Water scarcity and increasingly irregular rainfall threaten avocado production in Mediterranean regions, yet the long term physiological responses of mature trees to sustained deficit irrigation remain poorly understood. We conducted a two-year field study integrating continuous monitoring of the soil plant atmosphere continuum, drone-based multispectral imaging, canopy structural analysis, and fruit phenotyping in a mature avocado orchard subjected to three irrigation regimes. The two study years differed markedly in rainfall, providing a unique opportunity to evaluate how environmental conditions modulate tree responses to water limitation. Trees under severe deficit irrigation showed depletion of water in deeper soil layers and a flattened physiological profile, with near-zero diel variation in leaf thickness and trunk water potential, indicating minimal transpiration and decoupling of tree water status from environmental demand. Drone telemetry via NDVI detected stress during fruit growth and maturation, but not during flowering or the new summer leaf flush, revealing greater drought sensitivity at later maturation stages. Although canopy area did not differ among irrigation treatments, canopy surface roughness increased significantly under deficit irrigation, thereby identifying a novel structural indicator of drought stress. Despite large physiological differences among treatments, fruit number remained stable, while fruit weight decreased significantly under severe deficit irrigation, particularly in the wetter year, suggesting that annual rainfall modulates the trade-off between fruit retention and fruit growth. This study provides the first continuous, multi-scale characterization of avocado performance under sustained deficit irrigation in Mediterranean conditions. By integrating plant-based sensors, remote sensing, and artificial intelligence, we reveal previously undescribed stress dynamics and identify new indicators for precision irrigation management in fruit crops.
Why it matches plant phenotyping methods継続的な植物センサー、ドローン画像、樹冠構造解析、果実表現型計測を統合し、NDVIや樹冠表面粗さなどのストレス指標を抽出する方法が研究の主要部分であるため。
abstractWe conducted a two-year field study integrating continuous monitoring of the soil plant atmosphere continuum, drone-based multispectral imaging, canopy structural analysis, and fruit phenotyping
Global avocado production exceeds 10 million tons annually. Among the diseases affecting avocado fruit, anthracnose is one of the most significant, causing black lesions and fruit decay that can result in yield losses of 20–30%. To facilitate the early detection of anthracnose, this study proposes a computer vision-based approach. A dataset containing 2218 images of Fuerte avocados was first developed, comprising 1730 healthy samples and 488 anthracnose-infected samples after the labeling process. In the experimental phase, several convolutional neural network (CNN) models with varying depths (3, 4, 5, and 6 layers) were designed and evaluated. These models were subsequently integrated into different weighted ensemble configurations, where the best performance was achieved by the ensemble combining all four individual CNNs. The proposed weighted ensemble was compared against widely used state-of-the-art architectures, including VGG-16, ResNet-18, and MobileNetV2. Experimental results demonstrated the effectiveness of the proposed approach, achieving an F1-score of 0.9052, outperforming VGG-16 (0.8283), ResNet-18 (0.7328), and MobileNetV2 (0.7320).
Why it matches plant phenotyping methodsアボカド果実の病徴（炭疽病病変）を画像から検出するコンピュータビジョン手法を開発・比較検証しており、植物状態の取得が研究の中心である。
abstractTo facilitate the early detection of anthracnose, this study proposes a computer vision-based approach.
Crop monitoring over large land extensions represents a central challenge in precision agriculture, especially in polyculture contexts where species with different nutritional needs are combined. This study presents a methodology to manage and analyze large volumes of multispectral images captured by unmanned aerial vehicles (UAVs) in order to identify and monitor crops at the plant level. The images are efficiently stored and retrieved using a Hilbert Curve, which reduces the complexity of the search process from O(n2) to O(log(n)) where n represents the number of indexed data points). The system connects to a distributed Structured Query Language (SQL) database, allowing for fast image retrieval based on GPS coordinates and other metadata. Additionally, the Normalized Difference Vegetation Index (NDVI) is calculated using reflectance data from the red and near-infrared channels, adjusted by semantic segmentation masks generated with a U-Net model, which allows for species-specific evaluations. The methodology was evaluated on a 20,000 m2 polyculture farm with coffee, avocado, and plantain crops, using a dataset of 270 aerial images partitioned into 70% for training and 30% for validation. The results show improvements in retrieval speed and precision with the Hilbert Space-Filling Curve (HSFC) approach, and an accuracy of 82.3% and an the Mean Intersection over Union (MIoU) of 68.4% in species detection with the U-Net model. Overall, this integrated framework demonstrates a scalable potential for precision agriculture in complex polyculture systems, facilitating efficient data management and targeted crop interventions.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像、セマンティックセグメンテーション、NDVIを統合した植物レベルの健康状態評価手法が研究の中心であり、手法の構築と検証も行っている。
abstractThis study presents a methodology to manage and analyze large volumes of multispectral images captured by unmanned aerial vehicles (UAVs) in order to identify and monitor crops at the plant level.
