeders develop cultivars with better physiological performance. During the last few decades, high-throughput plant phenotyping and phenomics have been developed primarily in cereal breeding programs. In this study, plant reflectance, at the level of the leaf, was used to assess several physiological traits in five Vaccinium spp. cultivars growing under four controlled conditions (no-stress, water deficit, heat stress, and combined stress). Two modeling methodologies [Multiple Linear Regression (MLR) and Partial Least Squares (PLS)] with or without (W/O) prior wavelength selection (multicollinearity, genetic algorithms, or in combination) were considered. PLS generated better estimates than MLR, although prior wavelength selection improved MLR predictions. When data from the environments were combined, PLS W/O gave the best assessment for most of the traits, while in individual environments, the results varied according to the trait and methodology considered. The highest validation predictions were obtained for chlorophyll a/b (R2Val ≤ 0.87), maximum electron transport rate (R2Val ≤ 0.60), and the irradiance at which the electron transport rate is saturated (R2Val ≤ 0.59). The results of this study, the first to model modulated chlorophyll fluorescence by reflectance, confirming the potential for implementing this tool in blueberry breeding programs, at least for the estimation of a number of important physiological traits. Additionally, the differential effects of the environment on the spectral signature of each cultivar shows this tool could be directly used to assess their tolerance to specific environments.
Why it matches plant phenotyping methods葉の分光反射から生理形質を推定するモデルを開発・比較し、検証予測性能を評価しており、植物フェノタイピング手法が中心である。
abstractplant reflectance, at the level of the leaf, was used to assess several physiological traits
Phenotyping morphological traits of blueberry bushes in the field is important for selecting genotypes that are easily harvested by mechanical harvesters. Morphological data can also be used to assess the effects of crop treatments such as plant growth regulators, fertilizers, and environmental conditions. This paper investigates the feasibility and accuracy of an inexpensive unmanned aerial system in determining the morphological characteristics of blueberry bushes. Color images collected by a quadcopter are processed into three-dimensional point clouds via structure from motion algorithms. Bush height, extents, canopy area, and volume, in addition to crown diameter and width, are derived and referenced to ground truth. In an experimental farm, twenty-five bushes were imaged by a quadcopter. Height and width dimensions achieved a mean absolute error of 9.85 cm before and 5.82 cm after systematic under-estimation correction. Strong correlation was found between manual and image derived bush volumes and their traditional growth indices. Hedgerows of three Southern Highbush varieties were imaged at a commercial farm to extract five morphological features (base angle, blockiness, crown percent height, crown ratio, and vegetation ratio) associated with cultivation and machine harvestability. The bushes were found to be partially separable by multivariate analysis. The methodology developed from this study is not only valuable for plant breeders to screen genotypes with bush morphological traits that are suitable for machine harvest, but can also aid producers in crop management such as pruning and plot layout organization.
Why it matches plant phenotyping methodsUAS画像とSfM点群からブルーベリー樹体の形態形質を抽出し、地上真値と精度検証しており、植物フェノタイピング手法が研究の中心です。
abstractThis paper investigates the feasibility and accuracy of an inexpensive unmanned aerial system in determining the morphological characteristics of blueberry bushes.
Spatial variations in wild blueberry plant height pose a serious challenge for the operator to maintain the optimum harvester picking head height. Harvester head adjustment based on plant height increases harvestable fruit yield and quality while preventing plant pulling. An advanced Ultrasonic Plant Height Measurement System II (UPHMS II) was developed and incorporated into a commercial mechanical harvester. The developed system consisted of three ultrasonic sensors covering the width of the harvester head, a real-time kinematics global positioning system (RTK-GPS), custom built software, and a rugged computer. The custom software acquired and processed the ultrasonic sensing data in real-time during mechanical harvesting. Four wild blueberry fields were selected in central Nova Scotia to evaluate the performance of the developed system. Forty eight experimental plots were randomly constructed within four fields and wild blueberry plant heights were recorded manually prior to harvest. The UPHMS II was tested and evaluated to estimate plant height. The manual plant height measurements were compared with ultrasonically sensed data to ensure the accuracy of the developed system. A previous system (UPHMS I) comprising of one ultrasonic sensor was also tested and evaluated. Results of regression and scatter plots revealed that the UPHMS II was able to sense plant height in real-time more accurately with root mean square error (RMSE) of 1.7 cm when compared with the UPHMS I (RMSE = 5.7 cm). The UPHMS II equipped with three sensors covered the complete width of the harvester (0.91 m), which showed higher accuracy compared to the UPHMS I.
Why it matches plant phenotyping methods超音波センサーとリアルタイム処理によって収穫中に野生ブルーベリーの草丈を測定するシステムを開発・評価しており、植物形質取得法が研究の中心である。
abstractAn advanced Ultrasonic Plant Height Measurement System II (UPHMS II) was developed and incorporated into a commercial mechanical harvester.
Why it matches plant phenotyping methodsブルーベリーの硬度、弾性率、可溶性固形分という果実形質を、ハイパースペクトル・NIRセンシングと予測モデルで非破壊推定する手法の開発・検証が中心である。
abstractHyperspectral reflectance and transmittance sensing as well as near‐infrared (NIR) spectroscopy were investigated as non‐destructive tools for estimating blueberry firmness, elastic modulus and soluble solid content (SSC).