{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T07:44:52Z","timestamp":1786088692720,"version":"3.56.0"},"reference-count":69,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFE0126100"],"award-info":[{"award-number":["2019YFE0126100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62476251"],"award-info":[{"award-number":["62476251"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32401708"],"award-info":[{"award-number":["32401708"]}],"id":[{"id":"10.13039\/501100001809","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":[[2026,10]]},"DOI":"10.1016\/j.compag.2026.112154","type":"journal-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T08:44:26Z","timestamp":1784105066000},"page":"112154","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["DELKNet\u00a0+\u00a0LLM: lightweight multi-model integration real-time perception and pre decision analysis for facility tomato"],"prefix":"10.1016","volume":"253","author":[{"given":"Yun","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yijia","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4422-472X","authenticated-orcid":false,"given":"Xing","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingquan","family":"Chu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanan","family":"Mi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Petr","family":"Skobelev","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"21","key":"10.1016\/j.compag.2026.112154_b0005","doi-asserted-by":"crossref","first-page":"6289","DOI":"10.1093\/jxb\/eraf315","article-title":"Tomato in the spotlight: light regulation of whole-plant physiology","volume":"76","author":"Heuvelink","year":"2025","journal-title":"J. Exp. Bot."},{"issue":"2","key":"10.1016\/j.compag.2026.112154_b0010","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.tplants.2023.09.001","article-title":"Are unmanned aerial vehicle-based hyperspectral imaging and machine learning advancing crop science?","volume":"29","author":"Matese","year":"2024","journal-title":"Trends Plant Sci."},{"key":"10.1016\/j.compag.2026.112154_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.109908","article-title":"MetaFruit meets foundation models: Leveraging a comprehensive multi-fruit dataset for advancing agricultural foundation models","volume":"231","author":"Li","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.112154_b0020","doi-asserted-by":"crossref","first-page":"26839","DOI":"10.1109\/ACCESS.2024.3365742","article-title":"A review on large language models: architectures, applications, taxonomies, open issues and challenges","volume":"12","author":"Raiaan","year":"2024","journal-title":"IEEE Access"},{"issue":"2","key":"10.1016\/j.compag.2026.112154_b0025","first-page":"192","article-title":"Deep learning-based classification, detection, and segmentation of tomato leaf diseases: a state-of-the-art review","volume":"15","author":"Das","year":"2025","journal-title":"Artif. Intell. Agric."},{"key":"10.1016\/j.compag.2026.112154_b0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110806","article-title":"Evaluating and deploying Large Vision-Language Models for fruit quality assessment in smart agriculture systems","volume":"238","author":"Huang","year":"2025","journal-title":"Comput. Electron. Agric."},{"issue":"11","key":"10.1016\/j.compag.2026.112154_b0035","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.1016\/j.tplants.2020.05.009","article-title":"Altering plant architecture to improve performance and resistance","volume":"25","author":"Guo","year":"2020","journal-title":"Trends Plant Sci."},{"issue":"8","key":"10.1016\/j.compag.2026.112154_b0040","doi-asserted-by":"crossref","DOI":"10.1093\/hr\/uhaf109","article-title":"Phenotypic dynamics and temporal heritability of tomato architectural traits using an unmanned ground vehicle-based plant phenotyping system","volume":"12","author":"Xie","year":"2025","journal-title":"Hortic. Res."},{"key":"10.1016\/j.compag.2026.112154_b0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110387","article-title":"TomPhenoNet: a multi-modal fusion and multi-task learning network model for monitoring growth parameters of dwarf tomatoes","volume":"235","author":"Ma","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.112154_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.111380","article-title":"DRP-Net and clustering algorithm for Stem-Leaf segmentation and phenotypic trait extraction from tomato point clouds","volume":"243","author":"Yao","year":"2026","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.112154_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.atech.2025.101334","article-title":"Accurate organ segmentation and phenotype extraction of tomato plants based on deep learning and clustering algorithm","author":"Wang","year":"2025","journal-title":"Smart Agric. Technol."