Review Article
Ait Ameur, M.A., El-Sayed, A.M., Yan, X.T., Mehnen, J., Maier, A.M. 2025. A novel opto-tactile sensing approach to enhance the handling of soft fruit. Computers and Electronics in Agriculture 235: 110397. https://doi.org/10.1016/j.compag.2025.110397
10.1016/j.compag.2025.110397Aline, U., Semyalo, D., Pahlawan, M.F., Akter, T., Faqeerzada, M.A., Kim, S.-Y., Oh, D., Cho, B.-K. 2025. Integration of hyperspectral imaging and chemometrics for internal quality evaluation of packaged and non-packaged fresh fruits. Agriculture 15(16). https://doi.org/10.3390/agriculture15161718
10.3390/agriculture15161718Al-Najadi, R., Al-Mulla, Y., Al-Abri, I., Al-Sadi, A.M. 2025. Effectiveness of drone-based thermal sensors in optimizing controlled environment agriculture performance under arid conditions. Scientific Reports 15(1): 9042. https://doi.org/10.1038/s41598-025-94432-0
10.1038/s41598-025-94432-040091125PMC11911431Al-Sammarraie, M.A., Gierz, Ł., Jihad, G.H., Gokalp, Z., Özbek, O., Markowski, P. 2025. Classification of apple slices treated by atmospheric plasma jet for post-harvest processes using image processing and convolutional neural networks. Food and Bioprocess Technology 18: 8453-8467. https://doi.org/10.1007/s11947-025-03904-8
10.1007/s11947-025-03904-8Ariza-Sentís, M., Vélez, S., Martínez-Peña, R., Baja, H., Valente, J. 2024. Object detection and tracking in Precision Farming: a systematic review. Computers and Electronics in Agriculture 219: 108757. https://doi.org/10.1016/j.compag.2024.108757
10.1016/j.compag.2024.108757Kalaiselvi, T., Kukreja, V., Azeez, T.B., Somasundaram, K., Praveenkumar, S., Sriramakrishnan, P. 2025. An automatic mango quality grading system in smart agriculture using novel adaptive feature vector and ensemble learning. Multimedia Tools and Applications 84: 38045-38070. https://doi.org/10.1007/s11042-025-20688-3
10.1007/s11042-025-20688-3Bartold, M., Wróblewski, K., Kluczek, M., Dąbrowska-Zielińska, K., Goliński, P. 2024. Examining the sensitivity of satellite-derived vegetation indices to plant drought stress in grasslands in Poland. Plants 13(16).
10.3390/plants1316231939204755PMC11360788Basak, J.K., Paudel, B., Kang, M.Y., Karki, S., Sarkar, T.K., Tamrakar, N., Moon, B.E., Kim, H.T. 2025. Prediction of physicochemical properties of strawberry fruits using convolutional neural network-regression models. Horticulture, Environment, and Biotechnology, 1-15.
10.1007/s13580-025-00717-8Bu, Y., Liu, H., Li, H., Murengami, B.G., Wang, X., Chen, X. 2025. Grading Algorithm for Orah Sorting Line Based on Improved ShuffleNet V2. Applied Sciences 15(8): 4483. https://doi.org/10.3390/app15084483
10.3390/app15084483Cai, L., Zhang, Y., Cai, Z., Shi, R., Li, S., Li, J. 2024a. Detection of soluble solids content in tomatoes using full transmission Vis-NIR spectroscopy and combinatorial algorithms. Frontiers in Plant Science 15: 1500819. https://doi.org/10.3389/fpls.2024.1500819
10.3389/fpls.2024.150081939588094PMC11586169Cai, Y., Cui, B., Deng, H., Zeng, Z., Wang, Q., Lu, D., Cui, Y., Tian, Y. 2024b. Cherry tomato detection for harvesting using multimodal perception and an improved YOLOv7-tiny neural network. Agronomy 14(10): 2320. https://doi.org/10.3390/agronomy14102320
10.3390/agronomy14102320Cai, Z., Zhang, Y., Li, J., Zhang, J., Li, X. 2025. Synchronous detection of internal and external defects of citrus by structured-illumination reflectance imaging coupling with improved YOLO v7. Postharvest Biology and Technology 227: 113576. https://doi.org/10.1016/j.postharvbio.2025.113576
10.1016/j.postharvbio.2025.113576Chen, B.-J., Bu, J.-Y., Xia, J.-L., Li, M.-X., Su, W.-H. 2025a. AFBF-YOLO: An improved YOLO11n algorithm for detecting bunch and maturity of cherry tomatoes in greenhouse environments. Plants 14(16): 2587. https://doi.org/10.3390/plants14162587
10.3390/plants1416258740872209PMC12389433Chen, C., Li, J., Liu, B., Huang, B., Yang, J., Xue, L. 2025b. A robust vision system for measuring and positioning green asparagus based on YOLO-seg and 3D point cloud data. Computers and Electronics in Agriculture 230: 109937. https://doi.org/10.1016/j.compag.2025.109937
10.1016/j.compag.2025.109937Chen, D., Lin, F., Lu, C., Zhuang, J., Su, H., Zhang, D., He, J. 2025c. YOLOv8-MDN-Tiny: A lightweight model for multi-scale disease detection of postharvest golden passion fruit. Postharvest Biology and Technology 219: 113281. https://doi.org/10.1016/j.postharvbio.2024.113281
10.1016/j.postharvbio.2024.113281Chen, J., Yu, R., Yang, M., Che, W., Ning, Y., Zhan, Y. 2025d. SN-YOLO: A rotation detection method for tomato harvest in greenhouses. Electronics 14(16): 3243. https://doi.org/10.3390/electronics14163243
10.3390/electronics14163243Chen, W., Rao, Y., Wang, F., Zhang, Y., Wang, T., Jin, X., Hou, W., Jiang, Z., Zhang, W. 2024a. MLP-based multimodal tomato detection in complex scenarios: Insights from task-specific analysis of feature fusion architectures. Computers and Electronics in Agriculture 221: 108951. https://doi.org/10.1016/j.compag.2024.108951
10.1016/j.compag.2024.108951Chen, Y., Xu, H., Chang, P., Huang, Y., Zhong, F., Jia, Q., Chen, L., Zhong, H., Liu, S. 2024b. CES-YOLOv8: Strawberry maturity detection based on the improved YOLOv8. Agronomy 14(7): 1353. https://doi.org/10.3390/agronomy14071353
10.3390/agronomy14071353Conejero, M.N., Montes, H., Bengochea-Guevara, J.M., Garrido-Rey, L., Andújar, D., Ribeiro, A. 2025. A collaborative robotic fleet for yield mapping and manual fruit harvesting assistance. Computers and Electronics in Agriculture 235: 110351. https://doi.org/10.1016/j.compag.2025.110351