Avocado ( Persea americana Mill.), with a global production estimated at 10.4 million tons in 2023, suffers annual losses of 20-30% due to diseases such as anthracnose ( Colletotrichum gloeosporioides ) and scab ( Sphaceloma perseae ), resulting in substantial economic impacts for major producing countries (Mexico, Peru, and Colombia). This study introduces an advanced system that integrates a binary sequential voting architecture (VotingBS) with a fully functional web application, for the automated identification of two high-incidence diseases: anthracnose and scab, both of which critically affect fruit quality and yield. The proposed VotingBS architecture implements a hierarchical two-stage classification strategy. In the first stage, a five-model deep learning ensemble differentiates between healthy and diseased fruits. In the second stage, another ensemble determines which of the two diseases is present. For this purpose, a collection of 674 labeled fruit images was used for training and validation. Experimental results demonstrate outstanding model performance, achieving key metrics such as 98.92% precision, 98.89% recall, and 99.03% accuracy, significantly outperforming traditional approaches. Moreover, the solution was deployed through a web app featuring dedicated modules for crop management, phytosanitary analysis, and disease diagnosis. This architecture enhances the system's practical utility and facilitates its adoption by farmers, field technicians, and agricultural monitoring agencies. Overall, this work demonstrates how combining hybrid deep learning models with accessible digital platforms can revolutionize plant disease diagnostics, fostering a more efficient, automated, and resilient precision agriculture.
Why it matches plant phenotyping methodsアボカド果実の画像から健全・罹病状態および病害種を推定する深層学習分類システムとWebアプリを開発しており、植物病害表現型の取得・抽出が研究の中心である。
abstractThis study introduces an advanced system that integrates a binary sequential voting architecture (VotingBS) with a fully functional web application, for the automated identification of two high-incidence diseases: anthracnose and scab
AvocadoStrawberryFruitClassificationObject detectionGrowth / development / phenology
This paper presents a novel and innovative 14,630 fruit images dataset, consisting of 1333 original images and the remaining augmented images for strawberry and avocado fruits. The dataset records the growth of strawberries and avocados in four different stages: unripe, partially ripe, ripe, and rotten. Though the fruit ripening process is commonly known, a lack of systematic datasets to show the fruit changing from an unripe state to a rotting state was prevalent for the two fruits in question. Over two months, the dataset was collected through rigorous tracking to effectively provide a measure of each of the fruits' conditions. The fruits were obtained from Mahabaleshwar farms in Maharashtra, India, as well as from local markets in Maharashtra and Pune. The fruits were monitored continuously from the time of harvesting, and all observed changes were carefully recorded. The uniqueness of this dataset is that it covers both strawberries and avocados, which have different patterns of ripening and are highly commercially valuable. The images were annotated using the online annotation tool - makesense.ai, with a total of 1499 bounding boxes for each fruit. By encompassing these two diverse fruit types, the dataset provides a valuable resource for researchers, agriculturalists, and food scientists to investigate and compare the ripening behaviours of different fruit species.
Why it matches plant phenotyping methodsイチゴとアボカドの果実画像を用いて、未熟から腐敗までの可視的な成熟・状態を体系的に記録し、注釈付きデータセットとして提供しているため、植物器官の状態を対象とする画像ベースのフェノタイピングデータセットに該当する。
abstractThis paper presents a novel and innovative 14,630 fruit images dataset, consisting of 1333 original images and the remaining augmented images for strawberry and avocado fruits.
Reproduction assets foundThe paper is a Data in Brief article describing a public Mendeley Data repository containing the authors' own fruit image dataset (14,630 strawberry/avocado images with YOLO bounding-box annotations across ripening stages), which directly constitutes the paper's phenotyping measurements. No analysis code or trained模型s是Dataset · publicset up using a white background for enabling consistent and uniform image acquisition..
Data source location
Dataset was collected from (i) Mahabaleshwar, Maharashtra, India; and (ii) Pune, Maharashtra, India.
Data accessibility
Repository name: mendeley.com
Data identification number: 10.17632/zysvgmxcyz.1
Direct URL to data: https://data.mendeley.com/datasets/zysvgmxcyz/1
Related research article
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Value of the Data
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This dataset is a useful resource for machine learning solutions in fruit maturity detection and can contribute to the design of automated sorting and classification systems by ripeness stages.
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Food processing companies and agricultural scientists may utilize this data to Open asset ↗10.17632/zysvgmxcyz.1lines:1-51Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published6 Mar 20252025 5th International Conference on Expert Clouds and Applications (ICOECA)Cited by 0 · OpenAlex ↗
Avocado cultivation is a rapidly growing industry known for its creamy, nutritious fruit and economic value In Tamil Nadu the plane bear fruits for a period of 3 to 4 years after which tree becomes highly susceptible to diseases like Anthracnose, Root Rot, Algal Leaf Spot and Scab which poses a severe threat to the crop and in worst cases the tree dies. Therefore, current techniques of disease inspection such as manual inspections and RGB image analysis are imprecise for early detection since RGB datasets only involve channels in the visible spectrum. This is where multispectral imaging shines by covering ‘hidden’ spectral bands such as Orange, Cyan, and Near Infrared bands that are not visible by normal human eye. The research combines UAV systems with multispectral imagery to perform avocado disease identification as a solution for covering extensive monitoring areas spanning high tree heights. The researchers used Faster R-CNN to classify diseases through multispectral datasets training because Near-Infrared scanning proved most successful at detecting infections. The detection capabilities are improved through better feature extraction methods and optimized model training process. Identifying diseases at an early stage enables farmers to intervene in time thus protecting their avocado crops for continuous production. This research establishes new opportunities for UAV-based disease detection through multispectral methods that help maintain agricultural sustainability and avocado industry economic stability.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とFaster R-CNNによってアボカドの病害状態を直接推定する手法が研究の中心であり、植物病害フェノタイピングに該当する。
abstractThe research combines UAV systems with multispectral imagery to perform avocado disease identification
Assessing the health status of vegetation is of vital importance for all stakeholders. Multi-spectral and hyper-spectral imaging systems are tools for evaluating the health of vegetation in laboratory settings, and also hold the potential of assessing vegetation of large portions of land. However, the literature lacks benchmark datasets to test algorithms for predicting plant health status, with most researchers creating tailored datasets. This work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages, from avocado, olive, and grape trees, which are common crops in the Valparaíso region of Chile. This dataset is a valuable asset for developing tools in the field of precision agriculture and assessing the general health status of vegetation.