},{"key":"10.1016\/j.compag.2026.112154_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.109895","article-title":"Semantic segmentation-based observation pose estimation method for tomato harvesting robots","volume":"230","author":"Dong","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.112154_b0065","article-title":"Branch length recognition and pruning point localization method for greenhouse tomatoes based on improved YOLOv8","author":"Qiu","year":"2025","journal-title":"Smart Agric. Technol."},{"key":"10.1016\/j.compag.2026.112154_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.119368","article-title":"Phenomics for Komatsuna plant growth tracking using deep learning approach","volume":"215","author":"Kolhar","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.compag.2026.112154_b0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.atech.2025.101465","article-title":"GCD-YOLO: a deep learning network for accurate tomato fruit stalks identification in unstructured environments","author":"Weng","year":"2025","journal-title":"Smart Agric. Technol."},{"key":"10.1016\/j.compag.2026.112154_b0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.atech.2025.101324","article-title":"Incremental learning with domain adaption for tomato plant phenotyping","author":"Cardellicchio","year":"2025","journal-title":"Smart Agric. Technol."},{"issue":"1","key":"10.1016\/j.compag.2026.112154_b0085","doi-asserted-by":"crossref","first-page":"25726","DOI":"10.1038\/s41598-025-06692-5","article-title":"Optimization of a multi-environmental detection model for tomato growth point buds based on multi-strategy improved YOLOv8","volume":"15","author":"Liu","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compag.2026.112154_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109524","article-title":"Tomato fruit detection and phenotype calculation method based on the improved RTDETR model","volume":"227","author":"Gu","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.112154_b0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110192","article-title":"YOLOR-Stem: gaussian rotating bounding boxes and probability similarity measure for enhanced tomato main stem detection","volume":"233","author":"Gao","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.112154_b0100","doi-asserted-by":"crossref","unstructured":"Raiaan, M. A. K., Mukta, M. S. H., Fatema, K., Fahad, N. M., Sakib, S., Mim, M. M. J., ... & Azam, S. (2024). A review on large language models: Architectures, applications, taxonomies, open issues and challenges. IEEE access, 12, 26839-26874.","DOI":"10.1109\/ACCESS.2024.3365742"},{"issue":"1","key":"10.1016\/j.compag.2026.112154_b0105","first-page":"20","article-title":"Agricultural large language model based on precise knowledge retrieval and knowledge collaborative generation","volume":"7","author":"Jiang","year":"2025","journal-title":"Smart Agric."},{"key":"10.1016\/j.compag.2026.112154_b0110","unstructured":"Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., ... & McGrew, B. (2023). Gpt-4 technical report. arXiv preprint arXiv:2303.08774."},{"issue":"4","key":"10.1016\/j.compag.2026.112154_b0115","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1007\/s11023-020-09548-1","article-title":"GPT-3: its nature, scope, limits, and consequences","volume":"30","author":"Floridi","year":"2020","journal-title":"Mind. Mach."},{"key":"10.1016\/j.compag.2026.112154_b0120","unstructured":"Yang, A., Li, A., Yang, B., Zhang, B., Hui, B., Zheng, B., ... & Qiu, Z. (2025). Qwen3 technical report. arXiv preprint arXiv:2505.09388."},{"key":"10.1016\/j.compag.2026.112154_b0125","unstructured":"Team, G., Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., ... & Batsaikhan, B. O. (2024). Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXiv preprint arXiv:2403.05530."},{"issue":"1","key":"10.1016\/j.compag.2026.112154_b0130","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1038\/s44172-024-00271-8","article-title":"Interactive computer-aided diagnosis on medical image using large language models","volume":"3","author":"Wang","year":"2024","journal-title":"Commun. Eng."