10.1016/j.compag.2025.110351Dai, J., Wang, G., Yang, M., Liu, D. 2025a. PEBU-Net: A lightweight segmentation network for blueberry bruising based on Unet3+ using hyperspectral transmission imaging. Measurement 117700. https://doi.org/10.1016/j.measurement.2025.117700
10.1016/j.measurement.2025.117700Dai, N., Fang, J., Yuan, J., Liu, X. 2024. 3MSP2: Sequential picking planning for multi-fruit congregated tomato harvesting in multi-clusters environment based on multi-views. Computers and Electronics in Agriculture 225: 109303. https://doi.org/10.1016/j.compag.2024.109303
10.1016/j.compag.2024.109303Dai, S., Bai, T., Zhao, Y. 2025b. Keypoint Detection and 3D Localization Method for Ridge-Cultivated Strawberry Harvesting Robots. Agriculture 15(4): 372. https://doi.org/10.3390/agriculture15040372
10.3390/agriculture15040372Deng, X., Huang, T., Wang, W., Feng, W. 2025. SE-YOLO: A sobel-enhanced framework for high-accuracy, lightweight real-time tomato detection with edge deployment capability. Computers and Electronics in Agriculture 239: 110973. https://doi.org/10.1016/j.compag.2025.110973
10.1016/j.compag.2025.110973Dong, J., Lin, Y., Li, C., Zhou, S., Zheng, N. 2025a. AugDETR: Improving multi-scale learning for detection transformer. Computer Vision – ECCV 2024, Cham 15082: 238-255. https://doi.org/10.1007/978-3-031-72691-0_14
10.1007/978-3-031-72691-0_14Dong, L., Zhu, L., Zhao, B., Wang, R., Ni, J., Liu, S., Chen, K., Cui, X., Zhou, L. 2025b. Semantic segmentation-based observation pose estimation method for tomato harvesting robots. Computers and Electronics in Agriculture 230: 109895. https://doi.org/10.1016/j.compag.2025.109895
10.1016/j.compag.2025.109895Fang, T., Chen, W., Han, L. 2025. Location research and picking experiment of an apple-picking robot based on improved mask r-cnn and binocular vision. Horticulturae 11(7): 801. https://doi.org/10.3390/horticulturae11070801
10.3390/horticulturae11070801Fass, E., Shlomi, E., Ziv, C., Glickman, O., Helman, D. 2025. Machine learning models based on hyperspectral imaging for pre-harvest tomato fruit quality monitoring. Computers and Electronics in Agriculture 229: 109788. https://doi.org/10.1016/j.compag.2024.109788
10.1016/j.compag.2024.109788Freedman, D., Smolin, Y., Krupka, E., Leichter, I., Schmidt, M. 2014. SRA: Fast removal of general multipath for ToF sensors. Computer Vision – ECCV 2014, Cham. 8689: 234-249. https://doi.org/10.1007/978-3-319-10590-1_16
10.1007/978-3-319-10590-1_16Fu, C., Liu, S., Tian, A. 2025. Detection of blueberry based on hyperspectral imaging and deep learning. Food Research International, 221: 117141. https://doi.org/10.1016/j.foodres.2025.117141
10.1016/j.foodres.2025.117141Gan, Y., Ren, X., Liu, H., Chen, Y., Lin, P. 2025. Real-time lightweight strawberry ripeness detection framework based on YOLO11 deployed on edge computing platform. Journal of Food Measurement and Characterization 1-23. https://doi.org/10.1007/s11694-025-03559-1
10.1007/s11694-025-03559-1Gao, J., Yang, X., Liu, S., Liu, Y., Ning, X. 2025. Detection of tomato leaf pesticide residues based on fluorescence spectrum and hyper-spectrum. Horticulturae 11(2): 121. https://doi.org/10.3390/horticulturae11020121
10.3390/horticulturae11020121Gimpelj, J., Munih, M. 2025. Assessing geometry perception of direct time-of-flight sensors for robotic safety. Sensors 25(14): 4385. https://doi.org/10.3390/s25144385
10.3390/s2514438540732513PMC12298625Girshick, R. 2015. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision (1440-1448).
10.1109/ICCV.2015.169Girshick, R., Donahue, J., Darrell, T., Malik, J. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (580-587).
10.1109/CVPR.2014.81Gogoi, P., Valan, J. A. 2025. Enhancing date fruit classification using machine learning, CTGAN, and SHAP-based explainability: P. Gogoi and JA Valan. Journal of Food Measurement and Characterization, 19: 6851-6872. https://doi.org/10.1007/s11694-025-03428-x
10.1007/s11694-025-03428-xGoh, J. Y., Mohamed Ali, M. S., Md Yunos, Y., Sheikh, U. U., & Khan, M. S. (2025). Outdoor RGB and point cloud depth dataset for palm oil fresh fruit bunch ripeness classification and localization. Scientific Data 12(1): 687. https://doi.org/10.1038/s41597-025-04953-6
10.1038/s41597-025-04953-640274829PMC12022099Guan, X., Shi, L., Ge, H., Ding, Y., Nie, S. 2025. Development, design, and improvement of an intelligent harvesting system for aquatic vegetable brasenia schreberi. Agronomy 15(6): 1451.
10.3390/agronomy15061451Hao, F., Zhang, Z., Ma, D., Kong, H. 2025. GSBF-YOLO: a lightweight model for tomato ripeness detection in natural environments. Journal of Real-Time Image Processing 22(1): 47.
10.1007/s11554-025-01624-yHe, W., Huang, W., Popovic, T., Zhu, Z., Zhang, X. 2025a. Improving mango cold-damage and bruise detection using thermal imaging and flexible spectral sensing. Food Control 172: 111163.
10.1016/j.foodcont.2025.111163He, Y., Li, Y., Li, Z., Song, R., Xu, C. 2025b. An improved YOLOv8-based lightweight approach for orange maturity detection. Journal of Food Measurement and Characterization, 1-15.
10.1007/s11694-025-03286-7He, Z., Karkee, M., Zhang, Q. 2025c. Enhanced machine vision system for field-based detection of pickable strawberries: Integrating an advanced two-step deep learning model merging improved YOLOv8 and YOLOv5-cls. Computers and Electronics in Agriculture 234: 110173.
