Farmers must be able to estimate their crop yields at various growth stages for effective management of their farms and to enable them to interact with cooperatives or traders as early as possible. Here we developed an AI-based cocoa pod detection method using low resolution colour images of cocoa trees on farms in Côte d'Ivoire. We compared nano and extra-large architectures of six neural networks, including Faster RCNN, Baidu's Real-Time Detection Transformer (RTDetr), Detr-ResNet Vision Transformer (ViT), YOLOv5, YOLOv8 and YOLOv11. These networks were trained with 7,850 annotated cocoa pods on 400 low resolution images, and validated in two independent datasets: a 42 low resolution images containing 990 annotated pods, and a 100 low resolution images containing 2,400 annotated pods. The performances of the nano YOLOv8 and YOLOv11 networks were 2% higher than that of the RTDetr networks and 5% higher than that of the YOLOv5, ViT and Faster RCNN networks with an F1-score of 77% on all images and up to 90% on foreground trees. The dominance of nano architectures suggests that the extra-large architectures, which contain 20-30-times more neurons, may not have been fully trained. The study of learning performance curves showed that extra-large networks were unable to outperform nano networks, which contradicts the theory. After review, the annotated dataset was found to contain inconsistencies. The inconsistency of the training and validation data and their limited quantity restricted the objectivity of comparisons between network architectures. Finally, although the average detection performance of RTDetr for cocoa pods was only 2% lower than that of the YOLOv8 network, it was definitively excluded from the candidate models because its per-image processing time was 15-20% higher than that of YOLOv8 and YOLOv11. However, with a performance sensitivity to data of less than 0.5%, YOLOv8 Nano became the best option.
Why it matches plant phenotyping methodsカカオ果実を画像から検出・定量するAI手法を開発し、複数モデルと独立データセットで性能比較・検証しており、植物フェノタイピング手法が中心である。
abstractHere we developed an AI-based cocoa pod detection method using low resolution colour images of cocoa trees on farms in Côte d'Ivoire.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicData Availability: The data used in the study can be downloaded from CIRAD’s data verse at https://doi.org/10.18167/DVN1/8COJBB .Open asset ↗10.18167/DVN1/8COJBBlines:129-140Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Accurate detection of cocoa pod diseases is vital to reducing yield losses and supporting sustainable agriculture. Although deep learning models have shown promise in plant disease classification, their performance often varies between datasets due to limitations in feature extraction and generalisation. This study introduces a Learnable Gated Fusion Convolutional Block Attention Module (LGF-CBAM) integrated with a ResNetV2-101 backbone to improve discriminative feature learning and improve robustness in cocoa disease classification. Unlike the standard CBAM, which processes attention modules sequentially, LGF-CBAM adaptively balances the importance of spatial and channel cues through trainable gating parameters normalized with a softmax function. Incorporating LGF-CBAM provided outstanding results on the Cocoa_Pod_Disease_Gh dataset, achieving 98.95% accuracy along with F1 and PPV scores of 99.11%. The cross-dataset evaluation confirmed robustness, with accuracies of 98.53% on Cocoa Diseases (YOLOv4), 97.96% on Black and Borer Pod Rot, and 96.19% on Cacao Diseases in Davao. Although greater variability in the Coffee and Cocoa dataset reduced accuracy to 94.00%, the model still maintained strong adaptability under diverse conditions. These findings establish LGF-CBAM as a state-of-the-art framework that outperforms all other referenced systems, offering high accuracy, stability, and generalization. In general, this research contributes to a novel attention-based deep learning framework that can support early and reliable identification of cocoa pod diseases, providing a scalable solution for precision agriculture.
Why it matches plant phenotyping methodsカカオ果実の病害状態を画像から分類する深層学習手法を開発し、複数データセットで性能・頑健性を評価しており、植物フェノタイピング手法が研究の中心である。
abstractThis study introduces a Learnable Gated Fusion Convolutional Block Attention Module (LGF-CBAM) integrated with a ResNetV2-101 backbone to improve discriminative feature learning and improve robustness in cocoa disease classification.
