{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T05:40:17Z","timestamp":1776922817747,"version":"3.51.2"},"reference-count":91,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:00:00Z","timestamp":1774483200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers and Electronics in Agriculture"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.compag.2026.111717","type":"journal-article","created":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T19:51:43Z","timestamp":1774727503000},"page":"111717","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Two-stage deep learning model for non-destructive leaf counting in tomato crops"],"prefix":"10.1016","volume":"247","author":[{"given":"Amanullah","family":"Quamer","sequence":"first","affiliation":[]},{"given":"Nima","family":"Asgari","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9802-3056","authenticated-orcid":false,"given":"Joshua M","family":"Pearce","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.compag.2026.111717_b0005","doi-asserted-by":"crossref","first-page":"71557","DOI":"10.1109\/ACCESS.2023.3293850","article-title":"Optimal efficient energy production by PV module tilt-orientation prediction without compromising crop-light demands in agrivoltaic systems","volume":"11","author":"Abidin","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.compag.2026.111717_b0010","doi-asserted-by":"crossref","unstructured":"Aghamohammadesmaeilketabforoosh, K., Nikan, S., Antonini, G., Pearce, J.M., 2024. Optimizing Strawberry Disease and Quality Detection with Vision Transformers and Attention-Based Convolutional Neural Networks. Foods 13, 1869. https:\/\/doi.org\/10.3390\/foods13121869.","DOI":"10.3390\/foods13121869"},{"key":"10.1016\/j.compag.2026.111717_b0015","doi-asserted-by":"crossref","unstructured":"Aghamohammadesmaeilketabforoosh, K., Parfitt, J., Nikan, S., Pearce, J.M., 2025. From blender to farm: Transforming controlled environment agriculture with synthetic data and SwinUNet for precision crop monitoring. PLOS One 20, e0322189. https:\/\/doi.org\/10.1371\/journal.pone.0322189.","DOI":"10.1371\/journal.pone.0322189"},{"key":"10.1016\/j.compag.2026.111717_b0020","doi-asserted-by":"crossref","first-page":"478","DOI":"10.3390\/agriengineering3030032","article-title":"A mobile-based system for detecting plant leaf diseases using deep learning","volume":"3","author":"Ahmed","year":"2021","journal-title":"AgriEngineering"},{"key":"10.1016\/j.compag.2026.111717_b0025","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1007\/s40747-021-00536-1","article-title":"A novel deep learning method for detection and classification of plant diseases","volume":"8","author":"Albattah","year":"2022","journal-title":"Complex Intell. Syst."},{"key":"10.1016\/j.compag.2026.111717_b0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.scienta.2019.108768","article-title":"Morphology, yield and quality of greenhouse tomato cultivation with flexible photovoltaic rooftop panels (Almer\u00eda-Spain)","volume":"257","author":"Aroca-Delgado","year":"2019","journal-title":"Sci. Hortic."},{"key":"10.1016\/j.compag.2026.111717_b0035","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s10681-022-02992-3","article-title":"Deep learning: as the new frontier in high-throughput plant phenotyping","volume":"218","author":"Arya","year":"2022","journal-title":"Euphytica"},{"key":"10.1016\/j.compag.2026.111717_b0040","author":"Asgari","year":"2025","journal-title":"Lighting and Revenue Analysis of Grow Lights in Agrivoltaic Agrotunnel for Lettuces and Swiss Chard."},{"key":"10.1016\/j.compag.2026.111717_b0045","doi-asserted-by":"crossref","first-page":"6120","DOI":"10.3390\/su16146120","article-title":"Net zero agrivoltaic arrays for agrotunnel vertical growing systems: Energy analysis and system sizing","volume":"16","author":"Asgari","year":"2024","journal-title":"Sustainability"},{"key":"10.1016\/j.compag.2026.111717_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecoinf.2022.101583","article-title":"Eff-UNet++: a novel architecture for plant leaf segmentation and counting","volume":"68","author":"Bhagat","year":"2022","journal-title":"Ecol. Inform."