10.1016/j.compag.2025.110173He, Z., Liu, Z., Zhou, Z., Karkee, M., Zhang, Q. 2025d. Improving picking efficiency under occlusion: Design, development, and field evaluation of an innovative robotic strawberry harvester. Computers and Electronics in Agriculture 237: 110684. https://doi.org/10.1016/j.compag.2025.110684
10.1016/j.compag.2025.110684Hobart, M., Pflanz, M., Tsoulias, N., Weltzien, C., Kopetzky, M., Schirrmann, M. 2025. Fruit Detection and Yield Mass Estimation from a UAV Based RGB Dense Cloud for an Apple Orchard. Drones 9(1): 60. https://doi.org/10.3390/drones9010060
10.3390/drones9010060Hou, G., Chen, H., Niu, R., Li, T., Ma, Y., Zhang, Y. 2025. Research on multi-layer model attitude recognition and picking strategy of small tomato picking robot. Computers and Electronics in Agriculture 232: 110125. https://doi.org/10.1016/j.compag.2025.110125
10.1016/j.compag.2025.110125Hou, J., Che, Y., Fang, Y., Bai, H., Sun, L. 2024. Early bruise detection in apple based on an improved faster RCNN model. Horticulturae 10(1): 100. https://doi.org/10.3390/horticulturae10010100
10.3390/horticulturae10010100Hu, Z., Pu, Y., Wu, W., Pan, L., Yang, Y., Zhao, J. 2025. Online detection of moldy apple core based on diameter and SSC features. Food Control 168: 110879. https://doi.org/10.1016/j.foodcont.2024.110879
10.1016/j.foodcont.2024.110879Huang, Y., Li, Z., Bian, Z., Jin, H., Zheng, G., Hu, D., Sun, Y., Fan, C., Xie, W., Fang, H. 2025a. Overview of Deep Learning and Nondestructive Detection Technology for Quality Assessment of Tomatoes. Foods 14(2): 286. https://doi.org/10.3390/foods14020286
10.3390/foods1402028639856952PMC11764496Huang, Y., Pang, J., Yu, S., Su, J., Hou, S., Han, T. 2025b. Reconstruction, segmentation and phenotypic feature extraction of oilseed rape point cloud combining 3D gaussian splatting and CKG-PointNet++. Agriculture 15(12): 1289. https://doi.org/10.3390/agriculture15121289
10.3390/agriculture15121289Huang, Z., Lee, W.S., Yang, P., Ampatzidis, Y., Shinsuke, A., Peres, N.A. 2025c. Advanced canopy size estimation in strawberry production: a machine learning approach using YOLOv11 and SAM. Computers and Electronics in Agriculture 236: 110501. https://doi.org/10.1016/j.compag.2025.110501
10.1016/j.compag.2025.110501Hwang, D.-Y., Kim, S.-Y., Woo, S.-G., Lee, I.-H. 2011. Changes and characteristics of agricultural labor time in protected horticulture. Rural Development Administration. https://books.google.co.kr/books?id=PKiCswEACAAJ
Hwang, H.-J., Cho, J.-H., Kim, Y.-T. 2024. Deep learning-based real-time 6D pose estimation and multi-mode tracking algorithms for citrus-harvesting robots. Machines 12(9): 642. https://doi.org/10.3390/machines12090642
10.3390/machines12090642Igathinathane, C., Visvanathan, R., Bora, G., Rahman, S. 2025. Computer Vision-Based Multiple Width Measurements for Agricultural Produce. AgriEngineering 7(7): 204. https://doi.org/10.3390/agriengineering7070204
10.3390/agriengineering7070204Islam, M.S., Islam, M. 2025. Improving mango ripeness grading accuracy: A comprehensive analysis of deep learning, traditional machine learning, and transfer learning techniques. Machine Learning with Applications 19: 100619. https://doi.org/10.1016/j.mlwa.2025.100619
10.1016/j.mlwa.2025.100619Jain, E., Kukreja, V., Kaushik, P., Ahuja, V., Bansal, A. 2025. Hybrid ViT-ResNet: A high-accuracy AI model for automated strawberry ripeness classification in precision agriculture. Applied Fruit Science 67(5): 368. https://doi.org/10.1007/s10341-025-01576-4
10.1007/s10341-025-01576-4Jang, M., Hwang, Y. 2024. Tomato pose estimation using the association of tomato body and sepal. Computers and Electronics in Agriculture 221: 108961. 10.1016/j.compag.2024.108961doi.org/10.1016/j.compag.2024.108961doi.org/10.1016/j.compag.2024.108961
10.1016/j.compag.2024.108961Jaywant, S.A., Arif, K.M. 2024. Remote Sensing Techniques for Water Quality Monitoring: A Review. Sensors 24(24): 8041. https://doi.org/10.3390/s24248041
10.3390/s2424804139771777PMC11679694Jia, R., Xue, J., Lu, W., Song, Z., Xu, Z., Lu, S. 2024. A coupled calibration method for dual cameras-projector system with sub-pixel accuracy feature extraction. Sensors 24(6). https://doi.org/10.3390/s24061987
10.3390/s2406198738544249PMC10975780Jiang, L., Wang, Y., Yan, H., Yin, Y., Wu, C. 2025. Strawberry Fruit Deformity Detection and Symmetry Quantification Using Deep Learning and Geometric Feature Analysis. Horticulturae 11(6): 652. https://doi.org/10.3390/horticulturae11060652
10.3390/horticulturae11060652Jin, S., Zhou, L., Zhou, H. 2025. CO-YOLO: A lightweight and efficient model for Camellia oleifera fruit object detection and posture determination. Computers and Electronics in Agriculture 235: 110394. https://doi.org/10.1016/j.compag.2025.110394
10.1016/j.compag.2025.110394Kamat, P., Gite, S., Chandekar, H., Dlima, L., Pradhan, B. 2025. Multi-class fruit ripeness detection using YOLO and SSD object detection models. Discover Applied Sciences 7(9): 931. https://doi.org/10.1007/s42452-025-07617-7
10.1007/s42452-025-07617-7Kang, S., Fan, J., Ye, Y., Li, C., Du, D., Wang, J. 2025. Maturity recognition and localisation of broccoli under occlusion based on RGB-D instance segmentation network. Biosystems Engineering 250: 270-284. https://doi.org/10.1016/j.biosystemseng.2025.01.007
10.1016/j.biosystemseng.2025.01.007Kang, Y. S., Ryu, C. S., Kang, J. G. 2024. Presenting a Multispectral Image Sensor for Quantification of Total Polyphenols in Low-Temperature Stressed Tomato Seedlings Using Hyperspectral Imaging. Sensors 24(13). https://doi.org/10.3390/s24134260