Why it matches plant phenotyping methods植物のマルチスペクトル・ハイパースペクトル画像と葉の水分状態・化学形質を含む評価用データセットを構築しており、フェノタイピング手法開発のためのベンチマークが中心である。
abstractThis work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages
Reproduction assets foundThe paper's multispectral images, hyperspectral reflectance, and trait measurements (weight, chlorophyll, nitrogen, fuel moisture) are publicly deposited on Figshare with an explicit DOI. The authors' sample Matlab code is included within that dataset. The MicaSense imageprocessing repository is a generic third-party工具Dataset · publicAll the data is available at this repository DOI: https://doi.org/10.6084/m9.figshare.26950660.v2.Open asset ↗figshare · 10.6084/m9.figshare.26950660.v2pdf-page:14 lines:1-35Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Abstract Assessing the health status of vegetation is of vital importance for all stakeholders. Multi-spectral and hyper-spectral imaging systems are tools for evaluating the health of crops across large areas, particularly when deployed on robotic platforms such as unmanned aerial vehicles (UAVs). However, the literature lacks benchmark datasets to test algorithms for predicting plant health status, with most researchers creating tailored datasets. This work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages, from avocado, olive, and vineyard trees, which are common crops in the Valparaíso region of Chile. This dataset is a valuable asset for developing tools in the field of precision agriculture and assessing the general health status of vegetation.
Why it matches plant phenotyping methods植物の健康状態を推定するためのマルチスペクトル・ハイパースペクトル画像と葉の形質測定を組み合わせた評価用データセットが主題であり、植物フェノタイピング手法のベンチマーク資源に該当する。
abstractThis work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages
Reproduction assets foundThe paper is a dataset descriptor; its complete plant-phenotyping measurements (multispectral leaf images, hyperspectral reflectance, chlorophyll, nitrogen, weight/FMC across five drying stages for avocado, olive, and vineyard) are publicly deposited on figshare under DOI 10.6084/M9.FIGSHARE.26950660, along with aMatlåDataset · publicAll the data is available at this repository DOI: 10.6084/M9.FIGSHARE.26950660Open asset ↗figshare · 10.6084/M9.FIGSHARE.26950660pdf-page:15 lines:1-59Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published17 Sept 2024Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for PhotobiologyCited by 4 · OpenAlex ↗
The flesh oil content (OC) is a crucial commercial indicator of avocado maturity and directly correlates with its nutritional quality. To meet export standards and optimize edible characteristics, avocados must be harvested at the appropriate stage of physiological maturity. The significant variability in OC during maturation, without any external morphological indicators, poses a longstanding challenge. Currently, harvesting maturity is optimized through time-consuming, destructive laboratory methods like freeze-drying and chemical extraction, which use representative samples to estimate the maturity of entire orchards. In this study, for the first time, we employed fluorescence imaging of avocado skin using 365-nm UV polarized light excitation to estimate the OC in the 'Bacon' avocado cultivar. We developed a surface fluorescence index that strongly correlates with OC, achieving correlation coefficients up to - 0.91. Our non-destructive and rapid approach achieved a cross-validation accuracy with an R 2 value of 0.81, enabling the classification of avocados with low and high OC. This pioneering method shows considerable potential for further improvement and refinement. This study lays the groundwork for developing a portable, cost-effective, and real-time method for non-destructive in situ monitoring of avocado OC in the field and its integration into large-scale post-harvest grading systems.
Why it matches plant phenotyping methodsアボカド果実の油分含量という植物器官形質を、365 nm蛍光画像から非破壊推定する手法を開発・検証しており、形質取得法が研究の中心である。
abstractIn this study, for the first time, we employed fluorescence imaging of avocado skin using 365-nm UV polarized light excitation to estimate the OC in the 'Bacon' avocado cultivar.
Carbohydrate levels are important regulators of the growth and yield of tree crops. Current methods for measuring foliar carbohydrate concentrations are time consuming and laborious, but rapid imaging technologies have emerged with the potential to improve the effectiveness of tree nutrient management. Carbohydrate concentrations were predicted using hyperspectral imaging (400–1000 nm) of leaves of the evergreen tree crops, avocado, and macadamia. Models were developed using partial least squares regression (PLSR) and artificial neural network (ANN) algorithms to predict carbohydrate concentrations. PLSR models had R2 values of 0.51, 0.82, 0.86, and 0.85, and ANN models had R2 values of 0.83, 0.83, 0.78, and 0.86, in predicting starch, sucrose, glucose, and fructose concentrations, respectively, in avocado leaves. PLSR models had R2 values of 0.60, 0.64, 0.91, and 0.95, and ANN models had R2 values of 0.67, 0.82, 0.98, and 0.98, in predicting the same concentrations, respectively, in macadamia leaves. ANN only outperformed PLSR when predicting starch concentrations in avocado leaves and sucrose concentrations in macadamia leaves. Performance differences were possibly associated with nonlinear relationships between carbohydrate concentrations and reflectance values. This study demonstrates that PLSR and ANN models perform well in predicting carbohydrate concentrations in evergreen tree-crop leaves.