},{"key":"10.1016\/j.compag.2026.111717_b0055","doi-asserted-by":"crossref","first-page":"53","DOI":"10.3390\/jimaging9020053","article-title":"BotanicX-AI: Identification of tomato leaf diseases using an explanation-driven deep-learning model","volume":"9","author":"Bhandari","year":"2023","journal-title":"J. Imaging"},{"key":"10.1016\/j.compag.2026.111717_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109534","article-title":"AgRegNet: A deep regression network for flower and fruit density estimation, localization, and counting in orchards","volume":"227","author":"Bhattarai","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111717_b0065","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2022.838190","article-title":"Improved point-cloud segmentation for plant phenotyping through class-dependent sampling of training data to battle class imbalance","volume":"13","author":"Boogaard","year":"2022","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.compag.2026.111717_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.107757","article-title":"Detection of tomato plant phenotyping traits using YOLOv5-based single stage detectors","volume":"207","author":"Cardellicchio","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111717_b0075","doi-asserted-by":"crossref","first-page":"1826","DOI":"10.3390\/plants10091826","article-title":"Morpho-physiological classification of italian tomato cultivars (Solanum lycopersicum L.) according to drought tolerance during vegetative and reproductive growth","volume":"10","author":"Conti","year":"2021","journal-title":"Plants"},{"key":"10.1016\/j.compag.2026.111717_b0080","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1093\/plphys\/kiab301","article-title":"Resources for image-based high-throughput phenotyping in crops and data sharing challenges","volume":"187","author":"Danilevicz","year":"2021","journal-title":"Plant Physiol."},{"key":"10.1016\/j.compag.2026.111717_b0085","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2023.1206357","article-title":"Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean","volume":"14","author":"Duc","year":"2023","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.compag.2026.111717_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2021.110786","article-title":"Review of energy efficiency in controlled environment agriculture","volume":"141","author":"Engler","year":"2021","journal-title":"Renew. Sustain. Energy Rev."},{"key":"10.1016\/j.compag.2026.111717_b0095","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.solener.2020.01.057","article-title":"Performance of photovoltaic canarian greenhouse: A comparison study between summer and winter seasons","volume":"198","author":"Ezzaeri","year":"2020","journal-title":"Sol. Energy"},{"key":"10.1016\/j.compag.2026.111717_b0100","article-title":"A segmentation-guided deep learning framework for leaf counting","volume":"13","author":"Fan","year":"2022","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.compag.2026.111717_b0105","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2021.575751","article-title":"Leaf counting: Fusing network components for improved accuracy","volume":"12","author":"Farjon","year":"2021","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.compag.2026.111717_b0110","doi-asserted-by":"crossref","first-page":"850","DOI":"10.3390\/rs14040850","article-title":"Fusion classification of HSI and MSI using a spatial-spectral vision transformer for wetland biodiversity estimation","volume":"14","author":"Gao","year":"2022","journal-title":"Remote Sens."},{"key":"10.1016\/j.compag.2026.111717_b0115","doi-asserted-by":"crossref","first-page":"7602","DOI":"10.3390\/su17177602","article-title":"Application of deep learning technology in monitoring plant attribute changes","volume":"17","author":"Han","year":"2025","journal-title":"Sustainability"},{"key":"10.1016\/j.compag.2026.111717_b0120","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.3390\/agriculture12111763","article-title":"Double-arm cooperation and implementing for harvesting kiwifruit","volume":"12","author":"He","year":"2022","journal-title":"Agriculture"},{"key":"10.1016\/j.compag.2026.111717_b0125","doi-asserted-by":"crossref","DOI":"10.3389\/fchem.2022.988227","article-title":"Luminescent quantum dot films improve light use efficiency and crop quality in greenhouse horticulture","volume":"10","author":"Hebert","year":"2022","journal-title":"Front. Chem."},{"key":"10.1016\/j.compag.2026.111717_b0130","doi-asserted-by":"crossref","first-page":"e50","DOI":"10.1016\/S2542-5196(20)30277-1","article-title":"Articulating the effect of food systems innovation on the Sustainable Development Goals","volume":"5","author":"Herrero","year":"2021","journal-title":"Lancet Planet. Health"},{"key":"10.1016\/j.compag.2026.111717_b0135","doi-asserted-by":"crossref","first-page":"4799","DOI":"10.3390\/su17114799","article-title":"Regenerative agrivoltaics: Integrating photovoltaics and regenerative agriculture for sustainable food and energy systems","volume":"17","author":"Jamil","year":"2025","journal-title":"Sustainability"},{"key":"10.1016\/j.compag.2026.111717_b0140","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s13007-024-01138-x","article-title":"LeTra: A leaf tracking workflow based on convolutional neural networks and intersection over union","volume":"20","author":"Jurado-Ruiz","year":"2024","journal-title":"Plant Methods"},{"key":"10.1016\/j.compag.2026.111717_b0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.105210","article-title":"An approach for characterization of infected area in tomato leaf disease based on deep learning and object detection technique","volume":"115","author":"Kaur","year":"2022","journal-title":"Eng. Appl. Artif. Intel."