10.3390/s2413426039001041PMC11244052Kanwal, N., Kämper, W., Farrar, M. B., Tootoonchy, M., Lynch, C., Nichols, J., Wallace, H. M., Trueman, S. J., Bai, S. H. 2025. Rapid assessment of lychee and mango fruit quality using hyperspectral imaging. LWT, 117833. https://doi.org/10.1016/j.lwt.2025.117833
10.1016/j.lwt.2025.117833Karim, M.R., Ahmed, S., Reza, M.N., Lee, K.-H., Sung, J., Chung, S.-O. 2025. Geometric Feature Characterization of Apple Trees from 3D LiDAR Point Cloud Data. Journal of Imaging 11(1). https://doi.org/10.3390/jimaging11010005
10.3390/jimaging1101000539852318PMC11766997Kasampalis, D.S., Tsouvaltzis, P., Siomos, A.S. 2025. Assessment of Melon Fruit Nutritional Composition Using VIS/NIR/SWIR Spectroscopy Coupled with Chemometrics. Horticulturae 11(6). https://doi.org/10.3390/horticulturae11060658
10.3390/horticulturae11060658Kazmi, W., Foix, S., Alenyà, G., Andersen, H. J. 2014. Indoor and outdoor depth imaging of leaves with time-of-flight and stereo vision sensors: Analysis and comparison. ISPRS Journal of Photogrammetry and Remote Sensing 88: 128-146. https://doi.org/10.1016/j.isprsjprs.2013.11.012
10.1016/j.isprsjprs.2013.11.012Khan, Z., Shen, Y., Liu, H. 2025. ObjectDetection in Agriculture: A Comprehensive Review of Methods, Applications, Challenges, and Future Directions. Agriculture 15(13): 1351. https://doi.org/10.3390/agriculture15131351
10.3390/agriculture15131351Kim, M.-J., Yu, W.-H., Song, D.-J., Chun, S.-W., Kim, M. S., Lee, A., Kim, G., Shin, B.-S., Mo, C. 2024. Prediction of Soluble-Solid Content in Citrus Fruit Using Visible–Near-Infrared Hyperspectral Imaging Based on Effective-Wavelength Selection Algorithm. Sensors 24(5): 1512. https://doi.org/10.3390/s24051512
10.3390/s2405151238475048PMC10935418Krklješ, D., Kitić, G., Panić, M., Petes, C., Filipović, V., Stefanović, D., Obrenović, N., Lalić, M., Marko, O. 2025. Agrobot Gari, a multimodal robotic solution for blueberry production automation. Computers and Electronics in Agriculture 237: 110626. https://doi.org/10.1016/j.compag.2025.110626
10.1016/j.compag.2025.110626Krüger, M., Zemanek, T., Wuttke, D., Dinkel, M., Serfling, A., Böckmann, E. 2024. Hyperspectral imaging for pest symptom detection in bell pepper. Plant Methods 20(1): 156. https://doi.org/10.1186/s13007-024-01273-5
10.1186/s13007-024-01273-539358772PMC11447932Lang, Y., Zhang, Y., Sun, T., Chai, X., Zhang, N. 2025. Digital twin-driven system for efficient tomato harvesting in greenhouses. Computers and Electronics in Agriculture 236: 110451. https://doi.org/10.1016/j.compag.2025.110451
10.1016/j.compag.2025.110451Li, J., Zhang, J., Mei, M., Diao, Z., Li, X., Shi, R., Cai, Z. 2025a. Detection of bruising in pear with varying bruising degrees and formation times by using SIRI technique combining with texture feature-based LS-SVM and ResNet-18-based CNN model. Postharvest Biology and Technology 223: 113434. https://doi.org/10.1016/j.postharvbio.2025.113434
10.1016/j.postharvbio.2025.113434Li, P., Wen, M., Zeng, Z., Tian, Y. 2025b. Cherry tomato bunch and picking point detection for robotic harvesting using an RGB-D sensor and a StarBL-YOLO network. Horticulturae 11(8): 949. https://doi.org/10.3390/horticulturae11080949
10.3390/horticulturae11080949Li, W., Tang, B., Hou, Z., Wang, H., Bing, Z., Yang, Q., Zheng, Y. 2024a. Dynamic slicing and reconstruction algorithm for precise canopy volume estimation in 3D citrus tree point clouds. Remote Sensing 16(12): 2142. https://doi.org/10.3390/rs16122142
10.3390/rs16122142Li, W., Zhou, B., Zhou, Y., Jiang, C., Ruan, M., Ke, T., Wang, H., Lv, C. 2025c. Grape Disease Detection Using Transformer-Based Integration of Vision and Environmental Sensing. Agronomy 15(4): 831. https://doi.org/10.3390/agronomy15040831
10.3390/agronomy15040831Li, X., Zhu, B., Li, S., Liu, L., Song, K., Liu, J. 2025d. A Comprehensive Review of Crop Chlorophyll Mapping Using Remote Sensing Approaches: Achievements, Limitations, and Future Perspectives. Sensors 25(8): 2345. https://doi.org/10.3390/s25082345
10.3390/s2508234540285035PMC12031281Li, Z., Xu, R., Li, C., Munoz, P., Takeda, F., Leme, B. 2025d. In-field blueberry fruit phenotyping with a MARS-PhenoBot and customized BerryNet. Computers and Electronics in Agriculture 232: 110057. https://doi.org/10.1016/j.compag.2025.110057
10.1016/j.compag.2025.110057Liang, X., Wei, Z., Chen, K. 2025. A method for segmentation and localization of tomato lateral pruning points in complex environments based on improved YOLOV5. Computers and Electronics in Agriculture 229: 109731. https://doi.org/10.1016/j.compag.2024.109731
10.1016/j.compag.2024.109731Liu, H., Gu, W., Wang, W., Zou, Y., Yang, H., Li, T. 2025a. Persimmon fruit detection in complex scenes based on PerD-YOLOv8: Liu et al. Journal of Food Measurement and Characterization, 1-18. https://doi.org/10.1007/s11694-025-03268-9
10.1007/s11694-025-03268-9Liu, P., An, C., Sun, Z., Zhou, Y., Luo, X., Xu, H., Xie, L. 2025b. A knowledge-guided size correction strategy for improving SSC detection accuracy and robustness in apples across size variability. Food Control, 111457. https://doi.org/10.1016/j.foodcont.2025.111457
10.1016/j.foodcont.2025.111457Liu, Q., Lv, J., Zhang, C. 2024a. MAE-YOLOv8-based small object detection of green crisp plum in real complex orchard environments. Computers and Electronics in Agriculture 226: 109458. https://doi.org/10.1016/j.compag.2024.109458. https://doi.org/10.1016/j.compag.2024.109458