Why it matches plant phenotyping methods葉のハイパースペクトル画像から炭水化物濃度という植物形質を推定するモデルを開発・比較しており、非破壊的な表現型取得手法が研究の中心である。
abstractCarbohydrate concentrations were predicted using hyperspectral imaging (400–1000 nm) of leaves of the evergreen tree crops, avocado, and macadamia.
Avocados are an important economic crop of Hawaii, contributing to approximately 3% of all avocados grown in the United States. To export Hawaii-grown avocados, growers must follow strict United States Department of Agriculture Animal and Plant Health Inspection Service (USDA-APHIS) regulations. Currently, only the Sharwil variety can be exported relying on a systems approach, which allows fruit to be exported without quarantine treatment; treatments that can negatively impact the quality of avocados. However, for the systems approach to be applied, Hawaii avocado growers must positively identify the avocados variety as Sharwil with APHIS prior to export. Currently, variety identification relies on physical characteristics, which can be erroneous and subjective, and has been disputed by growers. Once the fruit is harvested, variety identification is difficult. While molecular markers can be used through DNA extraction from the skin, the process leaves the fruit unmarketable. This study evaluated the feasibility of using near-infrared spectroscopy to non-destructively discriminate between different Hawaii-grown avocado varieties, such as Sharwil, Beshore, and Yamagata, Nishikawa, and Greengold, and to positively identify Sharwil from the other varieties mentioned above. The classifiers built using a bench-top system achieved 95% total classification rates for both discriminating the varieties from one another and positively identifying Sharwil while the classifier built using a handheld spectrometer achieved 96% and 96.7% total classification rates for discriminating the varieties from one another and positively identifying Sharwil, respectively. Results from chemometric methods and chemical analysis suggested that water and lipid were key contributors to the performance of classifiers. The positive results demonstrate the feasibility of NIR spectroscopy for discriminating different avocado varieties as well as authenticating Sharwil. To develop robust and stable models for the growers, distributors, and regulators in Hawaii, more varieties and additional seasons should continue to be added.
Why it matches plant phenotyping methods近赤外分光法を用いてアボカド果実の品種を非破壊識別し、ベンチトップおよびハンドヘルド装置の分類性能を評価しており、植物器官の状態・品種形質取得法が研究の中心である。
abstractThis study evaluated the feasibility of using near-infrared spectroscopy to non-destructively discriminate between different Hawaii-grown avocado varieties
Drought and salinity stresses present significant challenges that exert a severe impact on crop productivity worldwide. Understanding the dynamics of salicylic acid (SA), a vital phytohormone involved in stress response, can provide valuable insights into the mechanisms of plant adaptation to cope with these challenging conditions. This paper describes and tests a sensor system that enables real-time and non-invasive monitoring of SA content in avocado plants exposed to drought and salinity. By using a reverse iontophoretic system in conjunction with a laser-induced graphene electrode, we demonstrated a sensor with high sensitivity (82.3 nA/[μmol L -1 ⋅cm -2 ]), low limit of detection (LOD, 8.2 μmol L -1 ), and fast sampling response (20 s). Significant differences were observed between the dynamics of SA accumulation in response to drought versus those of salt stress. SA response under drought stress conditions proved to be faster and more intense than under salt stress conditions. These different patterns shed light on the specific adaptive strategies that avocado plants employ to cope with different types of environmental stressors. A notable advantage of the proposed technology is the minimal interference with other plant metabolites, which allows for precise SA detection independent of any interfering factors. In addition, the system features a short extraction time that enables an efficient and rapid analysis of SA content.
Why it matches plant phenotyping methodsアボカドの乾燥・塩ストレス応答を示すSA含量を、ウェアラブルセンサーでリアルタイム・非破壊測定する技術の開発と試験が研究の中心であり、植物の生理状態を取得するフェノタイピング手法に該当する。
abstractThis paper describes and tests a sensor system that enables real-time and non-invasive monitoring of SA content in avocado plants exposed to drought and salinity.
In fruit tree growth, pruning is an important management practice for preventing overcrowding, improving canopy access to light and promoting regrowth. In fruit with a high energy content, including avocado (Persea Americana), ensuring all parts of the canopy have sufficient exposure to light is of particular importance. Due to the slow nature of agriculture and the numerous parameters contributing to yield, decisions in pruning, particularly in selective limb removal, are typically made using tradition or rules of thumb rather than data-driven analysis. Many existing algorithmic, simulation-based approaches rely on high-fidelity digital captures or purely computer-generated fruit trees, and are unable to provide specific results on an orchard scale. We present a framework for suggesting pruning strategies on LiDAR-scanned commercial fruit trees using a scoring function with a focus on improving light distribution throughout the canopy. Due to the destructive nature of physical experimentation, this framework is presented using a three-stage approach where stages can be independently validated. Firstly, a scoring function to assess the quality of the tree shape based on its light availability and size was developed for comparative analysis between trees using observations from agricultural literature, and was validated against yield characteristics from an avocado and mango orchard. This demonstrated a reasonable correlation against fruit count, with an R2 score of 0.615 for avocado and 0.506 for mango. The second stage was to implement a tool for simulating pruning by algorithmically estimating which parts of a tree point cloud would be removed given specific cut points using structural analysis of the tree. This was validated experimentally using manually generated ground truth pruned tree models, showing good results with an average F1 score of 0.78 across 144 experiments. Finally, new pruning locations were suggested by discovering points in the tree which negatively impact the light distribution, and we used the previous two stages to estimate the improvement of the tree given these suggestions. These results were compared to a tree which was commercially pruned using existing wisdom. The light distribution was improved by up to 25.15%, demonstrating a 16% improvement over the commercial pruning, and certain cut points were discovered which improved light distribution with a smaller negative impact on tree volume. The final results suggest value in the framework as a decision making tool for commercial growers, or as a starting point for automated pruning since the entire process can be performed with little human intervention. Further development should be performed to improve the suggestion mechanism and incorporate more agricultural objectives and operations.