},{"key":"10.1016\/j.compag.2026.111717_b0150","unstructured":"Keras, K., 2025. Keras [WWW Document]. URL https:\/\/keras.io\/api\/ (accessed 11.7.25)."},{"key":"10.1016\/j.compag.2026.111717_b0155","unstructured":"KOMATSUNA - Dataset Ninja [WWW Document], 2026. URL https:\/\/datasetninja.com\/komatsuna (accessed 2.28.26)."},{"key":"10.1016\/j.compag.2026.111717_b0160","doi-asserted-by":"crossref","first-page":"3609","DOI":"10.1109\/TRO.2025.3567544","article-title":"Autonomous tomato harvesting with top\u2013down fusion network for limited data","volume":"41","author":"Li","year":"2025","journal-title":"IEEE Trans. Rob."},{"key":"10.1016\/j.compag.2026.111717_b0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.fcr.2022.108551","article-title":"Quantifying contributions of leaf area and longevity to leaf area duration under increased planting density and nitrogen input regimens during maize yield improvement","volume":"283","author":"Li","year":"2022","journal-title":"Field Crops Res."},{"key":"10.1016\/j.compag.2026.111717_b0170","doi-asserted-by":"crossref","first-page":"0041","DOI":"10.34133\/plantphenomics.0041","article-title":"Self-supervised plant phenotyping by combining domain adaptation with 3D plant model simulations: application to wheat leaf counting at seedling stage","volume":"5","author":"Li","year":"2023","journal-title":"Plant Phenomics"},{"key":"10.1016\/j.compag.2026.111717_b0175","doi-asserted-by":"crossref","first-page":"120","DOI":"10.3390\/agronomy15010120","article-title":"Stem and leaf segmentation and phenotypic parameter extraction of tomato seedlings based on 3D point","volume":"15","author":"Liang","year":"2025","journal-title":"Agronomy"},{"key":"10.1016\/j.compag.2026.111717_b0180","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2023.1331918","article-title":"Editorial: Machine vision and machine learning for plant phenotyping and precision agriculture","volume":"14","author":"Liu","year":"2023","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.compag.2026.111717_b0185","doi-asserted-by":"crossref","DOI":"10.1002\/aesr.202300175","article-title":"The impact of semi\u2010transparent solar panels on tomato and broccoli growth","volume":"5","author":"Lopez-Zaplana","year":"2024","journal-title":"Adv. Energy Sustain. Res."},{"key":"10.1016\/j.compag.2026.111717_b0190","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2023.1274813","article-title":"Maize plant detection using UAV-based RGB imaging and YOLOv5","volume":"14","author":"Lu","year":"2024","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.compag.2026.111717_b0195","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.3390\/agriculture11101003","article-title":"Counting dense leaves under natural environments via an improved deep-learning-based object detection algorithm","volume":"11","author":"Lu","year":"2021","journal-title":"Agriculture"},{"key":"10.1016\/j.compag.2026.111717_b0200","doi-asserted-by":"crossref","first-page":"3569","DOI":"10.3390\/s21103569","article-title":"Evaluating the single-shot multibox detector and YOLO deep learning models for the detection of tomatoes in a greenhouse","volume":"21","author":"Magalh\u00e3es","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.compag.2026.111717_b0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2022.112351","article-title":"A review of research on agrivoltaic systems","volume":"161","author":"Mamun","year":"2022","journal-title":"Renew. Sustain. Energy Rev."},{"key":"10.1016\/j.compag.2026.111717_b0210","doi-asserted-by":"crossref","first-page":"52862","DOI":"10.1109\/ACCESS.2023.3280260","article-title":"MaizeNet: A deep learning approach for effective recognition of maize plant leaf diseases","volume":"11","author":"Masood","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.compag.2026.111717_b0215","unstructured":"Matplotlib 3.10.7 [WWW Document], 2025. URL https:\/\/matplotlib.org\/stable\/install\/index.html (accessed 11.7.25)."},{"key":"10.1016\/j.compag.2026.111717_b0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.123210","article-title":"A Convolutional Neural Network approach for image-based anomaly detection in smart agriculture","volume":"247","author":"Mendoza-Bernal","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.compag.2026.111717_b0225","article-title":"Crop-driven optimization of agrivoltaics using a digital-replica framework","volume":"4","author":"Mengi","year":"2023","journal-title":"Smart Agric. Technol."