10.1016/j.compag.2024.109458Liu, S., Ampatzidis, Y., Zhou, C., Lee, W. S. 2025c. AI-driven time series analysis for predicting strawberry weekly yields integrating fruit monitoring and weather data for optimized harvest planning. Computers and Electronics in Agriculture 233: 110212. https://doi.org/10.1016/j.compag.2025.110212
10.1016/j.compag.2025.110212Liu, T., Wang, X., Hu, K., Zhou, H., Kang, H., Chen, C. 2024b. FF3D: A Rapid and Accurate 3D Fruit Detector for Robotic Harvesting. Sensors 24(12): 3858. https://doi.org/10.3390/s24123858
10.3390/s2412385838931642PMC11207580Liu, W., Tan, Z., Xie, W. 2025d. A hand-like gripper embedded with flexible gel sensor for tomato harvesting: soft contact and intelligent ripeness sensing. Journal of Food Measurement and Characterization 19(5): 3150-3161. https://doi.org/10.1007/s11694-025-03168-y
10.1007/s11694-025-03168-yLiu, Y., Han, X., Zhang, H., Liu, S., Ma, W., Yan, Y., Sun, L., Jing, L., Wang, Y., Wang, J. 2025e. YOLOv8-MSP-PD: A Lightweight YOLOv8-Based Detection Method for Jinxiu Malus Fruit in Field Conditions. Agronomy 15(7): 1581. https://doi.org/10.3390/agronomy15071581
10.3390/agronomy15071581Loarca, J., Wiesner-Hanks, T., Lopez-Moreno, H., Maule, A. F., Liou, M., Torres-Meraz, M. A., Diaz-Garcia, L., Johnson-Cicalese, J., Neyhart, J., Polashock, J. 2024. BerryPortraits: phenotyping of ripening traits in cranberry (Vaccinium macrocarpon Ait.) With YOLOv8. Plant Methods 20(1): 172. https://doi.org/10.1186/s13007-024-01285-1
10.1186/s13007-024-01285-139538304PMC11562335Lyu, Y., Kuswidiyanto, L.W., Wang, P., Noh, H.-H., Jung, H.-Y., Han, X. 2025. One-dimensional convolutional neural network for automated kimchi cabbage downy mildew detection using aerial hyperspectral images. Remote Sensing 17(9): 1626. https://doi.org/10.3390/rs17091626
10.3390/rs17091626Mandrapa, B., Spohrer, K., Wuttke, D., Ruttensperger, U., Dieckhoff, C., Müller, J. 2024. Machine learning-based hyperspectral wavelength selection and classification of spider mite-infested cucumber leaves. Experimental and Applied Acarology 93(3): 627-644. https://doi.org/10.1007/s10493-024-00953-0
10.1007/s10493-024-00953-039177713PMC11464534Mei, Z., Li, Y., Zhu, R., Wang, S. 2025. Intelligent fruit localization and grasping method based on YOLO VX model and 3D vision. Agriculture 15(14): 1508. https://doi.org/10.3390/agriculture15141508
10.3390/agriculture15141508Miyashita, L., Tabata, S., Ishikawa, M. 2025. High-Speed 3D Vision Based on Structured Light Methods. Metrology 5(2): 24. https://doi.org/10.3390/metrology5020024
10.3390/metrology5020024Mojaravscki, D., Graziano Magalhães, P.S. 2024. Comparative evaluation of color correction as image preprocessing for olive identification under natural light using cell phones. AgriEngineering 6(1): 155-170. https://doi.org/10.3390/agriengineering6010010
10.3390/agriengineering6010010Mu, S., Dai, N., Yuan, J., Liu, X., Xin, Z., Meng, X. 2024. S2CPL: A novel method of the harvest evaluation and subsoil 3D cutting-Point location for selective harvesting of green asparagus. Computers and Electronics in Agriculture 225: 109316. https://doi.org/10.1016/j.compag.2024.109316
10.1016/j.compag.2024.109316Mullins, C.C., Esau, T.J., Zaman, Q.U., Toombs, C.L., Hennessy, P.J. 2024. Leveraging zero-shot detection mechanisms to accelerate image annotation for machine learning in wild blueberry (Vaccinium angustifolium Ait.). Agronomy 14(12): 2830. https://doi.org/10.3390/agronomy14122830
10.3390/agronomy14122830Mydhili, S., Senthilkumar, K., Buvaneswari, B., Lakshmi, T.V. 2025. Detecting defective apples through advanced computer vision and Legendre Multi Wavelet Neural Networks. Journal of Stored Products Research 114: 102702. https://doi.org/10.1016/j.jspr.2025.102702
10.1016/j.jspr.2025.102702Naqvi, L.H., Balasubramaniam, B., Li, J., Liu, L., Li, B. 2025. Four-dimensional hyperspectral imaging for fruit and vegetable grading. Agriculture 15(15): 1702. https://doi.org/10.3390/agriculture15151702
10.3390/agriculture15151702Navarro, E., Costa, N., Pereira, A. 2020. A systematic review of iot solutions for smart farming. Sensors 20(15): 4231. https://doi.org/10.3390/s20154231
10.3390/s2015423132751366PMC7436012Ni, J., Zhu, L., Dong, L., Wang, R., Chen, K., Gao, J., Wang, W., Zhou, L., Zhao, B., Rong, J., Han, Z., Lu, K., Feng, X. 2024. TomatoPoseNet: An Efficient Keypoint-Based 6D Pose Estimation Model for Non-Destructive Tomato Harvesting. Agronomy 14(12): 3027. https://doi.org/10.3390/agronomy14123027
10.3390/agronomy14123027Niu, Z., Huang, T., Xu, C., Sun, X., Taha, M. F., He, Y., Qiu, Z. 2025. A Novel Approach to Optimize Key Limitations of Azure Kinect DK for Efficient and Precise Leaf Area Measurement. Agriculture 15(2): 173. https://doi.org/10.3390/agriculture15020173
10.3390/agriculture15020173Noorali, N., Rajabipour, A., Sardari, H., Hosseinpour, S. 2025. Detection of external defects of tomato crop using appearance parameters by convolutional neural networks. Future Foods 11: 100611. https://doi.org/10.1016/j.fufo.2025.100611
10.1016/j.fufo.2025.100611Paciolla, F., Popeo, G., Farella, A., Pascuzzi, S. 2025. Agronomic Information Extraction from UAV-Based Thermal Photogrammetry Using MATLAB. Remote Sensing 17(15): 2746. https://doi.org/10.3390/rs17152746
10.3390/rs17152746Park, M., Lim, J., Joo, J., Lee, S. 2024. Food Market Response Tasks According to Population Structure Changes (Years 1 and 2). In Food Market Response Tasks According to Population Structure Changes: Korea Rural Economic Institute.