Why it matches plant phenotyping methodsLiDAR点群から樹形・樹冠内の光分布を評価し、剪定シミュレーションと剪定位置提案を行う技術を開発・検証しており、植物形態・構造の取得と解析が中心である。
abstractWe present a framework for suggesting pruning strategies on LiDAR-scanned commercial fruit trees using a scoring function with a focus on improving light distribution throughout the canopy.
Solar Induced Fluorescence (SIF) emitted from the photosynthetic apparatus is related indirectly to biomass production. It can be remotely sensed from airborne platforms, yet, the spatial resolution from satellites, for example, is too low for commercial agricultural uses. We used a non-imager point spectroradiometer mounted on SIF-UAV (Unmanned Aerial Vehicle) system, and retrieved SIF at high spatial resolution, which is fitted for precision agriculture applications. In this study, we tracked the spatial variation of SIF along the season over a commercial avocado (P. americana Mill) orchard. The retrieved SIF signals were Krieg-interpolated to create a continuous SIF layer that allowed the estimation of SIF values for each tree in the orchard. We show that the SIF signal retrieved over the canopies in the main fruit development stage is highly correlated with the levels of sugars accumulated in the ripening avocado fruit. It established the foundations for high-spatial resolution detection of the natural variation of photosynthesis activity, which may lead to in-season adjustments of agronomic inputs using precision agriculture technologies.
Why it matches plant phenotyping methodsUAV搭載分光放射計による樹冠SIFの高空間解像度取得・補間と樹体単位推定が中心で、植物の光合成活性という生理形質を評価しているため。
abstractWe used a non-imager point spectroradiometer mounted on SIF-UAV (Unmanned Aerial Vehicle) system, and retrieved SIF at high spatial resolution
The ability to accurately and systematically monitor avocado crop phenology offers significant benefits for the optimization of farm management activities, improvement of crop productivity, yield estimation, and evaluation crops’ resilience to extreme weather conditions and future climate change. In this study, Sentinel-2-derived enhanced vegetation indices (EVIs) from 2017 to 2021 were used to retrieve canopy reflectance information that coincided with crop phenological stages, such as flowering (F), vegetative growth (V), fruit maturity (M), and harvest (H), in commercial avocado orchards in Bundaberg, Queensland and Renmark, South Australia. Tukey’s honestly significant difference (Tukey-HSD) test after one-way analysis of variance (ANOVA) with EVI metrics (EVImean and EVIslope) showed statistically significant differences between the four phenological stages. From a Pearson correlation analysis, a distinctive seasonal trend of EVIs was observed (R = 0.68 to 0.95 for Bundaberg and R = 0.8 to 0.96 for Renmark) in all 5 years, with the peak EVIs being observed at the M stage and the trough being observed at the F stage. However, a Tukey-HSD test showed significant variability in mean EVI values between seasons for both the Bundaberg and Renmark farms. The variability of the mean EVIs between the two farms was also evident with a p-value
Why it matches plant phenotyping methodsSentinel-2時系列データからアボカド樹冠反射を取得し、開花・生育・成熟・収穫という植物フェノロジー状態を識別・評価する手法が研究の中心である。
abstractThe ability to accurately and systematically monitor avocado crop phenology offers significant benefits
Avocado is a traditional fruit in the diet of Ecuadorians and requires proper crop handling to guarantee high production. Implementations of new technological alternatives, such as spectroscopy indexes that correlate with each other, will optimize avocado crop management. This research validated the use of red band and near infrared-based plant indices with leaf nitrogen content. The plant indices used were normalized differential vegetation index (NDVI) and transformed vegetation index (TVI). These indexes were developed from two orthomosaics, obtaining images that capture red and near-infrared bands. Regression and correlation analysis were performed between the vegetable indices and the foliar nitrogen content analysis, generating R 2 values of 0.93 for NDVI, and 0.95 for TVI. The values of the plant indexes can be used to estimate plant vigor based on the nitrogen content of the foliar area.
Why it matches plant phenotyping methods赤色・近赤外画像から算出した植生指数を葉内窒素量および樹勢推定に関連付け、手法の妥当性を検証しているため、植物フェノタイピング手法が中心です。
abstractThis research validated the use of red band and near infrared-based plant indices with leaf nitrogen content.
Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) systems are useful tools for deriving horticultural tree structure estimates. However, there are limited studies to guide growers and agronomists on different applications of the two technologies for horticultural tree crops, despite the importance of measuring tree structure for pruning practices, yield forecasting, tree condition assessment, irrigation and fertilization optimization. Here, we evaluated ALS data against near coincident TLS data in avocado, macadamia and mango orchards to demonstrate and assess their accuracies and potential application for mapping crown area, fractional cover, maximum crown height, and crown volume. ALS and TLS measurements were similar for crown area, fractional cover and maximum crown height (coefficient of determination (R2) ≥ 0.94, relative root mean square error (rRMSE) ≤ 4.47%). Due to the limited ability of ALS data to measure lower branches and within crown structure, crown volume estimates from ALS and TLS data were less correlated (R2 = 0.81, rRMSE = 42.66%) with the ALS data found to consistently underestimate crown volume. To illustrate the effects of different spatial resolution, capacity and coverage of ALS and TLS data, we also calculated leaf area, leaf area density and vertical leaf area profile from the TLS data, while canopy height, tree row dimensions and tree counts) at the orchard level were calculated from ALS data. Our results showed that ALS data have the ability to accurately measure horticultural crown structural parameters, which mainly rely on top of crown information, and measurements of hedgerow width, length and tree counts at the orchard scale is also achievable. While the use of TLS data to map crown structure can only cover a limited number of trees, the assessment of all crown strata is achievable, allowing measurements of crown volume, leaf area density and vertical leaf area profile to be derived for individual trees. This study provides information for growers and horticultural industries on the capacities and achievable mapping accuracies of standard ALS data for calculating crown structural attributes of horticultural tree crops.
Why it matches plant phenotyping methodsALS/TLSによる果樹の樹冠構造形質の取得・比較検証が研究の中心であり、精度評価と適用可能性を明示的に扱っている。
abstractHere, we evaluated ALS data against near coincident TLS data in avocado, macadamia and mango orchards to demonstrate and assess their accuracies and potential application for mapping crown area, fractional cover, maximum crown height, and crown volume.
White root rot, caused by the soilborne fungus Rosellinia necatrix , is an important constraint to production for a wide range of woody crop plants such as avocado trees. The current methods of detection of white root rot are based on microbial and molecular techniques, and their application at orchard scale is limited. In this study, physiological parameters provided by imaging techniques were analyzed by machine learning methods. Normalized difference vegetation index (NDVI) and normalized canopy temperature (canopy temperature - air temperature) were tested as predictors of disease by several algorithms. Among them, logistic regression analysis (LRA) trained on NDVI data showed the highest sensitivity and lowest rate of false negatives. This algorithm based on NDVI could be a quick and feasible method to detect trees potentially affected by white root rot in avocado orchards.
Why it matches plant phenotyping methodsアボカド樹の病害状態を、画像由来のNDVI・樹冠温度と機械学習で推定する手法が研究の中心であり、植物病害フェノタイピングに該当する。
abstractIn this study, physiological parameters provided by imaging techniques were analyzed by machine learning methods.
AvocadoRaman / spectroscopyFruitClassificationGrowth / development / phenology
Avocado, a climacteric fruit, exerts high rate of respiration and ethylene production and thereby subject to ripening during storage. Therefore, its ripening is a significant factor to impart optimum quality in postharvest storage. To understand the dynamics of ripening and to assess the degree of ripening in the avocado, electrical sensing technique is utilized in this study. In particular, electrical impedance spectroscopy (EIS) is found to uncover the physiological and structural characteristics in plants and vegetables and to follow physiological progressions due to environmental impacts. In this work, we present an approach that will integrate EIS and machine learning technique that allows us to monitor the ripening degree of the avocado. It is evident from our study that the impedance absolute magnitude of the avocado gradually decreases as the ripening stages (firm, breaking, ripe, and overripe) proceed at a particular frequency. In addition, principal component analysis shows that impedance magnitude (two principal components combined explain 99.95% variation) has better discrimination capabilities for ripening degrees compared to impedance phase angle, impedance real part, and impedance imaginary part. Our classifier utilizes two principal component features over 100 EIS responses and demonstrates classification over firm, breaking, ripe, and overripe stages with an accuracy of 90%, precision of 93%, recall of 90%, f1-score of 90%, and auc of 88%. The study offers plant scientists a low cost and nondestructive approach to monitor postharvest ripening process for quality control during storage.
Why it matches plant phenotyping methodsアボカド果実の成熟度という植物器官の状態を、電気インピーダンス分光法と機械学習で非破壊推定する方法が研究の中心であり、分類性能も評価している。
abstractwe present an approach that will integrate EIS and machine learning technique that allows us to monitor the ripening degree of the avocado.
In field (on tree) fruit sizing has value in assessing crop health and for yield estimation. As the mobile phone is a sensor and communication rich device carried by almost all farm staff, an Android application ("FruitSize") was developed for measurement of fruit size in field using the phone camera, with a typical assessment rate of 240 fruit per hour achieved. The application was based on imaging of fruit against a backboard with a scale using a mobile phone, with operational limits set on camera to object plane angle and camera to object distance. Image processing and object segmentation techniques available in the OpenCV library were used to segment the fruit from background in images to obtain fruit sizes. Phone camera parameters were accessed to allow calculation of fruit size, with camera to fruit perimeter distance obtained from fruit allometric relationships between fruit thickness and width. Phone geolocation data was also accessed, allowing for mapping fruits of data. Under controlled lighting, RMSEs of 3.4, 3.8, 2.4, and 2.0 mm were achieved in estimation of avocado, mandarin, navel orange, and apple fruit diameter, respectively. For mango fruit, RMSEs of 5.3 and 3.7 mm were achieved on length and width, benchmarked to manual caliper measurements, under controlled lighting, and RMSEs of 5.5 and 4.6 mm were obtained in-field under ambient lighting.
