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Proceedings of the 2025 ASABE Annual International Meeting, Toronto, ON, Canada, 13\u201316 July 2025, American Society of Agricultural and Biological Engineers.","DOI":"10.13031\/aim.202500502"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"111754","DOI":"10.1016\/j.compag.2026.111754","article-title":"A review of visual navigation for agricultural robots in open fields and controlled environments","volume":"248","author":"Wang","year":"2026","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"111214","DOI":"10.1016\/j.compag.2025.111214","article-title":"Dense cotton boll counting with transformer-based video tracking and a customized phenotyping robot for data collection","volume":"240","author":"Tan","year":"2026","journal-title":"Comput. Electron. Agric."},{"key":"ref_7","unstructured":"Prasai, S., Cui, K., Zhu, R., Alqahtani, S., Zhang, Y., Pauca, V.P., Silman, M.R., and Yang, F. (2026, January 15\u201321). AwakeForest: An Interactive Geospatial Platform for Large-Scale Forest Imagery. Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-ECAI 2026), Demonstrations Track, Bremen, Germany."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cui, K., Zhu, R., Wang, M., Tang, W., Larsen, G.D., Pauca, V.P., Alqahtani, S., Yang, F., Segurado, D., and Lutz, D. (2025). Detection and geographic localization of natural objects in the wild: A case study on palms. arXiv.","DOI":"10.24963\/ijcai.2025\/1067"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"101257","DOI":"10.1016\/j.atech.2025.101257","article-title":"From sensors to insights: Technological trends in image-based high-throughput plant phenotyping","volume":"12","author":"Wang","year":"2025","journal-title":"Smart Agric. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4152816","DOI":"10.34133\/2020\/4152816","article-title":"Convolutional neural networks for image-based high-throughput plant phenotyping: A review","volume":"2020","author":"Jiang","year":"2020","journal-title":"Plant Phenomics"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1016\/j.jfutfo.2025.03.003","article-title":"Food 2050 concept: The trends that shape the future of our food","volume":"6","author":"Hamad","year":"2026","journal-title":"J. Future Foods"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, R.F., and Su, W.H. (2024). The application of deep learning in the whole potato production Chain: A Comprehensive review. Agriculture, 14.","DOI":"10.3390\/agriculture14081225"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"115066","DOI":"10.1016\/j.rser.2024.115066","article-title":"Harvesting the sun twice: Energy, food and water benefits from agrivoltaics in East Africa","volume":"208","author":"Kimaro","year":"2025","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tan, C., Li, C., Sun, J., and Song, H. (2023). Three-view cotton flower counting through multi-object tracking and multi-modal imaging. Proceedings of the 2023 ASABE Annual International Meeting, Omaha, NE, USA, 9\u201312 July 2023, American Society of Agricultural and Biological Engineers.","DOI":"10.13031\/aim.202300895"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"100776","DOI":"10.1016\/j.atech.2025.100776","article-title":"3D neural architecture search to optimize segmentation of plant parts","volume":"10","author":"Saeed","year":"2025","journal-title":"Smart Agric. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jiang, L., Li, C., and Fu, L. (2022). 3D deep Learning-based segmentation to reveal the Spatial distribution of cotton Bolls. Proceedings of the 2022 ASABE Annual International Meeting, Houston, TX, USA, 17\u201320 July 2022, American Society of Agricultural and Biological Engineers.","DOI":"10.13031\/aim.202200361"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1146\/annurev-arplant-070523-042828","article-title":"Deep learning in image-based plant phenotyping","volume":"75","author":"Murphy","year":"2024","journal-title":"Annu. Rev. Plant Biol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Nethala, P., Um, D., Vemula, N., Montero, O.F., Lee, K., and Bhandari, M. (2024). Techniques for canopy to organ level plant feature extraction via remote and proximal sensing: A survey and experiments. Remote Sens., 16.","DOI":"10.3390\/rs16234370"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cui, K., Shao, Z., Larsen, G., Pauca, V., Alqahtani, S., Segurado, D., Pinheiro, J., Wang, M., Lutz, D., and Plemmons, R. (2024). Palmprobnet: A probabilistic approach to understanding palm distributions in ecuadorian tropical forest via transfer learning. Proceedings of the 2024 