Reproduction assets foundThe authors' primary plant-phenotyping asset is the Cocoa_Pod_Disease_Gh image dataset, publicly deposited on Figshare with an explicit Data Availability statement and DOI. The Kaggle/Roboflow datasets are cited prior external datasets used for cross-dataset evaluation, not paper-specific deposits, so they are excludedDataset · publicThe data that support the findings of this study is available at https://figshare.com/articles/dataset/Cocoa_Disease_Datasets/31294003. https://doi.org/10.6084/m9.figshare.31294003.Open asset ↗figshare · 10.6084/m9.figshare.31294003html-lines:1039-1062Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published16 Apr 2026British Journal of Computer Networking and Information TechnologyCited by 0 · OpenAlex ↗
The application of computer vision in precision agriculture has demonstrated considerable promise in automated plant disease detection. However, the effectiveness of such approaches is strongly dependent on the availability of high-quality, domain-specific datasets, particularly for deployment on resource-constrained edge devices. This paper introduces CocoaDetectDB, a publicly available image dataset developed for the detection of cocoa plant diseases under Tiny Machine Learning (TinyML) constraints. The dataset comprises images of healthy cocoa pods and three major cocoa diseases—Cocoa Black Pod Disease (CBD), Cocoa Swollen Shoot Virus Disease (CSSVD), and Frosty Pod Rot (FPR)—captured under real-world field conditions and supplemented with openly accessible public data. Images were curated, cleaned, and resized to a uniform resolution of 112 × 112 pixels to support low-memory and low-power inference. To validate the suitability of the dataset for automated disease classification, baseline experiments were conducted using MobileNetV2 and a lightweight quantized TensorFlow Lite model. Experimental results demonstrate classification accuracies of 99.13% and 93.75%, respectively, indicating that CocoaDetectDB contains sufficiently discriminative features for both conventional lightweight models and TinyML deployment. The dataset is intended to support future research in cocoa disease detection, edge AI, and resource-efficient agricultural monitoring systems.
Why it matches plant phenotyping methodsココア植物の病害状態を画像で判定する公開データセットを構築し、軽量モデルで適合性を検証しており、画像ベースの植物表現型取得・分類が中心である。
abstractThis paper introduces CocoaDetectDB, a publicly available image dataset developed for the detection of cocoa plant diseases under Tiny Machine Learning (TinyML) constraints.
Precision agriculture is an essential approach to improving productivity, sustainability, and resilience in modern farming systems amid global changes. Drone-based monitoring represents a significant part of this agricultural transition because it can collect data across extensive regions and across multiple crop growth stages. Perennial cash crops are associated with farmers’ livelihoods and with links to global supply chains. Yield estimates for crops like cocoa and cashew are challenging because of the difficulty of measuring yields under very high canopy cover, erratic fruiting, and inefficient conventional in-field methodologies that involve excessive human error, labour, and time constraints. This paper proposes a hybrid deep learning model, GradTabViTNet, for multi-stage crop condition and yield estimation. The architecture integrates Vision Transformers (ViT) for geospatial attribute extraction, TabNet for attention-based tabular data analysis, CSRNet for accurate object counting in dense canopy environments, and Grad-CAM to enable interpretability by marking key regions in drone images. Classification and counting features are then combined using LightGBM regression to accurately estimate yield. Experimental evaluation using the cashew and cocoa datasets demonstrates that the proposed GradTabViTNet model outperforms existing methods, achieving 99.25% accuracy of 99.10%, precision 98.40%, recall, and 99.27% an F1-score of. The fusion of aerial monitoring with interpretable deep learning methods creates an extensible, stable approach for crop yield prediction, enabling sustainable agriculture, enhanced decision-making for farmers, and stronger food security through data-driven management of high-value perennial crops.
Why it matches plant phenotyping methodsドローン画像から樹冠下の作物状態と収量を推定する深層学習手法を提案し、カシューナッツ・カカオデータセットで既存手法と比較評価しているため、植物形質取得・推定法が中心である。
abstractThis paper proposes a hybrid deep learning model, GradTabViTNet, for multi-stage crop condition and yield estimation.
Abstract In cocoa agroforestry systems (AFS), tree-cover is a fundamental driver of ecosystem functioning and crop productivity. However, for smallholders, implementing specific cover targets remains challenging because tree-cover is difficult to measure without specialized equipment and technical skills. Consequently, sustainability standards and technical guidelines typically rely on simplified structural metrics, such as tree density or basal area, as proxies for cover. This study addresses the technical disconnect between these simplified metrics and actual canopy outcomes. Using data from 150 plots in Côte d’Ivoire, we developed a Beta regression model within a Bayesian framework to estimate tree-cover from standard inventory data. Our results show that tree density or basal area alone are unreliable predictors of cover, whereas a model integrating both variables achieves high accuracy (R2 = 0.89). Our results also demonstrate that existing guidelines can lead to highly variable canopy conditions that deviate markedly from intended cover targets. We propose a state-space representation as a decision-support tool to help smallholders estimate tree-cover and to provide a scientific basis for updating existing policies and standards. This framework allows smallholders to visualize management trajectories, driven by recruitment, tree growth, and mortality, to steer their systems toward ecologically meaningful cover targets.
