{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T07:13:31Z","timestamp":1773904411978,"version":"3.50.1"},"reference-count":51,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2024M763577"],"award-info":[{"award-number":["2024M763577"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62376272"],"award-info":[{"award-number":["62376272"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Industrial Information Integration"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.jii.2026.101107","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:35:46Z","timestamp":1773362146000},"page":"101107","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["CropGPT: A large multimodal model for precise and explainable diagnosis of crop pests and diseases"],"prefix":"10.1016","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9833-1729","authenticated-orcid":false,"given":"Yiding","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Zonghuan","family":"Han","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Chang","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Yuanze","family":"Qin","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiaoshuan","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8665-7075","authenticated-orcid":false,"given":"Lingxian","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"8","key":"10.1016\/j.jii.2026.101107_b1","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1038\/s41477-019-0476-y","article-title":"Non-invasive plant disease diagnostics enabled by smartphone-based fingerprinting of leaf volatiles","volume":"5","author":"Li","year":"2019","journal-title":"Nat. Plants"},{"key":"10.1016\/j.jii.2026.101107_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.biosystemseng.2026.104395","article-title":"Dynamic analysis of the infection process of cucumber powdery mildew based on instance segmentation","volume":"263","author":"Han","year":"2026","journal-title":"Biosyst. Eng."},{"key":"10.1016\/j.jii.2026.101107_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.108790","article-title":"A novel cascaded multi-task method for crop prescription recommendation based on electronic medical record","volume":"219","author":"Xu","year":"2024","journal-title":"Comput. Electron. Agric."},{"issue":"1","key":"10.1016\/j.jii.2026.101107_b4","article-title":"Deep Learning-Based trees disease recognition and classification using hyperspectral data","volume":"77","author":"Bhatti","year":"2023","journal-title":"Comput. Mater. Contin."},{"key":"10.1016\/j.jii.2026.101107_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107597","article-title":"Knowledge-guided adaptive spatial-temporal graph contrastive learning framework: Regional crop diseases prediction based on electronic medical records","volume":"189","author":"Xu","year":"2025","journal-title":"Neural Netw."},{"key":"10.1016\/j.jii.2026.101107_b6","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1109\/TIP.2021.3049334","article-title":"Plant disease recognition: A large-scale benchmark dataset and a visual region and loss reweighting approach","volume":"30","author":"Liu","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.jii.2026.101107_b7","article-title":"Monitoring corn growth with disease identification and yield prediction via advanced intelligent architecture","author":"Mhamed","year":"2025","journal-title":"J. Ind. Inf. Integr."},{"issue":"16","key":"10.1016\/j.jii.2026.101107_b8","doi-asserted-by":"crossref","first-page":"3504","DOI":"10.3390\/math11163504","article-title":"Deep-learning-based classification of Bangladeshi medicinal plants using neural ensemble models","volume":"11","author":"Uddin","year":"2023","journal-title":"Mathematics"},{"key":"10.1016\/j.jii.2026.101107_b9","doi-asserted-by":"crossref","first-page":"105776","DOI":"10.1109\/ACCESS.2021.3098307","article-title":"Discovering the ganoderma boninense detection methods using machine learning: A review of manual, laboratory, and remote approaches","volume":"9","author":"Tee","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.jii.2026.101107_b10","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.compag.2018.08.013","article-title":"Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification","volume":"153","author":"Barbedo","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.jii.2026.101107_b11","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.jii.2026.101107_b12","first-page":"27730","article-title":"Training language models to follow instructions with human feedback","volume":"35","author":"Ouyang","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.jii.2026.101107_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113197","article-title":"Knowledge assimilation: Implementing knowledge-guided agricultural large language model","volume":"314","author":"Jiang","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.jii.2026.101107_b14","series-title":"IJCAI","first-page":"5150","article-title":"AgriBERT: Knowledge-Infused agricultural language models for matching food and nutrition","author":"Rezayi","year":"2022"},{"key":"10.1016\/j.jii.2026.101107_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110218","article-title":"Agricultural large language model for standardized production of distinctive agricultural products","volume":"234","author":"Yi","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.jii.2026.101107_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103419","article-title":"Efficiently integrate large language models with visual perception: A survey from the training paradigm perspective","volume":"125","author":"Ma","year":"2026","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.jii.2026.101107_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.102998","article-title":"MammoVLM: A generative large vision\u2013language model for mammography-related diagnostic assistance","volume":"118","author":"Cao","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.jii.2026.101107_b18","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."