},{"key":"10.1016\/j.compag.2026.111717_b0230","doi-asserted-by":"crossref","DOI":"10.1002\/ppj2.20022","article-title":"Automation of leaf counting in maize and sorghum using deep learning","volume":"4","author":"Miao","year":"2021","journal-title":"Plant Phenome J."},{"key":"10.1016\/j.compag.2026.111717_b0235","doi-asserted-by":"crossref","first-page":"17489","DOI":"10.1109\/JSTARS.2024.3464411","article-title":"ConvLSTM\u2013ViT: A deep neural network for crop yield prediction using earth observations and remotely sensed data.","volume":"17","author":"Mirhoseini Nejad","year":"2024","journal-title":"IEEE J Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.compag.2026.111717_b0240","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2025.125285","article-title":"Energy performance and crop yield production of a semitransparent photovoltaic greenhouse","volume":"382","author":"Moreno","year":"2025","journal-title":"Appl. Energy"},{"key":"10.1016\/j.compag.2026.111717_b0245","doi-asserted-by":"crossref","DOI":"10.1016\/j.renene.2023.118976","article-title":"Energy and photosynthetic performance investigation of a semitransparent photovoltaic rooftop greenhouse for building integration","volume":"215","author":"Moreno","year":"2023","journal-title":"Renew. Energy"},{"key":"10.1016\/j.compag.2026.111717_b0250","doi-asserted-by":"crossref","first-page":"2984","DOI":"10.3390\/s20102984","article-title":"Intact detection of highly occluded immature tomatoes on plants using deep learning techniques","volume":"20","author":"Mu","year":"2020","journal-title":"Sensors"},{"key":"10.1016\/j.compag.2026.111717_b0255","doi-asserted-by":"crossref","first-page":"5279","DOI":"10.3390\/s24165279","article-title":"Lightweight corn leaf detection and counting using improved YOLOv8","volume":"24","author":"Ning","year":"2024","journal-title":"Sensors"},{"key":"10.1016\/j.compag.2026.111717_b0260","unstructured":"NumPy [WWW Document], 2025. URL https:\/\/numpy.org\/install\/ (accessed 11.7.25)."},{"key":"10.1016\/j.compag.2026.111717_b0265","unstructured":"Open source leaf counting, 2025."},{"key":"10.1016\/j.compag.2026.111717_b0270","doi-asserted-by":"crossref","unstructured":"OpenCV [WWW Document], 2025. URL https:\/\/opencv.org\/platforms\/ (accessed 11.7.25).","DOI":"10.36629\/2686-7788-2025-1-11-16"},{"key":"10.1016\/j.compag.2026.111717_b0275","unstructured":"Pandas 2.3.3 [WWW Document], 2025. URL https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html (accessed 11.7.25)."},{"key":"10.1016\/j.compag.2026.111717_b0280","doi-asserted-by":"crossref","first-page":"8492","DOI":"10.1109\/JSTARS.2023.3312815","article-title":"Deep learning-based plant organ segmentation and phenotyping of sorghum plants using LiDAR point cloud.","volume":"16","author":"Patel","year":"2023","journal-title":"IEEE J Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.compag.2026.111717_b0285","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.3390\/rs13112232","article-title":"Making use of 3D models for plant physiognomic analysis: A review","volume":"13","author":"Paturkar","year":"2021","journal-title":"Remote Sens."},{"key":"10.1016\/j.compag.2026.111717_b0290","article-title":"Algorithmic advancements in agrivoltaics: Modeling shading effects of semi-transparent photovoltaics","volume":"9","author":"Petrakis","year":"2024","journal-title":"Smart Agric. Technol."},{"key":"10.1016\/j.compag.2026.111717_b0295","article-title":"Estimation of low nutrients in tomato crops through the analysis of leaf images using machine learning","volume":"1","author":"Ponce","year":"2021","journal-title":"J. Artif. Intell. Technol."},{"key":"10.1016\/j.compag.2026.111717_b0300","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1109\/TASE.2024.3382731","article-title":"Advances and challenges in computer vision for image-based plant disease detection: a comprehensive survey of machine and deep learning approaches","volume":"22","author":"Qadri","year":"2025","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"10.1016\/j.compag.2026.111717_b0305","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1111\/pce.14330","article-title":"Leaf water potential measurements using the pressure chamber: Synthetic testing of assumptions towards best practices for precision and accuracy","volume":"45","author":"Rodriguez-Dominguez","year":"2022","journal-title":"Plant Cell Environ."