Why it matches plant phenotyping methods樹冠被覆率という植物群落の構造形質を、標準インベントリデータから推定するベイズ回帰モデルを開発・評価しており、推定精度も検証されているため、方法が中心的です。
abstractwe developed a Beta regression model within a Bayesian framework to estimate tree-cover from standard inventory data.
Agroforestry systems might contribute to balance some of the production, environmental and social challenges associated with agricultural intensification in tropical regions. However, the intricate functional dynamics within agroecosystems, combined with their diverse objectives, make it difficult to maximise productivity and identifying factors that constrain crop yields. Canopy structural traits strongly influence light distribution in agroforestry systems, affecting crop variability in light-use efficiency and productivity. Yet, information on 3D vegetation structure in agroforestry systems remains scarce, despite its potential to provide valuable information to better understand the functional complexity of these systems. Our workflow overcome this limitation by incorporating Terrestrial Laser Scanning (TLS) technology and a voxelization approach for light ray tracing (AMAPVox). In this context, this study aims to determine how TLS data, processed using a voxelization approach, can be applied in multi-strata agroforestry systems to quantify the three-dimensional (3D) distribution of Plant Area Density (PAD) across vegetation strata and the total Plant Area Index (PAI). We used detailed multi-scan voxelized data from 28 experimental plots established in Côte d’Ivoire with different species compositions and planting arrangements to quantify the 3D distribution of PAD and key structural traits (PAI, light attenuation and transmittance) at multiple spatial resolutions. Validation with estimates of light measurements based on hemispherical photographs at tree level showed a high level of concordance (R² > 0.42; p-value < 0.05) in estimating plant area index (PAI). Species composition, rather than planting arrangement, notably influenced the vertical distribution of PAD and canopy structural traits. PAI in the plots ranged from 4.94 m² m⁻² to 22.31 m² m⁻². The proposed approach, using TLS data and a voxel-based methodology, enables high-resolution modelling of canopy structural traits in multi-strata agroforestry systems. These results provide highly detailed measurements of key crop yield indicators, allowing decision support to develop management activities that increase crop production while minimising inputs and maintaining ecosystem services.
Why it matches plant phenotyping methodsTLSとボクセル化によるワークフローを用いて、植物群落の3D構造形質（PAD、PAI、光減衰・透過）を定量化し、光学測定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。
abstractOur workflow overcome this limitation by incorporating Terrestrial Laser Scanning (TLS) technology and a voxelization approach for light ray tracing (AMAPVox).
Premise This study investigates advanced training techniques to improve the performance of convolutional neural networks for disease detection in cocoa, Theobroma cacao . Methods Despite recent stagnation in accuracy improvements in computer vision for image classification, our research demonstrates significant advancements in performance through semi-supervised learning, specialised loss functions, and the inclusion of a non-cocoa class. Results Semi-supervised learning reduced overfitting and enhanced generalisability, particularly for subtle symptoms. The non-cocoa class exposed models to a broad range of relevant features, significantly improving model robustness and performance in difficult cases. Grad-CAM for qualitative assessment provided valuable insights into model behaviour, highlighting cases of overfitting missed by summary statistics. We also describe dynamic focal loss, a novel loss function that uses an empirical measure of difficulty to weight each image. Our results suggest that while PhytNet shows promise in terms of computational efficiency and superior handling of difficult images, ResNet18 with semi-supervised learning and dynamic focal loss emerged as the strongest contender for real-world deployment. Discussion This research underscores the potential of semi-supervised learning and advanced loss functions in enhancing the applicability of deep learning models in agricultural disease management. It also presents a new high-quality benchmark dataset of 7220 images of diseased and healthy cocoa trees, offering a much greater and more realistic challenge than the Plan Village dataset.
Why it matches plant phenotyping methodsカカオ葉・樹体の病徴画像から植物の病害状態を推定する深層学習手法を開発・比較し、性能評価とベンチマークデータセット構築を行っており、フェノタイピング手法が中心である。
abstractThis study investigates advanced training techniques to improve the performance of convolutional neural networks for disease detection in cocoa, Theobroma cacao .