},{"key":"10.1016\/j.jii.2026.101107_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109587","article-title":"Visual large language model for wheat disease diagnosis in the wild","volume":"227","author":"Zhang","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.jii.2026.101107_b20","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","article-title":"Deep learning in agriculture: A survey","volume":"147","author":"Kamilaris","year":"2018","journal-title":"Comput. Electron. Agric."},{"issue":"1","key":"10.1016\/j.jii.2026.101107_b21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.62762\/TMI.2024.631844","article-title":"Advances in machine intelligence: Past, present, and future","volume":"1","author":"Dhiman","year":"2024","journal-title":"ICCK Trans. Mach. Intell."},{"key":"10.1016\/j.jii.2026.101107_b22","doi-asserted-by":"crossref","first-page":"49846","DOI":"10.1109\/ACCESS.2023.3276763","article-title":"Detection of basal stem rot disease using deep learning","volume":"11","author":"Haw","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.jii.2026.101107_b23","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.1325","article-title":"Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods","volume":"9","author":"Haw","year":"2023","journal-title":"PeerJ Comput. Sci."},{"key":"10.1016\/j.jii.2026.101107_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.111286","article-title":"Quantitative characterization of lesion features and analysis of coupled stress effects of mixed infection by downy mildew and powdery mildew pathogens in cucumbers based on instance segmentation","volume":"242","author":"Han","year":"2026","journal-title":"Comput. Electron. Agric."},{"issue":"1","key":"10.1016\/j.jii.2026.101107_b25","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s13007-025-01495-1","article-title":"MicroDeblurNet: high-fidelity restoration of spatially variant defocus in microscopic images for cucumber downy mildew","volume":"22","author":"Zhang","year":"2026","journal-title":"Plant Methods"},{"key":"10.1016\/j.jii.2026.101107_b26","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.3389\/fpls.2016.01419","article-title":"Using deep learning for image-based plant disease detection","volume":"7","author":"Mohanty","year":"2016","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.jii.2026.101107_b27","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.compag.2018.04.002","article-title":"Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild","volume":"161","author":"Picon","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.jii.2026.101107_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.107390","article-title":"CAMFFNet: A novel convolutional neural network model for tobacco disease image recognition","volume":"202","author":"Lin","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.jii.2026.101107_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.jfca.2024.106400","article-title":"Rapid identification of moldy peanuts based on three-dimensional hyperspectral object detection","volume":"133","author":"Yang","year":"2024","journal-title":"J. Food Comp. Anal."},{"key":"10.1016\/j.jii.2026.101107_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2026.111443","article-title":"Intelligent management of crop diseases and pests in multiscale and multimodal complex scenarios: Technologies, applications, and prospects","volume":"244","author":"Xu","year":"2026","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.jii.2026.101107_b31","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108129","article-title":"ITF-WPI: Image and text based cross-modal feature fusion model for wolfberry pest recognition","volume":"212","author":"Dai","year":"2023","journal-title":"Comput. Electron. Agric.","ISSN":"https:\/\/id.crossref.org\/issn\/0168-1699","issn-type":"print"},{"key":"10.1016\/j.jii.2026.101107_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106098","article-title":"Few-shot vegetable disease recognition model based on image text collaborative representation learning","volume":"184","author":"Wang","year":"2021","journal-title":"Comput. Electron. Agric."},{"issue":"2","key":"10.1016\/j.jii.2026.101107_b33","doi-asserted-by":"crossref","first-page":"52","DOI":"10.62762\/TMI.2025.886122","article-title":"A novel image captioning technique using deep learning methodology","volume":"1","author":"Khan","year":"2025","journal-title":"ICCK Trans. Mach. Intell."},{"issue":"2","key":"10.1016\/j.jii.2026.101107_b34","doi-asserted-by":"crossref","first-page":"80","DOI":"10.62762\/TMI.2025.306750","article-title":"Emotion detection from speech using CNN-BiLSTM with feature rich audio inputs","volume":"1","author":"Tiwari","year":"2025","journal-title":"ICCK Trans. Mach. Intell."