Empowering Smallholder Farmers with UAV-Based Early Cotton Disease Detection Using AI
Early detection of cotton diseases is critical for safeguarding crop yield and minimizing agrochemical usage. However, most state-of-the-art systems rely on multispectral or hyperspectral sensors, which are costly and inaccessible to smallholder farmers. This paper introduces CottoNet, a lightweight...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/2504-446X/9/5/385 |
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| author | Halimjon Khujamatov Shakhnoza Muksimova Mirjamol Abdullaev Jinsoo Cho Cheolwon Lee Heung-Seok Jeon |
| author_facet | Halimjon Khujamatov Shakhnoza Muksimova Mirjamol Abdullaev Jinsoo Cho Cheolwon Lee Heung-Seok Jeon |
| author_sort | Halimjon Khujamatov |
| collection | DOAJ |
| description | Early detection of cotton diseases is critical for safeguarding crop yield and minimizing agrochemical usage. However, most state-of-the-art systems rely on multispectral or hyperspectral sensors, which are costly and inaccessible to smallholder farmers. This paper introduces CottoNet, a lightweight and efficient deep learning framework for detecting early-stage cotton diseases using only RGB images captured by unmanned aerial vehicles (UAVs). The proposed model integrates an EfficientNetV2-S backbone with a Dual-Attention Feature Pyramid Network (DA-FPN) and a novel Early Symptom Emphasis Module (ESEM) to enhance sensitivity to subtle visual cues such as chlorosis, minor lesions, and texture irregularities. A custom-labeled dataset was collected from cotton fields in Uzbekistan to evaluate the model under realistic agricultural conditions. CottoNet achieved a mean average precision (mAP@50) of 89.7%, an F1 score of 88.2%, and an early detection accuracy (EDA) of 91.5%, outperforming existing lightweight models while maintaining real-time inference speed on embedded devices. The results demonstrate that CottoNet offers a scalable, accurate, and field-ready solution for precision agriculture in resource-limited settings. |
| format | Article |
| id | doaj-art-56044ea9c5b945dab4549abc7a5144e2 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Drones |
| spelling | doaj-art-56044ea9c5b945dab4549abc7a5144e22025-08-20T03:47:49ZengMDPI AGDrones2504-446X2025-05-019538510.3390/drones9050385Empowering Smallholder Farmers with UAV-Based Early Cotton Disease Detection Using AIHalimjon Khujamatov0Shakhnoza Muksimova1Mirjamol Abdullaev2Jinsoo Cho3Cheolwon Lee4Heung-Seok Jeon5Department of Computer Engineering, Gachon University, Seognam-daero, Sujeong-gu, Seongnam-si 1342, Republic of KoreaDepartment of Computer Engineering, Gachon University, Seognam-daero, Sujeong-gu, Seongnam-si 1342, Republic of KoreaDepartment of Information Systems and Technologies, Tashkent State University of Economics, Tashkent 100066, UzbekistanDepartment of Computer Engineering, Gachon University, Seognam-daero, Sujeong-gu, Seongnam-si 1342, Republic of KoreaDepartment of Computer Engineering, Konkuk University, Chungju 05029, Republic of KoreaDepartment of Computer Engineering, Konkuk University, Chungju 05029, Republic of KoreaEarly detection of cotton diseases is critical for safeguarding crop yield and minimizing agrochemical usage. However, most state-of-the-art systems rely on multispectral or hyperspectral sensors, which are costly and inaccessible to smallholder farmers. This paper introduces CottoNet, a lightweight and efficient deep learning framework for detecting early-stage cotton diseases using only RGB images captured by unmanned aerial vehicles (UAVs). The proposed model integrates an EfficientNetV2-S backbone with a Dual-Attention Feature Pyramid Network (DA-FPN) and a novel Early Symptom Emphasis Module (ESEM) to enhance sensitivity to subtle visual cues such as chlorosis, minor lesions, and texture irregularities. A custom-labeled dataset was collected from cotton fields in Uzbekistan to evaluate the model under realistic agricultural conditions. CottoNet achieved a mean average precision (mAP@50) of 89.7%, an F1 score of 88.2%, and an early detection accuracy (EDA) of 91.5%, outperforming existing lightweight models while maintaining real-time inference speed on embedded devices. The results demonstrate that CottoNet offers a scalable, accurate, and field-ready solution for precision agriculture in resource-limited settings.https://www.mdpi.com/2504-446X/9/5/385UAV-based cotton disease detectionchannel attention moduleembedded devicesdual-attention feature pyramid network |
| spellingShingle | Halimjon Khujamatov Shakhnoza Muksimova Mirjamol Abdullaev Jinsoo Cho Cheolwon Lee Heung-Seok Jeon Empowering Smallholder Farmers with UAV-Based Early Cotton Disease Detection Using AI Drones UAV-based cotton disease detection channel attention module embedded devices dual-attention feature pyramid network |
| title | Empowering Smallholder Farmers with UAV-Based Early Cotton Disease Detection Using AI |
| title_full | Empowering Smallholder Farmers with UAV-Based Early Cotton Disease Detection Using AI |
| title_fullStr | Empowering Smallholder Farmers with UAV-Based Early Cotton Disease Detection Using AI |
| title_full_unstemmed | Empowering Smallholder Farmers with UAV-Based Early Cotton Disease Detection Using AI |
| title_short | Empowering Smallholder Farmers with UAV-Based Early Cotton Disease Detection Using AI |
| title_sort | empowering smallholder farmers with uav based early cotton disease detection using ai |
| topic | UAV-based cotton disease detection channel attention module embedded devices dual-attention feature pyramid network |
| url | https://www.mdpi.com/2504-446X/9/5/385 |
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