Reproduction assets foundThe paper's data availability statement explicitly provides the paper-specific cocoa image dataset and the FAIGB dataset on OSF, plus authors' analysis code on GitHub, all with public URLs.Dataset · publicThe cocoa image data is available at https://osf.io/2fw6gOpen asset ↗osf · 2fw6glines:753-923Dataset · publicthe FAIGB web‐scraped dataset of crop disease images is available at https://osf.io/nuafhOpen asset ↗osf · nuafhlines:753-923Code · publicAll code necessary to reproduce these results is available on GitHub ( https://github.com/jrsykes/CocoaReader/tree/main/CocoaNet/PhytNet_Cocoa )Open asset ↗github · jrsykes/CocoaReaderlines:753-923Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 6 Sept 2026
Improving cacao yield, a key objective in post-domestication crop improvement, remains a primary goal for breeders, but progress is often hindered by the confounding effects of population structure. To overcome this, we analyzed 346 diverse cacao accessions using an ML-based association mapping framework (with and without population structure adjustment) and a phenotype-only ML prediction of yield. By correcting for population structure, our Bootstrap Forest-based GWAS revealed association signals that showed consistent enrichment for ribosome and protein-synthesis functions, and a recurrent subset of SNPs with high importance appeared across multiple yield components, including pod index and seed number. In parallel, a Neural Network model was utilized to identify cotyledon mass and length as the most powerful predictors for total wet bean mass (R² = 0.715 by repeated five-fold cross-validation), suggesting a practical, low-cost screening proxy for breeding). Collectively, this study delivers a robust genetic framework and a novel predictive tool to accelerate the development of high-yielding cacao varieties through the early identification of elite clones.
Why it matches plant phenotyping methodsカカオの形質（湿重量収量）を、測定可能な種子形質から予測するニューラルネットワークを開発・検証しており、低コストな表現型スクリーニング手法が研究の中心です。
Abstract Food security is a vital aspect of the United Nations’ Sustainable Development Goals (SDGs) which aims to promote sustainable farming in the world. Farming-driven economies such as Ghana are faced with challenges due to plant diseases. Cacao, a vital crop in Ghana is severely impacted with diseases which affect its yield and decrease exports revenue through reduced exports. Leveraging deep learning techniques offers an effective solution for early detection of diseases in cacao plants. This study adopts a comprehensive approach, starting with an Exploratory Data Analysis (EDA) of the dataset containing images of both healthy and diseased cacao plants from Ghanaian farms. Using exploratory data analysis (EDA), we can identify patterns and understand the characteristics of the dataset, laying a solid foundation for developing robust machine learning models tailored to the specific challenges faced by Ghanaian cacao farmers. Our approach involves developing and evaluating deep learning models to detect and classify cacao plant diseases. These models are designed with the Predictability, Compatibility, and Stability (PCS) framework in mind, ensuring reliability and effectiveness in disease detection. The custom convolution neural network (CNN) model outperformed other models considered in experimental analysis. This study aims to revolutionize cacao farming through precise, stable, and ethical deep learning solutions, ultimately enhancing crop resilience, productivity, and the livelihood of Ghanaian farmers.
Why it matches plant phenotyping methodsカカオ植物画像から病害を検出・分類する深層学習手法の開発と評価が中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法に該当する。
abstractdeveloping and evaluating deep learning models to detect and classify cacao plant diseases
Premise Automated disease, weed, and crop classification with computer vision will be invaluable in the future of agriculture. However, existing model architectures like ResNet, EfficientNet, and ConvNeXt often underperform on smaller, specialised datasets typical of such projects. Methods We address this gap with informed data collection and the development of a new convolutional neural network architecture, PhytNet. Utilising a novel dataset of infrared cocoa tree images, we demonstrate PhytNet's development and compare its performance with existing architectures. Data collection was informed by spectroscopy data, which provided useful insights into the spectral characteristics of cocoa trees. Cocoa was chosen as a focal species due to the diverse pathology of its diseases, which pose significant challenges for detection. Results ResNet18 showed some signs of overfitting, while EfficientNet variants showed distinct signs of overfitting. By contrast, PhytNet displayed excellent attention to relevant features, almost no overfitting, and an exceptionally low computation cost of 1.19 GFLOPS. Conclusions We show that PhytNet is a promising candidate for rapid disease or plant classification and for precise localisation of disease symptoms for autonomous systems. We also show that the most informative light spectra for detecting cocoa disease are outside the visible spectrum and that efforts to detect disease in cocoa should be focused on local symptoms, rather than the systemic effects of disease.