ACM Southeast Conference, Marietta, GA, USA, 18\u201320 April 2024, Association for Computing Machinery.","DOI":"10.1145\/3603287.3651220"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4413815","DOI":"10.1109\/TGRS.2025.3584093","article-title":"Efficient localization and spatial distribution modeling of canopy palms using uav imagery","volume":"63","author":"Cui","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","first-page":"5505115","article-title":"Superpixel-Based Bipartite Graph Clustering Enriched with Spatial Information for Hyperspectral and LiDAR Data","volume":"63","author":"Cao","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"108730","DOI":"10.1016\/j.compag.2024.108730","article-title":"Optical remote sensing of crop biophysical and biochemical parameters: An overview of advances in sensor technologies and machine learning algorithms for precision agriculture","volume":"218","author":"Kganyago","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"123820","DOI":"10.1016\/j.indcrop.2026.123820","article-title":"Methodological framework of automatic field-based plot extraction and maize emergence counting using UAV imagery and deep learning","volume":"250","author":"Pun","year":"2026","journal-title":"Ind. Crops Prod."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"122225","DOI":"10.1016\/j.indcrop.2025.122225","article-title":"Field-scale single-plant sunflower head detection and geometric parameters measurement integrating multi-modal UAV data, deep learning and point cloud analysis","volume":"237","author":"Dong","year":"2025","journal-title":"Ind. Crops Prod."},{"key":"ref_25","unstructured":"Cui, K., Bohara, S., Prasai, S., Shao, Z., Tang, W., Pillaca, M., Flores, E., Yang, Z., Larsen, G., and Dethier, E. (2026). ELDOR: A Dataset and Benchmark for Illegal Gold Mining in the Amazon Rainforest. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5510514","DOI":"10.1109\/TGRS.2024.3370633","article-title":"Contrastive multiview subspace clustering of hyperspectral images based on graph convolutional networks","volume":"62","author":"Guan","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"100292","DOI":"10.1016\/j.sintl.2024.100292","article-title":"Integrating artificial intelligence and Internet of Things (IoT) for enhanced crop monitoring and management in precision agriculture","volume":"5","author":"Sharma","year":"2024","journal-title":"Sens. Int."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hamrani, A., Allouhi, A., Bouarab, F.Z., and Jayachandran, K. (2025). AI and robotics in agriculture: A systematic and quantitative review of research trends (2015\u20132025). Crops, 5.","DOI":"10.20944\/preprints202509.0408.v1"},{"key":"ref_29","unstructured":"Wang, Q., Ye, H., Kim, J., Ke, J., Wang, Y., Kuo, M., Shao, Z., Li, D., Lin, Y., and Jiang, T. (2026). T2S-Bench & Structure-of-Thought: Benchmarking and Prompting Comprehensive Text-to-Structure Reasoning. arXiv."},{"key":"ref_30","unstructured":"Wang, Y., Jiang, T., Shao, Z., Ye, H., Sun, J., Ma, M., Zhang, J., Chen, Y., and Li, H. (2026). Zeus: Accelerating diffusion models with only second-order predictor. arXiv."},{"key":"ref_31","unstructured":"Jiang, T., Wang, Y., Ye, H., Shao, Z., Sun, J., Zhang, J., Chen, Z., Zhang, J., Chen, Y., and Li, H. (2025). Sada: Stability-guided adaptive diffusion acceleration. arXiv."},{"key":"ref_32","unstructured":"Shao, Z., Zhang, L., Cui, K., Wang, Y., Jiang, T., Ye, H., Wang, Q., Du, Z., Fu, Y., and Yang, F. (2026, January 10\u201311). DecodeShare: Tracing the Shared Subspace of LLM Decode-Time Decisions. Proceedings of the Forty-third International Conference on Machine Learning, Seoul, Republic of Korea."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shao, Z., Wang, Y., Wang, Q., Jiang, T., Du, Z., Ye, H., Zhuo, D., Chen, Y., and Li, H. (2026, January 20\u201327). Flashsvd: Memory-efficient inference with streaming for low-rank models. Proceedings of the AAAI Conference on Artificial Intelligence, Singapore.","DOI":"10.1609\/aaai.v40i30.39720"},{"key":"ref_34","unstructured":"Wu, W., Shao, Z., Cui, K., Kim, J., Wang, Y., Ye, H., Zhuo, D., and Chen, Y. (2026). FlashSVD v1. 5: Making Low-Rank Transformers Inference Actually Fast. arXiv."},{"key":"ref_35","unstructured":"Wang, Q., Shi, J., Wan, K., Zhao, H., Ye, H., Shao, Z., Ke, J., Liu, Y., Miranda, D., and Lapsiya, P. (2026, January 6\u201311). Seeing is Solving: Unlocking Efficient Multimodal RL via View Alignment. Proceedings of the Forty-third International Conference on Machine Learning, Seoul, Republic of Korea."