},{"key":"10.1016\/j.compag.2026.111717_b0310","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T., 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation, in: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (Eds.), Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015, Lecture Notes in Computer Science. Springer International Publishing, Cham, pp. 234\u2013241. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10.1016\/j.compag.2026.111717_b0315","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109000","article-title":"Evaluating geometric measurement accuracy based on 3D model reconstruction of nursery tomato plants by Agisoft photoscan software","volume":"221","author":"Roshan","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111717_b0320","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109108","article-title":"High-throughput proximal ground crop phenotyping systems \u2013 a comprehensive review","volume":"224","author":"Rui","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111717_b0325","series-title":"MobileNetV2: Inverted Residuals and Linear Bottlenecks","first-page":"4510","author":"Sandler","year":"2018"},{"key":"10.1016\/j.compag.2026.111717_b0330","unstructured":"scikit-learn [WWW Document], 2025. URL https:\/\/scikit-learn.org\/stable\/install.html (accessed 11.7.25)."},{"key":"10.1016\/j.compag.2026.111717_b0335","unstructured":"seaborn 0.13.2 [WWW Document], 2025. URL https:\/\/seaborn.pydata.org\/installing.html (accessed 11.7.25)."},{"key":"10.1016\/j.compag.2026.111717_b0340","doi-asserted-by":"crossref","first-page":"5695","DOI":"10.1007\/s00521-023-09391-2","article-title":"Enhancing crop recommendation systems with explainable artificial intelligence: a study on agricultural decision-making","volume":"36","author":"Shams","year":"2024","journal-title":"Neural Comput. & Applic."},{"key":"10.1016\/j.compag.2026.111717_b0345","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.tplants.2020.07.010","article-title":"Challenges and opportunities in machine-augmented plant stress phenotyping","volume":"26","author":"Singh","year":"2021","journal-title":"Trends Plant Sci."},{"key":"10.1016\/j.compag.2026.111717_b0350","series-title":"High-Throughput Crop Phenotyping, Concepts and Strategies in Plant Sciences","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/978-3-030-73734-4_7","author":"Singh","year":"2021"},{"key":"10.1016\/j.compag.2026.111717_b0355","doi-asserted-by":"crossref","DOI":"10.1117\/1.JEI.32.5.052407","article-title":"Leaf counting in the presence of occlusion in Arabidopsis thaliana plant using convolutional neural networks","volume":"32","author":"\u0160taka","year":"2023","journal-title":"J. Electron. Imaging"},{"key":"10.1016\/j.compag.2026.111717_b0360","doi-asserted-by":"crossref","first-page":"1903","DOI":"10.1038\/s41598-023-28484-5","article-title":"Designing plant\u2013transparent agrivoltaics","volume":"13","author":"Stallknecht","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compag.2026.111717_b0365","author":"Swartz","year":"2021","journal-title":"OPEN Leaf : an Open-Source Cloud-Based Phenotyping System for Tracking Dynamic Changes at Leaf-Specific Resolution in Arabidopsis."},{"key":"10.1016\/j.compag.2026.111717_b0370","doi-asserted-by":"crossref","first-page":"542","DOI":"10.3390\/agriengineering3030035","article-title":"Tomato Leaf diseases classification based on leaf images: A comparison between classical machine learning and deep learning methods","volume":"3","author":"Tan","year":"2021","journal-title":"AgriEngineering"},{"key":"10.1016\/j.compag.2026.111717_b0375","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1186\/s13007-021-00761-2","article-title":"Prediction of plant-level tomato biomass and yield using machine learning with unmanned aerial vehicle imagery","volume":"17","author":"Tatsumi","year":"2021","journal-title":"Plant Methods"},{"key":"10.1016\/j.compag.2026.111717_b0380","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.biosystemseng.2023.06.012","article-title":"Effects of organic photovoltaic modules installed inside greenhouses on microclimate and plants","volume":"232","author":"Teitel","year":"2023","journal-title":"Biosyst. Eng."},{"key":"10.1016\/j.compag.2026.111717_b0385","unstructured":"TensorFlow v2.5.0 [WWW Document], 2025. URL https:\/\/www.tensorflow.org\/versions\/r2.5\/api_docs (accessed 11.7.25)."},{"key":"10.1016\/j.compag.2026.111717_b0390","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1007\/s41348-021-00500-8","article-title":"Artificial intelligence in tomato leaf disease detection: a comprehensive review and discussion","volume":"129","author":"Thangaraj","year":"2022","journal-title":"J. Plant Dis. Prot."