Polder, G., Dieleman, J. A., Hageraats, S., Meinen, E. 2024. Imaging spectroscopy for monitoring the crop status of tomato plants. Computers and Electronics in Agriculture 216: 108504. https://doi.org/10.1016/j.compag.2023.108504
10.1016/j.compag.2023.108504Qiao, Y., Wang, C., Zhu, W., Sun, L., Bai, J., Zhou, R., Zhu, Z., Cai, J. 2025. Online assessment of soluble solids content in strawberries using a developed Vis/NIR spectroscopy system with a hanging grasper. Food Chemistry 478: 143671. https://doi.org/10.1016/j.foodchem.2025.143671
10.1016/j.foodchem.2025.143671Qin, J., Chen, Z., Zhang, Y., Nie, J., Yan, T., Wan, B. 2025. YOLO-CT: A method based on improved YOLOv8n-Pose for detecting multi-species mature cherry tomatoes and locating picking points in complex environments. Measurement, 117954. https://doi.org/10.1016/j.measurement.2025.117954
10.1016/j.measurement.2025.117954Ram, B. G., Oduor, P., Igathinathane, C., Howatt, K., Sun, X. 2024. A systematic review of hyperspectral imaging in precision agriculture: Analysis of its current state and future prospects. Computers and Electronics in Agriculture 222: 109037. https://doi.org/10.1016/j.compag.2024.109037
10.1016/j.compag.2024.109037Redmon, J., Divvala, S., Girshick, R., Farhadi, A. 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
10.1109/CVPR.2016.91Ren, S., He, K., Girshick, R., Sun, J. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28.
Rezaei, P., Hemmat, A., Shahpari, N., Mireei, S.A. 2024. Machine vision-based algorithms to detect sunburn pomegranate for use in a sorting machine. Measurement 232: 114682.
10.1016/j.measurement.2024.114682Seki, H., Murakami, H., Ma, T., Tsuchikawa, S., Inagaki, T. 2024. Evaluating Soluble Solids in White Strawberries: A Comparative Analysis of Vis-NIR and NIR Spectroscopy. Foods 13(14): 2277. https://doi.org/10.3390/foods13142274
10.3390/foods1314227439063358PMC11275640Shanthini, K., Francis, J., George, S.N., George, S., Devassy, B.M. 2025. Early bruise detection, classification and prediction in strawberry using Vis-NIR hyperspectral imaging. Food Control 167: 110794. https://doi.org/10.1016/j.foodcont.2024.110794
10.1016/j.foodcont.2024.110794Shen, Q., Xu, D., Guo, T., Mao, X., Xia, F. 2025. Two–Stage Multimodal 3D Point Localization Framework for Automatic Grape Harvesting. Smart Agricultural Technology, 101062. https://doi.org/10.1016/j.atech.2025.101062
10.1016/j.atech.2025.101062Shui, Y., Yuan, K., Wu, M., Zhao, Z. 2024. Improved Multi-Size, Multi-Target and 3D Position Detection Network for Flowering Chinese Cabbage Based on YOLOv8. Plants 13(19): 2008. https://doi.org/10.3390/plants13192808
10.3390/plants1319280839409677PMC11479180Singh, S., Mall, R.K., Chaturvedi, A., Singh, N., Srivastava, P.K. 2024. Chapter 6 - Advances in remote sensing in measuring urban heat island effect and its management. In A. Kumar, P. K. Srivastava, P. Saikia, & R. K. Mall (Eds.), Earth Observation in Urban Monitoring (pp. 113-132). Elsevier. https://doi.org/10.1016/B978-0-323-99164-3.00011-2
10.1016/B978-0-323-99164-3.00011-2Sorour, M., From, P. 2025. Laser-Powered Harvesting Tool for Tabletop Grown Strawberries. Electronics 14(9): 1708. https://doi.org/10.3390/electronics14091708
10.3390/electronics14091708Stafford, J. V. 2000. Implementing Precision Agriculture in the 21st Century. Journal of Agricultural Engineering Research 76(3): 267-275. https://doi.org/10.1006/jaer.2000.0577
10.1006/jaer.2000.0577Su, Y., Wang, J., Bai, S. 2025. MSI-based damage detection and damage degree classification study of snow pear. Agricultural Products Processing and Storage 1(1): 8. https://doi.org/10.1007/s44462-025-00007-7
10.1007/s44462-025-00007-7Tasioulas, S., Watson, J., Kasampalis, D.S., Tsouvaltzis, P. 2025. Integration of Color Analysis, Firmness Testing, and visNIR Spectroscopy for Comprehensive Tomato Quality Assessment and Shelf-Life Prediction. Agronomy 15(2): 478. https://doi.org/10.3390/agronomy15020478
10.3390/agronomy15020478Tituaña, L., Gholami, A., He, Z., Xu, Y., Karkee, M., Ehsani, R. 2024. A small autonomous field robot for strawberry harvesting. Smart Agricultural Technology 8: 100454. https://doi.org/10.1016/j.atech.2024.100454
10.1016/j.atech.2024.100454Tong, J. 2025. AOD-Net: a lightweight real-time fruit detection algorithm for agricultural automation. Journal of Food Measurement and Characterization, 1-13. https://doi.org/10.1007/s11694-025-03149-1
10.1007/s11694-025-03149-1Tosi, F., Bartolomei, L., Poggi, M. 2025. A Survey on Deep Stereo Matching in the Twenties. International Journal of Computer Vision 133(7): 4245-4276. https://doi.org/10.1007/s11263-024-02331-0
10.1007/s11263-024-02331-0Ulu, B., Günaydın, S., Çetin, N. 2025. Comparison performance of the CNN-based deep learning models for the distinguishing ultrasound pretreated and microwave dried jujube fruits. Measurement 249: 117047. https://doi.org/10.1016/j.measurement.2025.117047
10.1016/j.measurement.2025.117047van Marrewijk, B.M., van Daalen, T., Xin, B., van Henten, E.J., Polder, G., Kootstra, G. 2025. 3D plant segmentation: Comparing a 2D-to-3D segmentation method with state-of-the-art 3D segmentation algorithms. Biosystems Engineering 254: 104147. https://doi.org/10.1016/j.biosystemseng.2025.104147
10.1016/j.biosystemseng.2025.104147Wang, C., Wang, H., Han, Q., Wu, Z., Li, C., Zhang, Z. 2025a. Litchi bunch detection and ripeness assessment using deep learning and clustering with image processing techniques. Biosystems Engineering 255: 104173. https://doi.org/10.1016/j.biosystemseng.2025.104173