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Han, Y., Duan, B., Guan, R., Yang, G., and Zhen, Z. (2024). LUFFD-YOLO: A lightweight model for UAV remote sensing forest fire detection based on attention mechanism and multi-level feature fusion. Remote Sens., 16.","DOI":"10.3390\/rs16122177"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"123877","DOI":"10.1016\/j.indcrop.2026.123877","article-title":"Machine learning models for plant fungal disease detection and risk prediction: A systematic review","volume":"250","author":"Mehdizadeh","year":"2026","journal-title":"Ind. Crops Prod."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e70060","DOI":"10.1002\/ppj2.70060","article-title":"Artificial intelligence-powered plant phenomics: Progress, challenges, and opportunities","volume":"9","author":"Wang","year":"2026","journal-title":"Plant Phenome J."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"110057","DOI":"10.1016\/j.compag.2025.110057","article-title":"In-field blueberry fruit phenotyping with a MARS-PhenoBot and customized BerryNet","volume":"232","author":"Li","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5535216","DOI":"10.1109\/TGRS.2024.3464648","article-title":"Spatial\u2013spectral graph contrastive clustering with hard sample mining for hyperspectral images","volume":"62","author":"Guan","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4405818","DOI":"10.1109\/TGRS.2024.3385202","article-title":"Superpixel-based and spatially regularized diffusion learning for unsupervised hyperspectral image clustering","volume":"62","author":"Cui","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"100721","DOI":"10.1016\/j.atech.2024.100721","article-title":"Towards rigorous dataset quality standards for deep learning tasks in precision agriculture: A case study exploration","volume":"10","author":"Carraro","year":"2025","journal-title":"Smart Agric. Technol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"100550","DOI":"10.1016\/j.atech.2024.100550","article-title":"An efficient and lightweight banana detection and localization system based on deep CNNs for agricultural robots","volume":"9","author":"Zheng","year":"2024","journal-title":"Smart Agric. Technol."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Xu, C., Chen, R., Huang, X., Han, Y., Tong, N., and Shen, S. (2026). Applications, Challenges, and Prospects of Artificial Intelligence in Crop Production. Plants, 15.","DOI":"10.3390\/plants15121863"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Dong, G., Lou, X., and Wang, H. (2026). Towards Robust UAV Navigation in Agriculture: Key Technologies, Application, and Future Directions. Plants, 15.","DOI":"10.3390\/plants15091303"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, H.W., Qin, Y.M., Wu, A.Q., Xi, X., Hu, P., and Wang, R.F. (2026). Ground Mobile Robots for High-Throughput Plant Phenotyping: A Review from the Closed-Loop Perspective of Perception, Decision, and Action. Plants, 15.","DOI":"10.20944\/preprints202603.1236.v1"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Jin, M., Ma, L., Cheng, L., Liu, Z., Tang, Z., Li, W., Du, R., Sun, T., Xiang, Y., and Zhang, F. (2026). Hyperspectral Estimation of Layer-Specific Leaf Nitrogen Content in Potato Canopy by Integrating Fractional-Order Derivatives and Three-Band Spectral Indices. Plants, 15.","DOI":"10.3390\/plants15132045"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Han, Y., Han, M., Gao, Y., He, M., Wu, Z., and Yang, W. (2026). Morphological Characteristics, Sediment Grain Size, and Spatial Distribution Patterns of Caragana tibetica Nabkhas in Desert Steppe. Plants, 15.","DOI":"10.3390\/plants15081235"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Qian, R., Zhang, Q., Gong, Y., Wang, J., Cui, X., Yin, X., and Li, M. (2026). Hybrid Deep Learning\u2013Geostatistical Mapping of Forest Aboveground Biomass in Lishui, China. 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A Precise Apple Quality Prediction Model Integrating Driving Factor Screening and BP Neural Network. Plants, 14.","DOI":"10.3390\/plants14243795"}],"container-title":["Plants"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2223-7747\/15\/17\/2706\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T09:47:26Z","timestamp":1788428846000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2223-7747\/15\/17\/2706"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,3]]},"references-count":53,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["plants15172706"],"URL":"https:\/\/doi.org\/10.3390\/plants15172706","relation":{},"ISSN":["2223-7747"],"issn-type":[{"value":"2223-7747","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,3]]}}}