},{"key":"10.1016\/j.compag.2026.111717_b0395","article-title":"Optimization of packing factor for maximum electric power and crop yield in greenhouse integrated semi-transparent photo-voltaic thermal (GiSPVT) system in desert land: An experimental study.","volume":"1","author":"Tiwari","year":"2021","journal-title":"E-Prime - Adv. Electr. Eng. Electron Energy"},{"key":"10.1016\/j.compag.2026.111717_b0400","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s13007-018-0273-z","article-title":"The use of plant models in deep learning: An application to leaf counting in rosette plants","volume":"14","author":"Ubbens","year":"2018","journal-title":"Plant Methods"},{"key":"10.1016\/j.compag.2026.111717_b0405","doi-asserted-by":"crossref","first-page":"737","DOI":"10.3390\/agriculture13030737","article-title":"EffiMob-Net: A deep learning-based hybrid model for detection and identification of tomato diseases using leaf images","volume":"13","author":"Ullah","year":"2023","journal-title":"Agriculture"},{"key":"10.1016\/j.compag.2026.111717_b0410","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1007\/s10462-024-11100-x","article-title":"Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture","volume":"58","author":"Upadhyay","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.compag.2026.111717_b0415","doi-asserted-by":"crossref","first-page":"274","DOI":"10.36953\/ECJ.28802904","article-title":"Artificial intelligence and its applications in agriculture: A review","volume":"26","author":"Vardhan","year":"2025","journal-title":"Environ. Conserv. J."},{"key":"10.1016\/j.compag.2026.111717_b0420","doi-asserted-by":"crossref","first-page":"1258","DOI":"10.3390\/atmos13081258","article-title":"A systematic literature review on controlled-environment agriculture: How vertical farms and greenhouses can influence the sustainability and footprint of urban microclimate with local food production","volume":"13","author":"Vatistas","year":"2022","journal-title":"Atmos."},{"key":"10.1016\/j.compag.2026.111717_b0425","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.3390\/agronomy11061152","article-title":"Semi-Transparent Organic Photovoltaics Applied as Greenhouse Shade for Spring and Summer Tomato Production in Arid climate","volume":"11","author":"Waller","year":"2021","journal-title":"Agronomy"},{"key":"10.1016\/j.compag.2026.111717_b0430","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.3390\/agronomy12081865","article-title":"3DPhenoMVS: a Low-cost 3D tomato phenotyping pipeline using 3D reconstruction point cloud based on multiview images","volume":"12","author":"Wang","year":"2022","journal-title":"Agronomy"},{"key":"10.1016\/j.compag.2026.111717_b0435","first-page":"324","volume":"F1000Research 10","author":"Williamson","year":"2023","journal-title":"Data Management Challenges for Artificial Intelligence in Plant and Agricultural Research"},{"key":"10.1016\/j.compag.2026.111717_b0440","article-title":"A miniaturized phenotyping platform for individual plants using multi-view stereo 3D reconstruction","volume":"13","author":"Wu","year":"2022","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.compag.2026.111717_b0445","doi-asserted-by":"crossref","first-page":"0040","DOI":"10.34133\/plantphenomics.0040","article-title":"Generating 3D multispectral point clouds of plants with fusion of snapshot spectral and RGB-D Images","volume":"5","author":"Xie","year":"2023","journal-title":"Plant Phenomics"},{"key":"10.1016\/j.compag.2026.111717_b0450","doi-asserted-by":"crossref","first-page":"1890","DOI":"10.3390\/s23041890","article-title":"Leaf-counting in monocot plants using deep regression models","volume":"23","author":"Xie","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.compag.2026.111717_b0455","doi-asserted-by":"crossref","first-page":"106","DOI":"10.3390\/technologies12070106","article-title":"Analysis and development of an IoT system for an agrivoltaics plant","volume":"12","author":"Zito","year":"2024","journal-title":"Technologies"}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926003121?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926003121?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T04:48:06Z","timestamp":1776919686000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0168169926003121"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":91,"alternative-id":["S0168169926003121"],"URL":"https:\/\/doi.org\/10.1016\/j.compag.2026.111717","relation":{},"ISSN":["0168-1699"],"issn-type":[{"value":"0168-1699","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Two-stage deep learning model for non-destructive leaf counting in tomato crops","name":"articletitle","label":"Article Title"},{"value":"Computers and Electronics in Agriculture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compag.2026.111717","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"111717"}}