},{"key":"10.1016\/j.jii.2026.101107_b35","article-title":"Multimodal-information-based optimized agricultural prescription recommendation system of crop electronic medical records","volume":"43","author":"Xu","year":"2025","journal-title":"J. Ind. Inf. Integr."},{"key":"10.1016\/j.jii.2026.101107_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.107993","article-title":"Cucumber disease recognition with small samples using image-text-label-based multi-modal language model","volume":"211","author":"Cao","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.jii.2026.101107_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.cropro.2024.107006","article-title":"Small-sample cucumber disease identification based on multimodal self-supervised learning","volume":"188","author":"Cao","year":"2025","journal-title":"Crop. Prot."},{"key":"10.1016\/j.jii.2026.101107_b38","doi-asserted-by":"crossref","unstructured":"Z. Chen, J. Wu, W. Wang, W. Su, G. Chen, S. Xing, M. Zhong, Q. Zhang, X. Zhu, L. Lu, et al., Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 24185\u201324198.","DOI":"10.1109\/CVPR52733.2024.02283"},{"issue":"2","key":"10.1016\/j.jii.2026.101107_b39","first-page":"1","article-title":"Big models in agriculture: key technologies, application and future directions","volume":"6","author":"Guo","year":"2024","journal-title":"Smart Agric."},{"key":"10.1016\/j.jii.2026.101107_b40","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsm.2025.09.001","article-title":"Deepseek: implications for data science and management in the AI era","author":"Xu","year":"2025","journal-title":"Data Sci. Manag."},{"issue":"1","key":"10.1016\/j.jii.2026.101107_b41","doi-asserted-by":"crossref","first-page":"5357","DOI":"10.1038\/s41598-026-35003-9","article-title":"The development and evaluation of agricultural question-answering systems based on large language models","volume":"16","author":"Eldem","year":"2026","journal-title":"Sci. Rep."},{"key":"10.1016\/j.jii.2026.101107_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.126708","article-title":"ChatAgri: Exploring potentials of ChatGPT on cross-linguistic agricultural text classification","volume":"557","author":"Zhao","year":"2023","journal-title":"Neurocomputing"},{"key":"10.1016\/j.jii.2026.101107_b43","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109824","article-title":"Potato disease detection and prevention using multimodal AI and large language model","volume":"229","author":"Zhu","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.jii.2026.101107_b44","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2016.01419","article-title":"Using deep learning for image-based plant disease detection","volume":"7","author":"Mohanty","year":"2016","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.jii.2026.101107_b45","series-title":"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD","isbn-type":"print","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1145\/3371158.3371196","article-title":"PlantDoc: A dataset for visual plant disease detection","author":"Singh","year":"2020","ISBN":"https:\/\/id.crossref.org\/isbn\/9781450377386"},{"key":"10.1016\/j.jii.2026.101107_b46","series-title":"European Conference on Computer Vision","first-page":"38","article-title":"Grounding dino: Marrying dino with grounded pre-training for open-set object detection","author":"Liu","year":"2024"},{"issue":"2","key":"10.1016\/j.jii.2026.101107_b47","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.jii.2026.101107_b48","first-page":"34892","article-title":"Visual instruction tuning","volume":"36","author":"Liu","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.jii.2026.101107_b49","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.jii.2026.101107_b50","article-title":"MobileCLIP2: Improving Multi-Modal reinforced training","author":"Faghri","year":"2025","journal-title":"Trans. Mach. Learn. Res.","ISSN":"https:\/\/id.crossref.org\/issn\/2835-8856","issn-type":"print"},{"key":"10.1016\/j.jii.2026.101107_b51","doi-asserted-by":"crossref","unstructured":"J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, Bert: Pre-training of deep bidirectional transformers for language understanding, in: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol. 1, 2019, pp. 4171\u20134186.","DOI":"10.18653\/v1\/N19-1423"}],"container-title":["Journal of Industrial Information Integration"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2452414X26000488?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2452414X26000488?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T03:26:52Z","timestamp":1773890812000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2452414X26000488"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":51,"alternative-id":["S2452414X26000488"],"URL":"https:\/\/doi.org\/10.1016\/j.jii.2026.101107","relation":{},"ISSN":["2452-414X"],"issn-type":[{"value":"2452-414X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"CropGPT: A large multimodal model for precise and explainable diagnosis of crop pests and diseases","name":"articletitle","label":"Article Title"},{"value":"Journal of Industrial Information Integration","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jii.2026.101107","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"101107"}}