Why it matches plant phenotyping methodsスマートフォン画像から果実サイズを抽出するアプリケーションを開発し、手動ノギス測定との誤差検証も行っており、植物表現型取得法が研究の中心である。
abstractan Android application ("FruitSize") was developed for measurement of fruit size in field using the phone camera
UAS-based multi-spectral imagery is becoming increasingly popular for the improved monitoring and managing of various horticultural crops. However, for UAS data to be used as an industry standard for assessing tree structure and condition as well as production parameters, it is imperative that the appropriate data collection and pre-processing protocols are established to enable multi-temporal comparison. There are several UAS-based radiometric correction methods commonly used for precision agricultural purposes. However, their relative accuracies have not been assessed for data acquired in complex horticultural environments. This study assessed the variations in estimated surface reflectance values of different radiometric corrections applied to multi-spectral UAS imagery acquired in both avocado and banana orchards. We found that inaccurate calibration panel measurements, inaccurate signal-to-reflectance conversion, and high variation in geometry between illumination, surface, and sensor viewing produced significant radiometric variations in at-surface reflectance estimates. Potential solutions to address these limitations included appropriate panel deployment, site-specific sensor calibration, and appropriate BRDF correction. Future UAS based horticultural crop monitoring can benefit from the proposed solutions to radiometric corrections to ensure they are using comparable image-based maps of multi-temporal biophysical properties.
Why it matches plant phenotyping methods果樹園のUASマルチスペクトル画像に対する放射補正手法を比較・評価し、樹体構造・状態や生産関連パラメータの測定に必要な校正条件を検討しているため、画像ベース植物フェノタイピング手法が中心です。
abstractThis study assessed the variations in estimated surface reflectance values of different radiometric corrections applied to multi-spectral UAS imagery acquired in both avocado and banana orchards.
Understanding the net photosynthesis of plant canopies requires quantifying photosynthesis in challenging environments, principally due to the variable light intensities and qualities generated by sunlight interactions with clouds and surrounding foliage. The dynamics of sunflecks and rates of change in light intensity at the beginning and end of sustained light (SL) events makes photosynthetic measurements difficult, especially when dealing with less accessible parts of plant foliage. High time resolved photosynthetic monitoring from pulse amplitude modulated (PAM) fluorometers has limited applicability due to the invasive nature of frequently applied saturating flashes. An alternative approach used here provides remote (<5 m), high time resolution (10 s), PAM equivalent but minimally invasive measurements of photosynthetic parameters. We assessed the efficacy of the QA flash protocol from the Light-Induced Fluorescence Transient (LIFT) technique for monitoring photosynthesis in mature outer canopy leaves of potted Persea americana Mill. cv. Haas (Avocado) trees in a semi-controlled environment and outdoors. Initially we established that LIFT measurements were leaf angle independent between ±40° from perpendicular and moreover, that estimates of 685 nm reflectance (R685) from leaves of similar chlorophyll content provide a species dependent, but reasonable proxy for incident light intensity. Photosynthetic responses during brief light events (≤10 min), and the initial stages of SL events, showed similar declines in the quantum yield of photosystem II (ΦII) with large transient increases in 'constitutive loss processes' (ΦNO) prior to dissipation of excitation by non-photochemical quenching (ΦNPQ). Our results demonstrate the capacity of LIFT to monitor photosynthesis at a distance during highly dynamic light conditions that potentially may improve models of canopy photosynthesis and estimates of plant productivity. For example, generalized additive modelling performed on the 85 dynamic light events monitored identified negative relationships between light event length and ∆ΦII and ∆electron transport rate using either ∆photosynthetically active radiation or ∆R685 as indicators of leaf irradiance.
Why it matches plant phenotyping methodsLIFTによる遠隔・高時間分解能の光合成パラメータ取得法を評価し、動的光条件下での測定性能を検証しているため、植物フェノタイピング手法が中心である。
abstractAn alternative approach used here provides remote (<5 m), high time resolution (10 s), PAM equivalent but minimally invasive measurements of photosynthetic parameters.
The geometric characterization of tree crops is a necessary task to obtain simple though valuable variables such as height and width of tree tops and more complex variables such as porosity and tree top surface, in which this paper focuses. To obtain these variables, a ground-based mechatronic LiDAR system and a multi-size voxels algorithm have been developed. We test the LIDAR system and our algorithms for two cases: a single tree case and a multi-tree case. In the latter, we apply our algorithms in a tree row from an avocado grove in Chile, showing that our system is portable, accurate and can offer the farmer a useful tool for crop monitoring. In addition, we compare our approach with others previously published, showing that our system is more efficient when estimating porosity and crown surface, and offers more capabilities for the decision making process in agricultural activities.
Why it matches plant phenotyping methods樹冠の形態・構造形質（多孔性、樹冠表面、樹高・幅）を取得するLiDARシステムとボクセル解析法を開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractTo obtain these variables, a ground-based mechatronic LiDAR system and a multi-size voxels algorithm have been developed.
Ethene (C2H4), which is produced in plants as they mature, was used to study its photoacoustic properties using photoacoustic spectroscopy. Detection of trace amounts, with N2 gas, of C2H4 gas was also applied. The gas was tested in various conditions- temperature, concentration of the gas, gas cell length, and power of the laser to determine their effect on the photoacoustic signal, the ideal conditions to detect trace gas amounts, and concentration of C2H4 produced by an avocado and banana. A detection limit of 10 ppm was determined for pure C2H4. A detection of 5% and 13% (by volume) concentration of C2H4 produced for a ripening avocado and banana, respectively, in closed space.