},{"issue":"13","key":"10.1016\/j.compag.2026.112154_b0135","doi-asserted-by":"crossref","first-page":"19141","DOI":"10.1007\/s10639-025-13550-4","article-title":"A systematic review of AI, VR, and LLM applications in special education: opportunities, challenges, and future directions","volume":"30","author":"Voultsiou","year":"2025","journal-title":"Educ. Inf. Technol."},{"key":"10.1016\/j.compag.2026.112154_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.111165","article-title":"Systems engineering issues for industry applications of large language model","volume":"151","author":"Chen","year":"2024","journal-title":"Appl. Soft Comput."},{"issue":"1","key":"10.1016\/j.compag.2026.112154_b0145","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MIS.2024.3517792","article-title":"The rise of small language models","volume":"40","author":"Zhang","year":"2025","journal-title":"IEEE Intell. Syst."},{"key":"10.1016\/j.compag.2026.112154_b0150","doi-asserted-by":"crossref","first-page":"54608","DOI":"10.1109\/ACCESS.2024.3389497","article-title":"GPT (generative pre-trained transformer)\u2014a comprehensive review on enabling technologies, potential applications, emerging challenges, and future directions","volume":"12","author":"Yenduri","year":"2024","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.compag.2026.112154_b0155","first-page":"20","article-title":"Agricultural large language model based on precise knowledge retrieval and knowledge collaborative generation","volume":"7","author":"Jiang","year":"2025","journal-title":"Smart Agric."},{"key":"10.1016\/j.compag.2026.112154_b0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108168","article-title":"GPT-aided diagnosis on agricultural image based on a new light YOLOPC","volume":"213","author":"Qing","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.112154_b0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109268","article-title":"Investigating the effect of different fine-tuning configuration scenarios on agricultural term extraction using BERT","volume":"225","author":"Panoutsopoulos","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.112154_b0170","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2025.3589241","article-title":"RAG-driven memory architectures in conversational llms-a literature review with insights into emerging agriculture data sharing","author":"Akbar","year":"2025","journal-title":"IEEE Access"},{"key":"10.1016\/j.compag.2026.112154_b0175","doi-asserted-by":"crossref","unstructured":"Devi, K. S., & Santhosh, M. (2025, August). AI-Driven Gardening Assistant: Intelligent Plant Care and Disease Detection Using AI. In 2025 8th International Conference on Circuit, Power & Computing Technologies (ICCPCT) (pp. 1158-1162). IEEE.","DOI":"10.1109\/ICCPCT65132.2025.11176616"},{"key":"10.1016\/j.compag.2026.112154_b0180","doi-asserted-by":"crossref","unstructured":"Chen, X., Song, G., Xiao, X., Li, W., Fan, Y., & Run, D. (2025, August). Fine-tuning multimodal large models for bean, strawberry, and tomato disease identification. In Fifth International Conference on Computer Vision and Pattern Analysis (ICCPA 2025) (Vol. 13733, pp. 86-91). SPIE.","DOI":"10.1117\/12.3076689"},{"issue":"7","key":"10.1016\/j.compag.2026.112154_b0185","doi-asserted-by":"crossref","first-page":"3850","DOI":"10.3390\/app15073850","article-title":"Enhancing plant protection knowledge with large language models: a fine-tuned question-answering system using lora","volume":"15","author":"Xiong","year":"2025","journal-title":"Appl. Sci."},{"key":"10.1016\/j.compag.2026.112154_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110442","article-title":"CDIP-ChatGLM3: a dual-model approach integrating computer vision and language modeling for crop disease identification and prescription","volume":"236","author":"Yan","year":"2025","journal-title":"Comput. Electron. Agric."},{"issue":"1","key":"10.1016\/j.compag.2026.112154_b0195","first-page":"97","article-title":"Tomato growth height prediction method by phenotypic feature extraction using multi-modal data","volume":"7","author":"Gong","year":"2025","journal-title":"Smart Agric."