{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T08:17:26Z","timestamp":1783066646217,"version":"3.54.6"},"reference-count":38,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100006407","name":"Henan Province Natural Science Foundation","doi-asserted-by":"publisher","award":["262300421269"],"award-info":[{"award-number":["262300421269"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFD1400302"],"award-info":[{"award-number":["2022YFD1400302"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.engappai.2026.115046","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T14:02:45Z","timestamp":1778248965000},"page":"115046","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P1","title":["Development and application of a new cotton aphid detection network under complex background"],"prefix":"10.1016","volume":"178","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3454-3535","authenticated-orcid":false,"given":"Liangliang","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengjie","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7641-5344","authenticated-orcid":false,"given":"Jinpu","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shixin","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongbo","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.115046_b1","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al., 2016. TensorFlow: A system for large-scale machine learning. In: 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16). pp. 265\u2013283."},{"key":"10.1016\/j.engappai.2026.115046_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106279","article-title":"Tomato plant disease detection using transfer learning with C-GAN synthetic images","volume":"187","author":"Abbas","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.engappai.2026.115046_b3","article-title":"Diagnosis and recognition of grape leaf diseases: An automated system based on a novel saliency approach and canonical correlation analysis based multiple features fusion","volume":"24","author":"Adeel","year":"2019","journal-title":"Sustain. Comput.: Inform. Syst."},{"issue":"3","key":"10.1016\/j.engappai.2026.115046_b4","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1007\/s00371-021-02067-9","article-title":"Class-discriminative focal loss for extreme imbalanced multiclass object detection towards autonomous driving","volume":"38","author":"Chen","year":"2022","journal-title":"Vis. Comput."},{"key":"10.1016\/j.engappai.2026.115046_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105612","article-title":"Occurrence prediction of cotton pests and diseases by bidirectional long short-term memory networks with climate and atmosphere circulation","volume":"176","author":"Chen","year":"2020","journal-title":"Comput. Electron. Agric."},{"issue":"2","key":"10.1016\/j.engappai.2026.115046_b6","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1093\/comjnl\/bxaa098","article-title":"A cotton disease diagnosis method using a combined algorithm of case-based reasoning and fuzzy logic","volume":"64","author":"Dong","year":"2021","journal-title":"Comput. J."},{"key":"10.1016\/j.engappai.2026.115046_b7","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/9153207","article-title":"Classification of citrus diseases using optimization deep learning approach","volume":"2022","author":"Elaraby","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"10.1016\/j.engappai.2026.115046_b8","article-title":"Oil palm fresh fruit bunch ripeness classification on mobile devices using deep learning approaches","volume":"188","author":"Elwirehardja","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.engappai.2026.115046_b9","series-title":"Yolox: Exceeding yolo series in 2021","author":"Ge","year":"2021"},{"key":"10.1016\/j.engappai.2026.115046_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.107407","article-title":"CST: Convolutional swin transformer for detecting the degree and types of plant diseases","volume":"202","author":"Guo","year":"2022","journal-title":"Comput. Electron. Agric."},{"issue":"1","key":"10.1016\/j.engappai.2026.115046_b11","doi-asserted-by":"crossref","first-page":"6334","DOI":"10.1038\/s41598-022-10140-z","article-title":"Deep learning-based approach for identification of diseases of maize crop","volume":"12","author":"Haque","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.engappai.2026.115046_b12","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.compag.2017.02.026","article-title":"An improved moth flame optimization algorithm based on rough sets for tomato diseases detection","volume":"136","author":"Hassanien","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.engappai.2026.115046_b13","doi-asserted-by":"crossref","DOI":"10.3389\/fphys.2022.911297","article-title":"Small object detection via pixel level balancing with applications to blood cell detection","volume":"13","author":"Hu","year":"2022","journal-title":"Front. Physiol."},{"key":"10.1016\/j.engappai.2026.115046_b14","series-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"Ioffe","year":"2015"},{"key":"10.1016\/j.engappai.2026.115046_b15","series-title":"Ultralytics\/Yolov5: V6.2-Yolov5 Classification Models, Apple M1, Reproducibility, Clearml and Deci.ai Integrations","author":"Jocher","year":"2022"},{"issue":"16","key":"10.1016\/j.engappai.2026.115046_b16","doi-asserted-by":"crossref","first-page":"8140","DOI":"10.3390\/app12168140","article-title":"Complete blood cell detection and counting based on deep neural networks","volume":"12","author":"Lee","year":"2022","journal-title":"Appl. Sci."},{"key":"10.1016\/j.engappai.2026.115046_b17","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P., 2017. Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2980\u20132988.","DOI":"10.1109\/ICCV.2017.324"},{"key":"10.1016\/j.engappai.2026.115046_b18","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B., 2021a. Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 10012\u201310022.","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"7","key":"10.1016\/j.engappai.2026.115046_b19","doi-asserted-by":"crossref","first-page":"4486","DOI":"10.1109\/TCSVT.2021.3127149","article-title":"SwinNet: Swin transformer drives edge-aware RGB-D and RGB-T salient object detection","volume":"32","author":"Liu","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.engappai.2026.115046_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2019.104859","article-title":"Crop yield prediction with deep convolutional neural networks","volume":"163","author":"Nevavuori","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.engappai.2026.115046_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.116473","article-title":"Tea chrysanthemum detection under unstructured environments using the TC-YOLO model","volume":"193","author":"Qi","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.115046_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.106780","article-title":"An improved YOLOv5 model based on visual attention mechanism: Application to recognition of tomato virus disease","volume":"194","author":"Qi","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.engappai.2026.115046_b23","doi-asserted-by":"crossref","unstructured":"Redmon, 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\u2013788.","DOI":"10.1109\/CVPR.2016.91"},{"key":"10.1016\/j.engappai.2026.115046_b24","first-page":"1","article-title":"A fast accurate fine-grain object detection model based on YOLOv4 deep neural network","author":"Roy","year":"2022","journal-title":"Neural Comput. Appl."},{"issue":"1","key":"10.1016\/j.engappai.2026.115046_b25","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1007\/s10489-021-02452-w","article-title":"Citrus disease detection and classification using end-to-end anchor-based deep learning model","volume":"52","author":"Syed-Ab-Rahman","year":"2022","journal-title":"Appl. Intell."},{"key":"10.1016\/j.engappai.2026.115046_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.106853","article-title":"An efficient attention module for instance segmentation network in pest monitoring","volume":"195","author":"Wang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.engappai.2026.115046_b27","article-title":"Tomato anomalies detection in greenhouse scenarios based on YOLO-Dense","volume":"12","author":"Wang","year":"2021","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.engappai.2026.115046_b28","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S., 2018. Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision. ECCV, pp. 3\u201319.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"10.1016\/j.engappai.2026.115046_b29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-019-3262-y","article-title":"Occurrence prediction of pests and diseases in cotton on the basis of weather factors by long short term memory network","volume":"20","author":"Xiao","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"10.1016\/j.engappai.2026.115046_b30","doi-asserted-by":"crossref","first-page":"751","DOI":"10.3389\/fpls.2020.00751","article-title":"A deep-learning-based real-time detector for grape leaf diseases using improved convolutional neural networks","volume":"11","author":"Xie","year":"2020","journal-title":"Front. Plant Sci."},{"issue":"23","key":"10.1016\/j.engappai.2026.115046_b31","doi-asserted-by":"crossref","first-page":"7949","DOI":"10.3390\/s21237949","article-title":"One spatio-temporal sharpening attention mechanism for light-weight YOLO models based on sharpening spatial attention","volume":"21","author":"Xue","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.engappai.2026.115046_b32","doi-asserted-by":"crossref","unstructured":"Yu, J., Jiang, Y., Wang, Z., Cao, Z., Huang, T., 2016. Unitbox: An advanced object detection network. In: Proceedings of the 24th ACM International Conference on Multimedia. pp. 516\u2013520.","DOI":"10.1145\/2964284.2967274"},{"key":"10.1016\/j.engappai.2026.115046_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2019.06.001","article-title":"Fruit detection for strawberry harvesting robot in non-structural environment based on mask-RCNN","volume":"163","author":"Yu","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.engappai.2026.115046_b34","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2022.795410","article-title":"An improved DeepLab v3+ deep learning network applied to the segmentation of grape leaf black rot spots","volume":"13","author":"Yuan","year":"2022","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.engappai.2026.115046_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105256","article-title":"Decision support systems for agriculture 4.0: Survey and challenges","volume":"170","author":"Zhai","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.engappai.2026.115046_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.107484","article-title":"Accurate cotton diseases and pests detection in complex background based on an improved YOLOX model","volume":"203","author":"Zhang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.engappai.2026.115046_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106644","article-title":"RIC-Net: A plant disease classification model based on the fusion of inception and residual structure and embedded attention mechanism","volume":"193","author":"Zhao","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.engappai.2026.115046_b38","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D., 2020. Distance-IoU loss: Faster and better learning for bounding box regression. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34, pp. 12993\u201313000.","DOI":"10.1609\/aaai.v34i07.6999"}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626013291?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626013291?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T07:37:48Z","timestamp":1783064268000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626013291"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":38,"alternative-id":["S0952197626013291"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115046","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Development and application of a new cotton aphid detection network under complex background","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115046","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"115046"}}