Adaptive weight optimization with large pretraining for pest detection
Frequent infestations by agricultural pests reduce crop production and significantly affect economic efficiency. Therefore, timely and effective pest control is critical to improving productivity and facilitate environmental protection. Herein, we propose an adaptive weight optimization method based...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S157495412500233X |
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| _version_ | 1849233589442445312 |
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| author | Kejian Yu Wenwen Xu Fuqin Geng Yunzhi Wu |
| author_facet | Kejian Yu Wenwen Xu Fuqin Geng Yunzhi Wu |
| author_sort | Kejian Yu |
| collection | DOAJ |
| description | Frequent infestations by agricultural pests reduce crop production and significantly affect economic efficiency. Therefore, timely and effective pest control is critical to improving productivity and facilitate environmental protection. Herein, we propose an adaptive weight optimization method based on transfer learning for multimodal pest detection. This approach utilizes pretrained model parameters from public datasets to extract features and enhance cross-modal feature from text and images. Accurate pest recognition and localization are achieved through an adaptive loss function, which optimizes the model’s performance across multiple tasks. In tests conducted on IP102 (36 species) and Pest24 (24 species), which are major agricultural pest datasets, the proposed model achieves average precisions of 65.8% and 76.3% at 50% Intersection over Union (IoU) threshold, respectively. By doing so, our model outperforms existing state-of-the-art methods despite using only 30 training cycles. These results highlight the significant practical value of multimodal pest detection methods in enhancing the efficiency and accuracy of agricultural pest identification. The code and dataset are available at https://github.com/Healer-ML/Pest-Detection. |
| format | Article |
| id | doaj-art-098b75964fcd4ff3b3bfd8f70a09b26c |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-098b75964fcd4ff3b3bfd8f70a09b26c2025-08-20T05:05:10ZengElsevierEcological Informatics1574-95412025-12-019010322410.1016/j.ecoinf.2025.103224Adaptive weight optimization with large pretraining for pest detectionKejian Yu0Wenwen Xu1Fuqin Geng2Yunzhi Wu3School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China; Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei 230036, ChinaSchool of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China; Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei 230036, ChinaSchool of Foreign Languages, Hefei University of Technology, Hefei 230009, ChinaSchool of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China; Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei 230036, China; Corresponding author at: Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei 230036, China.Frequent infestations by agricultural pests reduce crop production and significantly affect economic efficiency. Therefore, timely and effective pest control is critical to improving productivity and facilitate environmental protection. Herein, we propose an adaptive weight optimization method based on transfer learning for multimodal pest detection. This approach utilizes pretrained model parameters from public datasets to extract features and enhance cross-modal feature from text and images. Accurate pest recognition and localization are achieved through an adaptive loss function, which optimizes the model’s performance across multiple tasks. In tests conducted on IP102 (36 species) and Pest24 (24 species), which are major agricultural pest datasets, the proposed model achieves average precisions of 65.8% and 76.3% at 50% Intersection over Union (IoU) threshold, respectively. By doing so, our model outperforms existing state-of-the-art methods despite using only 30 training cycles. These results highlight the significant practical value of multimodal pest detection methods in enhancing the efficiency and accuracy of agricultural pest identification. The code and dataset are available at https://github.com/Healer-ML/Pest-Detection.http://www.sciencedirect.com/science/article/pii/S157495412500233XPest detectionMulti-modalTransfer learning |
| spellingShingle | Kejian Yu Wenwen Xu Fuqin Geng Yunzhi Wu Adaptive weight optimization with large pretraining for pest detection Ecological Informatics Pest detection Multi-modal Transfer learning |
| title | Adaptive weight optimization with large pretraining for pest detection |
| title_full | Adaptive weight optimization with large pretraining for pest detection |
| title_fullStr | Adaptive weight optimization with large pretraining for pest detection |
| title_full_unstemmed | Adaptive weight optimization with large pretraining for pest detection |
| title_short | Adaptive weight optimization with large pretraining for pest detection |
| title_sort | adaptive weight optimization with large pretraining for pest detection |
| topic | Pest detection Multi-modal Transfer learning |
| url | http://www.sciencedirect.com/science/article/pii/S157495412500233X |
| work_keys_str_mv | AT kejianyu adaptiveweightoptimizationwithlargepretrainingforpestdetection AT wenwenxu adaptiveweightoptimizationwithlargepretrainingforpestdetection AT fuqingeng adaptiveweightoptimizationwithlargepretrainingforpestdetection AT yunzhiwu adaptiveweightoptimizationwithlargepretrainingforpestdetection |