},{"key":"10.1016\/j.compag.2026.112154_b0200","doi-asserted-by":"crossref","unstructured":"Tace, Y., Tabaa, M., Elfilali, S., Bensag, H., & Leghris, C. (2024, May). Novel approach for detecting Bacterial spot combining Transfer Learning and Large Language Models. In 2024 IEEE 12th International Symposium on Signal, Image, Video and Communications (ISIVC) (pp. 1-6). IEEE.","DOI":"10.1109\/ISIVC61350.2024.10577820"},{"issue":"1","key":"10.1016\/j.compag.2026.112154_b0205","first-page":"20","article-title":"Agricultural large language model based on precise knowledge retrieval and knowledge collaborative generation","volume":"7","author":"Jiang","year":"2025","journal-title":"Smart Agric."},{"key":"10.1016\/j.compag.2026.112154_b0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.plaphe.2025.100094","article-title":"ChatLeafDisease: a chain-of-thought prompting approach for crop disease classification using large language models","author":"Pan","year":"2025","journal-title":"Plant Phenomics"},{"key":"10.1016\/j.compag.2026.112154_b0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110028","article-title":"Integrating reinforcement learning and large language models for crop production process management optimization and control through a new knowledge-based deep learning paradigm","volume":"232","author":"Chen","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.112154_b0220","unstructured":"Zhang, W., Wang, L., Lu, L., Xu, M., Li, S., Yang, Y., & Fang, T. (2026). Agri-R1: Empowering Generalizable Agricultural Reasoning in Vision-Language Models with Reinforcement Learning. arXiv preprint arXiv:2601.04672."},{"key":"10.1016\/j.compag.2026.112154_b0225","series-title":"In Proceedings of the Computer Vision and Pattern Recognition Conference","first-page":"4194","article-title":"Multi-agent systems for robotic autonomy with LLMs","author":"Chen","year":"2025"},{"key":"10.1016\/j.compag.2026.112154_b0230","doi-asserted-by":"crossref","unstructured":"Lim, J., Vogel-Heuser, B., & Kovalenko, I. (2024, August). Large language model-enabled multi-agent manufacturing systems. In 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE) (pp. 3940-3946). IEEE.","DOI":"10.1109\/CASE59546.2024.10711432"},{"key":"10.1016\/j.compag.2026.112154_b0235","unstructured":"NERCITA (2025). Facility Horticulture Environmental Monitoring and Intelligent Control Platform. National Engineering Research Center for Information Technology in Agriculture. https:\/\/njtg.nercita.org.cn\/user\/index.shtml."},{"key":"10.1016\/j.compag.2026.112154_b0240","unstructured":"CNKI (China National Knowledge Infrastructure). (2025). China National Knowledge Infrastructure. Retrieved January 17, 2025, from https:\/\/www.cnki.net\/."},{"key":"10.1016\/j.compag.2026.112154_b0245","unstructured":"Wikimedia Foundation. (2025). Wikipedia: The Free Encyclopedia [Database]. Retrieved January 17, 2025, from https:\/\/zh.wikipedia.org\/wiki\/Wikipedia."},{"key":"10.1016\/j.compag.2026.112154_b0250","unstructured":"Yang, B., Zhang, Y., Feng, L., Chen, Y., Zhang, J., Xu, X., ... & Li, S. (2025). Agrigpt: A large language model ecosystem for agriculture. arXiv preprint arXiv:2508.08632."},{"key":"10.1016\/j.compag.2026.112154_b0255","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110757","article-title":"Grain-YOLO: an improved lightweight YOLO v8 and its android deployment for rice grains detection","volume":"237","author":"Liu","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.112154_b0260","unstructured":"Li, H., Li, J., Wei, H., Liu, Z., Zhan, Z., & Ren, Q. (2022). Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles. arXiv preprint arXiv:2206.02424, 10(5)."},{"key":"10.1016\/j.compag.2026.112154_b0265","doi-asserted-by":"crossref","DOI":"10.3389\/frai.2025.1622292","article-title":"Survey and analysis of hallucinations in large language models: attribution to prompting strategies or model behavior","volume":"8","author":"Anh-Hoang","year":"2025","journal-title":"Frontiers in Artificial Intelligence"},{"key":"10.1016\/j.compag.2026.112154_b0270","unstructured":"Bai, J., Bai, S., Chu, Y., Cui, Z., Dang, K., Deng, X., ... & Zhu, T. (2023). Qwen technical report. arXiv preprint arXiv:2309.16609."