Why it matches plant phenotyping methods植物病害画像から症状を検出・局在化するCNNアーキテクチャPhytNetを開発し、既存モデルと比較検証しており、植物表現型取得・抽出法が中心である。
abstractthe development of a new convolutional neural network architecture, PhytNet
Reproduction assets foundThe paper's cocoa disease image/spectroscopy data are deposited on OSF (freely accessible via the provided link) and the PhytNet training/optimisation code is publicly available on GitHub. Both are paper-specific, public, and actionable.Code · publicThe code to optimise and train PhytNet for your data can be found at: https://Github.com/jrsykes/PhytNet .Open asset ↗Github · jrsykes/PhytNetlines:214-297Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Abstract Agriculture in India is a vital sector that contains a major portion of the population and impacts substantially the country’s economy. Cocoa is a crop that has commercial importance and is used for the production of chocolates. It is one of the main crops cultivated in south India due to the humid tropical climate. However, the cocoa plant is susceptible to various diseases caused by bacteria, viruses, and pests resulting in yield losses. Visual analysis is a subjective and time-consuming process. Further, farmers use improper pesticides to prevent diseases, and this will degrade the plant and soil quality. To overcome these problems, this paper proposes an automatic cocoa plant disease detection and pesticide recommendation system using computer vision and deep learning techniques. The proposed system was evaluated on several cocoa plant images, and an accuracy of 97.36% was obtained in disease classification. The proposed system can help cocoa farmers in the detection of cocoa plant diseases in the early stage and reduce the use of excessive pesticides, thus promoting sustainable agriculture practices.
Why it matches plant phenotyping methodsココア植物画像から病害状態を推定する画像・深層学習手法が研究の中心であり、病害分類性能も評価しているため、植物フェノタイピング手法として収録する。
abstractthis paper proposes an automatic cocoa plant disease detection and pesticide recommendation system using computer vision and deep learning techniques.
Abstract Plant pathogens can decimate crops and render the local cultivation of a species unprofitable. In extreme cases this has caused famine and economic collapse. Timing is vital in treating crop diseases, and the use of computer vision for precise disease detection and timing of pesticide application is gaining popularity. Computer vision can reduce labour costs, prevent misdiagnosis of disease, and prevent misapplication of pesticides. Pesticide misapplication is both financially costly and can exacerbate pesticide resistance and pollution. Here, we review the application and development of computer vision and machine learning methods for the detection of plant disease. This review goes beyond the scope of previous works to discuss important technical concepts and considerations when applying computer vision to plant pathology. We present new case studies on adapting standard computer vision methods and review techniques for acquiring training data, the use of diagnostic tools from biology, and the inspection of informative features. In addition to an in‐depth discussion of convolutional neural networks (CNNs) and transformers, we also highlight the strengths of methods such as support vector machines and evolved neural networks. We discuss the benefits of carefully curating training data and consider situations where less computationally expensive techniques are advantageous. This includes a comparison of popular model architectures and a guide to their implementation.
Why it matches plant phenotyping methods植物病害を対象としたコンピュータビジョンによる症状・病害の検出手法を中心に扱うレビューであり、植物フェノタイピング手法の方法論的整理と評価が主題である。
abstractHere, we review the application and development of computer vision and machine learning methods for the detection of plant disease.