Why it matches plant phenotyping methods植物由来エチレンを光音響分光で検出する測定法を条件検討・検出限界評価し、果実の成熟状態に関連するエチレン産生を測定しているため、植物状態の取得法が中心です。
abstractwas used to study its photoacoustic properties using photoacoustic spectroscopy
Poster presented at PAGXXV Subtropical fruits, like avocado and cherimoya, are key crops for food security in a wide range of countries, with an increasing commercial importance worldwide. Even though their importance is starting to be recognized and high throughput sequencing approaches are currently being used to characterize genome-wide patterns from natural diversity populations and breeding stocks, currently ontological available information for these subtropical fruits crops is scarce and often not based in internationally standardized formats. Thus, the challenge to correlate the expanding molecular information data available with plant phenotype and crop traits remains an important issue in breeding programs for these crops. With the aim to facilitate future analyses we present a controlled vocabulary for harmonizing the annotation of phenotypic and genomic data for these crops. These new ontologies represent an extended ontology to fit avocado and cherimoya traits commonly used in variety descriptions, mainly established by Biodiversity International and the International Union for the Protection of New Varieties of Plants (UPOV), but also custom ad hoc descriptors. The developed ontology includes measurable or observable characteristics of plants as well as abiotic and biotic stress susceptibility. The resource is available in standard OBO formats ready to be used in GMOD and Tripal inspired biological databases to allow data sharing and reusability. The approach followed here can be of interest to other crops in which standardized ontologies are still missing
Why it matches plant phenotyping methodsアボカドとチェリモヤの植物形質を標準化・相互運用する表現型オントロジーを開発した研究であり、形質データ記述のための再利用可能な方法・リソースが中心である。
abstractwe present a controlled vocabulary for harmonizing the annotation of phenotypic and genomic data for these crops.
The thermal behaviour of an orchard is intrinsically related to the plant physiological status and it is commonly observed using thermal imagery, in most cases, provided by a drone or by a satellite. Such remote sensing methods are currently popular since they allow to analyse large amounts of land data with few sensor readings. However, they are restricted by the spatial resolution of the images, which always correspond to top views of the canopies. The latter does not allow for a side recording or analysis of the orchard. In this work, we design and evaluate a portable ground-based system for a manual thermal and geometrical characterisation of an orchard, merging thermal images with LiDAR-based range readings in order to obtain a 3D thermal reconstruction of the crop to overcome the previously mentioned issues. The proposed system can work in Global Navigation Satellite System (GNSS) denied environments and delivers multiple views of the orchard, offering the user a three-dimensional view of the thermal behaviour of the grove. Further, the implemented algorithm classifies points from the LiDAR measurements which correspond to the canopy using a supervised classifier. Later, a matching procedure is performed between such points and the thermal information provided by the thermal camera. In order to reconstruct the entire orchard or only a section of the grove, several frames are registered using the Iterative Closest Point algorithm. The system was tested in two conditions: in laboratory and in field within a plantation of Hass avocado, which is one of the main fruit trees growing in Chile, and its performance is compared with an LI-6400 Infra-red Gas Analyser (IRGA) portable photosynthesis system (LI-COR, Lincoln, NE).
Why it matches plant phenotyping methodsLiDARと熱画像を融合し、果樹の樹冠形状と熱的・生理的状態を3D再構成する地上型計測システムを設計・評価しており、植物フェノタイピング手法が中心である。
abstractwe design and evaluate a portable ground-based system for a manual thermal and geometrical characterisation of an orchard
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Rapid developments in remote‐sensing of vegetation and high‐throughput precision plant phenotyping promise a range of real‐life applications using leaf optical properties for non‐destructive assessment of plant performance. Use of leaf optical properties for assessing plant performance requires the ability to use photosynthetic pigments as proxies for physiological properties and the ability to detect these pigments fast, reliably and at low cost. We describe a simple and cost‐effective protocol for the rapid analysis of chlorophylls, carotenoids and tocopherols using high‐performance liquid chromatography (HPLC). Many existing methods are based on the expensive solvent acetonitrile, take a long time or do not include lutein epoxide and α‐carotene. We aimed to develop an HPLC method which separates all major chlorophylls and carotenoids as well as lutein epoxide, α‐carotene and α‐tocopherol. Using a C30‐column and a mobile phase with a gradient of methanol, methyl‐tert‐butyl‐ether (MTBE) and water, our method separates the above pigments and isoprenoids within 28 min. The broad applicability of our method is demonstrated using samples from various plant species and tissue types, e.g. leaves of Arabidopsis and avocado plants, several deciduous and conifer tree species, various crops, stems of parasitic dodder, fruit of tomato, roots of carrots and Chlorella algae. In comparison to previous methods, our method is very affordable, fast and versatile and can be used to analyze all major photosynthetic pigments that contribute to changes in leaf optical properties and which are of interest in most ecophysiological studies.
Why it matches plant phenotyping methods植物の生理状態・性能に関係する色素を迅速かつ低コストに定量するHPLC法の開発が中心で、植物フェノタイピングおよび葉の光学特性評価への利用を明示している。
titleFast detection of leaf pigments and isoprenoids for ecophysiological studies, plant phenotyping and validating remote‐sensing of vegetation