},{"key":"10.1016\/j.compag.2026.112154_b0275","doi-asserted-by":"crossref","unstructured":"Graham, Y. (2015, September). Re-evaluating automatic summarization with BLEU and 192 shades of ROUGE. In Proceedings of the 2015 conference on empirical methods in natural language processing (pp. 128-137).","DOI":"10.18653\/v1\/D15-1013"},{"key":"10.1016\/j.compag.2026.112154_b0280","doi-asserted-by":"crossref","unstructured":"Sohan, M., Sai Ram, T., & Rami Reddy, C. V. (2024). A review on yolov8 and its advancements. In International conference on data intelligence and cognitive informatics (pp. 529-545). Springer, Singapore.","DOI":"10.1007\/978-981-99-7962-2_39"},{"key":"10.1016\/j.compag.2026.112154_b0285","doi-asserted-by":"crossref","unstructured":"Wang, C. Y., Yeh, I. H., & Mark Liao, H. Y. (2024, September). Yolov9: Learning what you want to learn using programmable gradient information. In European conference on computer vision (pp. 1-21). Cham: Springer Nature Switzerland.","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"10.1016\/j.compag.2026.112154_b0290","doi-asserted-by":"crossref","first-page":"107984","DOI":"10.52202\/079017-3429","article-title":"Yolov10: real-time end-to-end object detection","volume":"37","author":"Wang","year":"2024","journal-title":"Adv. Neural Information Processing Sys."},{"key":"10.1016\/j.compag.2026.112154_b0295","unstructured":"Khanam, R., & Hussain, M. (2024). Yolov11: An overview of the key architectural enhancements. arXiv preprint arXiv:2410.17725."},{"issue":"4","key":"10.1016\/j.compag.2026.112154_b0300","doi-asserted-by":"crossref","first-page":"2388","DOI":"10.1109\/TPAMI.2024.3524377","article-title":"Hyper-yolo: when visual object detection meets hypergraph computation","volume":"47","author":"Feng","year":"2024","journal-title":"IEEE Trans. Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.compag.2026.112154_b0305","unstructured":"Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., ... & Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861."},{"key":"10.1016\/j.compag.2026.112154_b0310","article-title":"A calculation method of phenotypic traits based on three-dimensional reconstruction of tomato canopy","volume":"204","author":"Zhu","year":"2023","journal-title":"Comput. Electron.n Agric."},{"issue":"1","key":"10.1016\/j.compag.2026.112154_b0315","article-title":"Fine-tuning large language models for specialized use cases","volume":"3","author":"Anisuzzaman","year":"2025","journal-title":"Mayo Clinic Proce.: Digital Health"},{"issue":"10","key":"10.1016\/j.compag.2026.112154_b0320","article-title":"RAG-enhanced smart farming: a methodology for multimodal fusion and AI-driven crop health diagnosis","volume":"18","author":"Prathusha","year":"2025","journal-title":"Int. J. Intelligent Eng. & Sys."},{"key":"10.1016\/j.compag.2026.112154_b0325","series-title":"Multiple View Geometry in Computer Vision","author":"Hartley","year":"2003"},{"issue":"2","key":"10.1016\/j.compag.2026.112154_b0330","first-page":"3","article-title":"Lora: Low-rank adaptation of large language models","volume":"1","author":"Hu","year":"2022","journal-title":"Iclr"},{"key":"10.1016\/j.compag.2026.112154_b0335","unstructured":"Steel, R. G. D., & Torrie, J. H. (1960). Principles and procedures of statistics."},{"issue":"4","key":"10.1016\/j.compag.2026.112154_b0340","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.ijforecast.2006.03.001","article-title":"Another look at measures of forecast accuracy","volume":"22","author":"Hyndman","year":"2006","journal-title":"Int. J. Forecasting"},{"issue":"2","key":"10.1016\/j.compag.2026.112154_b0345","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2023.e13167","article-title":"A state of art review on estimation of solar radiation with various models","volume":"9","author":"G\u00fcrel","year":"2023","journal-title":"Heliyon"}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926007490?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926007490?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T07:00:19Z","timestamp":1786086019000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0168169926007490"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":69,"alternative-id":["S0168169926007490"],"URL":"https:\/\/doi.org\/10.1016\/j.compag.2026.112154","relation":{},"ISSN":["0168-1699"],"issn-type":[{"value":"0168-1699","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DELKNet\u00a0+\u00a0LLM: lightweight multi-model integration real-time perception and pre decision analysis for facility tomato","name":"articletitle","label":"Article Title"},{"value":"Computers and Electronics in Agriculture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compag.2026.112154","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"112154"}}