Reproduction assets foundThe paper's data availability statement provides public OSF deposits (view-only links) containing image data, annotations, training data, and semi-supervised model weights for the cocoa disease-detection case studies, plus public GitHub repositories with the authors' custom training/analysis code (CocoaReader, CocoaNetDataset · publicThe image data, annotations, and link to the accompanying GitHub repository for Case Study 1 can be found at: https://osf.io/79kx3/?view_only=4a2c1dccee1a4baeb85de5002c702f10 .Open asset ↗osflines:411-466Dataset · publicFor Case Study 2, the data used to train the initial supervised model, the .csv search terms file for the below web scraper, and the final semi‐supervised model weights can be found at: https://osf.io/h5gj7/?view_only=dbf9f245e21a41e185f5b73e718b4cad .Open asset ↗osflines:411-466Code · publicThe custom code used to train both the initial model and the final semi‐supervised model can be found at: https://github.com/jrsykes/CocoaReader/blob/main/PlantNotPlant .Open asset ↗github · jrsykes/CocoaReaderlines:411-466Code · publicThe custom code to run the sweep in Case Study 4 can be found in the following GitHub repository: https://github.com/jrsykes/CocoaReader/tree/main/CocoaNet .Open asset ↗github · jrsykes/CocoaReaderlines:411-466Dataset · publicThe data used to generate these results and the full wandb report can be found at: https://osf.io/2fw6g/?view_only=adc66ba66f83465a9e7b111515a60bf2 .Open asset ↗osflines:411-466Code · publicThe “contaminated” data used to train the semi‐supervised model were generated using the code at: https://github.com/jrsykes/Google-Image-Scraper .Open asset ↗github · jrsykes/Google-Image-Scraperlines:411-466Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Cocoa cultivation is the basis for chocolate production; it has a unique aroma that makes it useful in the production of snacks and usable for cooking or baking. The maximum harvest period of cocoa is normally once or twice a year and spread over several months, depending on the country. Determining the best harvesting period for cocoa pods plays a major role in the export process and the pods quality. The degree of ripening of the pods affects the quality of the resulting beans. Also, unripe pods do not have enough sugar and may prevent proper bean fermentation. As for too-mature pods, they are usually dry, and their beans may germinate inside the pods, or they may develop a fungal disease and cannot be used. Computer-based determination of the ripeness of cocoa pods throughout image analysis could facilitate massive cocoa ripeness detection. Recent technological advances in computing power, communication systems, and machine learning techniques provide opportunities for agricultural engineering and computer scientists to meet the demands of the manual. The need for diverse and representative sets of pod images is essential for developing and testing automatic cocoa pod maturity detection systems. In this perspective, we collected images of cocoa pods to set up a database of cocoa pods of the Côte d'Ivoire named CocoaMFDB. We performed a pre-processing step using the CLAHE algorithm to improve the quality of the images since the effect of the light was not controlled on our data set. CocoaMFDB allows the characterization of cocoa pods according to their maturity level and provides information on the pod family for each image. Our dataset comprises three large families, namely Amelonado, Angoleta, and Guiana, grouped into two maturity categories: the ripe and unripe pods. It is, therefore, perfect for developing and evaluating image analysis algorithms for future research.
Why it matches plant phenotyping methodsカカオ果実の成熟度という植物器官形質を対象とする画像データセットを構築し、画像解析アルゴリズムの開発・評価用に提供しているため、フェノタイピング手法・データセットが中心です。
abstractwe collected images of cocoa pods to set up a database of cocoa pods of the Côte d'Ivoire named CocoaMFDB.
Reproduction assets foundThe paper is a data descriptor for CocoaMFDB, a public dataset of cocoa pod images (maturity/family) with PASCAL VOC XML annotations, deposited on Mendeley Data with a direct URL and DOI matching an allowed URL.Dataset · publiced pods.
Data source location
The images of cocoa pods are from the plantations of Yakassé 1, a village of Grand Bassam first capital of Côte d'Ivoire with a Latitude and longitude of 5°12′42″ north, 3°44′19″.
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/9msjjh3np6.2
Direct URL to data: https://data.mendeley.com/datasets/9msjjh3np6/2
Value of the Data
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Images of cocoa pods obtained will be used in identifying cocoa types and varieties.
•Open asset ↗Mendeley Data · 10.17632/9msjjh3np6.2lines:1-52Plant phenotyping relevance match · UnverifiedEurope PMC · checked 8 Sept 2026
The utilization of computer vision in smart farming is becoming a trend in constructing an agricultural automation scheme. Deep learning (DL) is famous for the accurate approach to addressing the tasks in computer vision, such as object detection and image classification. The superiority of the deep learning model on the smart farming application, called Progressive Contextual Excitation Network (PCENet), has also been studied in our recent study to classify cocoa bean images. However, the assessment of the computational time on the PCENet model shows that the original model is only 0.101s or 9.9 FPS on the Jetson Nano as the edge platform. Therefore, this research demonstrates the compression technique to accelerate the PCENet model using pruning filters. From our experiment, we can accelerate the current model and achieve 16.7 FPS assessed in the Jetson Nano. Moreover, the accuracy of the compressed model can be maintained at 86.1%, while the original model is 86.8%. In addition, our approach is more accurate than ResNet18 as the state-of-the-art only reaches 82.7%. The assessment using the corn leaf disease dataset indicates that the compressed model can achieve an accuracy of 97.5%, while the accuracy of the original PCENet is 97.7%.