10.1016/j.biosystemseng.2025.104173Wang, G., Wang, Y., Liu, D., Yang, M., Chen, G., Dai, J. 2025b. Hyperspectral-based assessment of internal quality indicators of peach and quantitative transfer learning model analysis. Postharvest Biology and Technology 230: 113824. https://doi.org/10.1016/j.postharvbio.2025.113824
10.1016/j.postharvbio.2025.113824Wang, Q., Xu, F., Chen, Q., Mi, Z., Fan, Y., Su, B. 2025c. Detection and location of wine grape (Cabernet Sauvignon) picking points by using a dual-stage deep learning method. Computers and Electronics in Agriculture 237: 110637. https://doi.org/10.1016/j.compag.2025.110637
10.1016/j.compag.2025.110637Wang, X., Burusa, A.K., Rapado-Rincón, D., Versmissen, T.A., van Henten, E.J., Kootstra, G. 2025d. Multiple object tracking with multi-view active vision to effectively find plant nodes in a cluttered tomato greenhouse. Computers and Electronics in Agriculture 234: 110266. https://doi.org/10.1016/j.compag.2025.110266
10.1016/j.compag.2025.110266Wang, X., Wen, X., Li, Y., Du, C., Zhang, D., Sun, C., Chen, B. 2025e. A Precise Detection Method for Tomato Fruit Ripeness and Picking Points in Complex Environments. Horticulturae 11(6): 585. https://doi.org/10.3390/horticulturae11060585
10.3390/horticulturae11060585Wang, Y., Li, F., Zhang, H., Shi, B. 2024. Task-level customized pruning for image classification on edge devices. Electronics 13(20): 4029. https://doi.org/10.3390/electronics13204029
10.3390/electronics13204029Wang, Y., Zhang, Z. 2025f. Segment any leaf 3D: A zero-shot 3D leaf instance segmentation method based on multi-view images. Sensors 25(2): 526. https://doi.org/10.3390/s25020526
10.3390/s2502052639860892PMC11769372Wang, Z., Wang, J., Lan, W., Wang, M., Tu, K., Zhu, L., Pan, L. 2025g. Exploring the impact of lenticels on the detection of soluble solids content in apples and pears using hyperspectral imaging and one-dimensional convolutional neural networks. Food Research International 205: 115960. https://doi.org/10.1016/j.foodres.2025.115960
10.1016/j.foodres.2025.115960Xie, K., Zhu, J., Ren, H., Wang, Y., Yang, W., Chen, G., Lin, C., Zhai, R. 2024. Delving into the potential of deep learning algorithms for point cloud segmentation at organ level in plant phenotyping. Remote Sensing 16(17): 3290. https://doi.org/10.3390/rs16173290
10.3390/rs16173290Xu, C., Liu, J., Wang, D., Guan, X., Tang, H., Li, Y. 2024. Evaluation of bruise volume quantification methods using finite element analysis for apple (Malus pumila Mill.). Postharvest Biology and Technology 213: 112930. https://doi.org/10.1016/j.postharvbio.2024.112930
10.1016/j.postharvbio.2024.112930Xu, J., Lu, Y. 2024. Prototyping and evaluation of a novel machine vision system for real-time, automated quality grading of sweetpotatoes. Computers and Electronics in Agriculture 219: 108826. https://doi.org/10.1016/j.compag.2024.108826
10.1016/j.compag.2024.108826Yan, B., Li, X., Yan, R. 2025a. An AI-Based Horticultural Plant Fruit Visual Detection Algorithm for Apple Fruits. Horticulturae 11(5): 541. https://doi.org/10.3390/horticulturae11050541
10.3390/horticulturae11050541Yan, J., Wang, G., Du, H., Liu, Y., Ouyang, A., Hu, M. 2025b. Convolutional neural networks fusing spectral shape features with attentional mechanisms for accurate prediction of soluble solids content in apples. Journal of Food Measurement and Characterization 19(1): 412-423. https://doi.org/10.1007/s11694-024-02978-w
10.1007/s11694-024-02978-wYang, H., Yang, L., Wu, T., Yuan, Y., Li, J., Li, P. 2025a. MFD-YOLO: A fast and lightweight model for strawberry growth state detection. Computers and Electronics in Agriculture 234: 110177. https://doi.org/10.1016/j.compag.2025.110177
10.1016/j.compag.2025.110177Yang, L., Noguchi, T., & Hoshino, Y. 2024. Development of a Grape Cut Point Detection System Using Multi-Cameras for a Grape-Harvesting Robot. Sensors, 24(24): 8035. https://doi.org/10.3390/s24248035
10.3390/s2424803539771770PMC11679280Yang, Y., Wijewardane, N. K., Harvey, L., Zhang, X. 2025b. Sweetpotato moisture content and textural property estimation using hyperspectral imaging and machine learning. Journal of Food Measurement and Characterization, 1-17. https://doi.org/10.1007/s11694-025-03140-w
10.1007/s11694-025-03140-wYoon, H. I., Ryu, D., Park, J.-E., Kim, H.-Y., Park, S. H., Yang, J.-S. 2024. Non-destructive prediction of rosmarinic acid content in basil plants using a portable hyperspectral imaging system and ensemble learning algorithms. Horticulturae 10(11): 1156. https://doi.org/10.3390/horticulturae10111156
10.3390/horticulturae10111156You, H., Li, Z., Wei, Z., Zhang, L., Bi, X., Bi, C., Li, X., Duan, Y. 2025a. A blueberry maturity detection method integrating attention-driven multi-scale feature interaction and dynamic upsampling. Horticulturae 11(6): 600. https://doi.org/10.3390/horticulturae11060600
10.3390/horticulturae11060600You, J., Wang, B., Qin, C., Wang, D., Jin, N., Zhao, X., Li, F., Dou, G., Bai, H. 2025b. Development of tomato non-destructive measurement system based on machine vision. Journal of Food Measurement and Characterization, 1-19. https://doi.org/10.1007/s11694-025-03197-7
10.1007/s11694-025-03197-7Yu, B., Zhao, H., Zhang, X. 2025. BlueberryNet: A lightweight CNN for real-time ripeness detection in automated blueberry processing systems. Processes 13(8): 2518. https://doi.org/10.3390/pr13082518