Why it matches plant phenotyping methods植物画像から病害状態を分類する深層学習モデルについて、枝刈りによる圧縮・高速化と精度検証を主題としており、表現型推定手法の開発・検証が中心である。
abstractthis research demonstrates the compression technique to accelerate the PCENet model using pruning filters.
Cocoa is an important commodity crop, not only to produce chocolate, one of the most complex products from the sensory perspective, but one that commonly grows in developing countries close to the tropics. This paper presents novel techniques applied using cover photography and a novel computer application (VitiCanopy) to assess the canopy architecture of cocoa trees in a commercial plantation in Queensland, Australia. From the cocoa trees monitored, pod samples were collected, fermented, dried, and ground to obtain the aroma profile per tree using gas chromatography. The canopy architecture data were used as inputs in an artificial neural network (ANN) algorithm, with the aroma profile, considering six main aromas, as targets. The ANN model rendered high accuracy (correlation coefficient (R) = 0.82; mean squared error (MSE) = 0.09) with no overfitting. The model was then applied to an aerial image of the whole cocoa field studied to produce canopy vigor, and aroma profile maps up to the tree-by-tree scale. The tool developed could significantly aid the canopy management practices in cocoa trees, which have a direct effect on cocoa quality.
Why it matches plant phenotyping methodsカバー写真とVitiCanopyによるココア樹の樹冠構造・活力の取得、およびANNによる樹ごとの推定・マッピングが研究の中心であり、植物表現型の実質的な取得・解析手法を扱っている。
abstractThis paper presents novel techniques applied using cover photography and a novel computer application (VitiCanopy) to assess the canopy architecture of cocoa trees in a commercial plantation in Queensland, Australia.
Cocoa is an important commodity crop not only to produce one of the most complex products such as chocolate from the sensory perspective, but one that commonly grows in developing countries close to the tropics. This paper presents novel techniques applied using cover photography and a novel computer application (VitiCanopy) to assess the canopy architecture of cocoa trees in a commercial plantation in Queensland, Australia. From the cocoa trees monitored, pod samples were collected, fermented, dried and grinded to obtain the aroma profile per tree using gas chromatography. The canopy architecture data were used as inputs in an artificial neural network (ANN) algorithm and the aroma profile considering six main aromas as targets. The ANN model rendered high accuracy (R = 0.82; MSE = 0.09) with no overfitting. The model was then applied to a satellite image from the whole cocoa field studied to produce canopy vigor and aroma profile maps up to the tree-by-tree scale. The tool developed could aid significantly the canopy management practices in cocoa trees that have a direct effect on cocoa quality.
Why it matches plant phenotyping methodsカバー写真とVitiCanopyによるカカオ樹冠構造の取得・評価、およびANNによる樹冠形質からの推定が研究の中心であり、単なる生物学的実験での routine 測定ではない。
abstractThis paper presents novel techniques applied using cover photography and a novel computer application (VitiCanopy) to assess the canopy architecture of cocoa trees in a commercial plantation in Queensland, Australia.
Pest control strategies for crop diseases highly depend on visual inspection to assess the severity of the infection, which usually lead to inconsistencies: either over or under assessment. These inconsistencies could be attributed to the limitations of humans to perceive small differences. A more precise disease assessment is needed for better pest management decision, which will result to a more efficient utilization and allocation of resources for farm inputs. This translates to a better income for cacao farmers. This paper introduces a mobile application named AuToDiDAC or Automated Tool for Disease Detection and Assessment for Cacao Black Pod Rot (BPR). AuToDiDAC automatically detects, separates, and assesses the infection level of BPR in cacao through image processing and machine learning techniques. It gives the farmers the capacity to objectively monitor and report the infection level of the BPR compared to the common visual rating for plant disease level of infection. Pixel-level accuracy test of the tool showed an average of 84% accuracy on an independent test set of ten cacao pod images.
Why it matches plant phenotyping methodsカカオ果実の病徴を画像処理・機械学習で検出、分離し、感染レベルを評価する手法と精度検証が研究の中心であるため、植物病害状態のフェノタイピング手法として含める。
abstractThis paper introduces a mobile application named AuToDiDAC or Automated Tool for Disease Detection and Assessment for Cacao Black Pod Rot (BPR).