10.3390/pr13082518Yu, S., Yan, X., Jia, T., Qiu, D., Hu, D. 2024. Binocular structured light-based 3D reconstruction for morphological measurements of apples. Postharvest Biology and Technology 213: 112952. https://doi.org/10.1016/j.postharvbio.2024.112952
10.1016/j.postharvbio.2024.112952Yuan, F., Wang, J., Ding, W., Mei, S., Fang, C., Chen, S., Zhou, H. 2025. A lightweight and rapid dragon fruit detection method for harvesting robots. Agriculture 15(11): 1120. https://doi.org/10.3390/agriculture15111120
10.3390/agriculture15111120Zhai, M., Wang, Z., Li, H., Mo, X., Yu, Y., Dong, W., Zha, Z., Wu, J. 2025. Non-contact detection of sub-healthy apples with moldy core by air jet excitation and laser Doppler vibration sensing. Postharvest Biology and Technology 222: 113427. https://doi.org/10.1016/j.postharvbio.2025.113427
10.1016/j.postharvbio.2025.113427Zhang, B., Xu, H., Tian, K., Huang, J., Kong, F., Mu, S., Wu, T., Mu, Z., Wang, X., Zhou, D. 2024a. Research on automatic alignment for corn harvesting based on euclidean clustering and K-Means clustering. Agriculture 14(11): 2071. https://doi.org/10.3390/agriculture14112071
10.3390/agriculture14112071Zhang, B., Xu, S., Xiong, Z., Qin, H., Ai, X., Yuan, T., Li, W. 2024b. Research on robot control technology of tomato plant lowering in greenhouses. Agronomy 14(9): 1966. https://doi.org/10.3390/agronomy14091966
10.3390/agronomy14091966Zhang, J., Xie, J., Zhang, F., Gao, J., Yang, C., Song, C., Rao, W., Zhang, Y. 2024c. Greenhouse tomato detection and pose classification algorithm based on improved YOLOv5. Computers and Electronics in Agriculture 216: 108519. https://doi.org/10.1016/j.compag.2023.108519
10.1016/j.compag.2023.108519Zhang, N., Wang, M., Wang, N. 2002. Precision agriculture—a worldwide overview. Computers and Electronics in Agriculture 36(2): 113-132. https://doi.org/10.1016/S0168-1699(02)00096-0
10.1016/S0168-1699(02)00096-0Zhang, P., Dai, N., Wang, Z., Yuan, J., Xin, Z., Liu, X., Papadakis, G. 2025a. A parallel dual-arm robotic control method of white asparagus based on moving-looking-harvesting coordination and asynchronous harvest cooperation. Computers and Electronics in Agriculture 232: 110046. https://doi.org/10.1016/j.compag.2025.110046
10.1016/j.compag.2025.110046Zhang, Y., Wei, H., Tang, X., Gao, Y., Tong, H. 2025b. Research on automatic citrus grading system based on 3D vision and deep learning: Y. Zhang et al. Journal of Food Measurement and Characterization, 1-19. https://doi.org/10.1007/s11694-025-03444-x
10.1007/s11694-025-03444-xZhao, G., Dong, S., Wen, J., Ban, Y., Zhang, X. 2025. Selective fruit harvesting prediction and 6D pose estimation based on YOLOv7 multi-parameter recognition. Computers and Electronics in Agriculture 229: 109815. https://doi.org/10.1016/j.compag.2024.109815
10.1016/j.compag.2024.109815Zhao, J., Yao, X., Wang, Y., Yi, Z., Xie, Y., Zhou, X. 2024. Lightweight-Improved YOLOv5s Model for Grape Fruit and Stem Recognition. Agriculture 14(5): 774. https://doi.org/10.3390/agriculture14050774
10.3390/agriculture14050774Zhao, Q., Qu, Y. 2024. The Retrieval of Ground NDVI (Normalized Difference Vegetation Index) Data Consistent with Remote-Sensing Observations. Remote Sensing 16(7): 1212. https://doi.org/10.3390/rs16071212
10.3390/rs16071212Zheng, Y., Luo, X., Gao, Y., Sun, Z., Huang, K., Gao, W., Xu, H., Xie, L. 2025. Lycopene detection in cherry tomatoes with feature enhancement and data fusion. Food Chemistry 463: 141183. https://doi.org/10.1016/j.foodchem.2024.141183
10.1016/j.foodchem.2024.141183Zhou, H., Ahmed, A., Liu, T., Romeo, M., Beh, T., Pan, Y., Kang, H., Chen, C. 2025a. Finger vision enabled real-time defect detection in robotic harvesting. Computers and Electronics in Agriculture 234: 110222. https://doi.org/10.1016/j.compag.2025.110222
10.1016/j.compag.2025.110222Zhou, X., Zou, X., Meng, H., Wu, F., Chen, S., Luo, X. 2025b. Multi-task perception and three-dimensional picking point localization method for grapes based on structural constraints and geometric analysis. Computers and Electronics in Agriculture 238: 110814. https://doi.org/10.1016/j.compag.2025.110814
10.1016/j.compag.2025.110814Zhou, Y., Wang, X., Wang, Z., Ye, Y., Zhu, F., Yu, K., Zhao, Y. 2025c. Rolling 2D lidar-based navigation line extraction method for modern orchard automation. Agronomy 15(4): 816. https://doi.org/10.3390/agronomy15040816
10.3390/agronomy15040816Zhu, H., Yang, L., Wang, Y., Wang, Y., Hou, W., Rao, Y., Liu, L. 2024a. Enhanced detection algorithm for apple bruises using structured light imaging. Artificial Intelligence in Agriculture 11: 50-60. https://doi.org/10.1016/j.aiia.2023.12.001
10.1016/j.aiia.2023.12.001Zhu, K., Li, J., Zhang, K., Arunachalam, C., Bhattacharya, S., Lu, R., Li, Z. 2025. Foundation model-based apple ripeness and size estimation for selective harvesting. Computers and Electronics in Agriculture 236: 110407. https://doi.org/10.1016/j.compag.2025.110407
10.1016/j.compag.2025.110407Zhu, X., Huang, Z., Li, B. 2024b. Three-Dimensional Phenotyping Pipeline of Potted Plants Based on Neural Radiation Fields and Path Segmentation. Plants 13(23): 3368. https://doi.org/10.3390/plants13233368
10.3390/plants1323336839683161PMC11644607- Publisher :Korean Society of Precision Agriculture
- Publisher(Ko) :한국정밀농업학회
- Journal Title :Precision Agriculture Science and Technology
- Journal Title(Ko) :정밀농업과학기술
- Volume : 7
- No :3
- Pages :274-302
- Received Date : 2025-09-19
- Revised Date : 2025-09-28
- Accepted Date : 2025-09-28
- DOI :https://doi.org/10.22765/pastj.20250020
Precision Agriculture Science and Technology
