AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data

• Published in: Plants (Basel) Vol. 13; no. 5; p. 653
• Main Authors: Khan, Asma, Malebary, Sharaf J, Dang, L Minh, Binzagr, Faisal, Song, Hyoung-Kyu, Moon, Hyeonjoon
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 27.02.2024; MDPI
• Subjects: Accuracy; Agricultural ecosystems; Agricultural pests; Agricultural production; Agricultural research; Agriculture; Artificial intelligence; Classification; computer vision; Control; convolution neural network; Crop diseases; Crop management; Crops; Datasets; Deep learning; Diseases and pests; Feature extraction; Insects; Machine learning; Management; Methods; Model testing; Modules; monitoring system; Neural networks; Object recognition; Pest control; Pests; Plant diseases; Sustainable agriculture; Taxonomy; UAV technology; Unmanned aerial vehicles; South Korea; deep learning; monitoring system; UAV technology; sustainable agriculture; computer vision; convolution neural network
• ISSN: 2223-7747; 2223-7747
• DOI: 10.3390/plants13050653

Our research focuses on addressing the challenge of crop diseases and pest infestations in agriculture by utilizing UAV technology for improved crop monitoring through unmanned aerial vehicles (UAVs) and enhancing the detection and classification of agricultural pests. Traditional approaches often require arduous manual feature extraction or computationally demanding deep learning (DL) techniques. To address this, we introduce an optimized model tailored specifically for UAV-based applications. Our alterations to the YOLOv5s model, which include advanced attention modules, expanded cross-stage partial network (CSP) modules, and refined multiscale feature extraction mechanisms, enable precise pest detection and classification. Inspired by the efficiency and versatility of UAVs, our study strives to revolutionize pest management in sustainable agriculture while also detecting and preventing crop diseases. We conducted rigorous testing on a medium-scale dataset, identifying five agricultural pests, namely ants, grasshoppers, palm weevils, shield bugs, and wasps. Our comprehensive experimental analysis showcases superior performance compared to various YOLOv5 model versions. The proposed model obtained higher performance, with an average precision of 96.0%, an average recall of 93.0%, and a mean average precision (mAP) of 95.0%. Furthermore, the inherent capabilities of UAVs, combined with the YOLOv5s model tested here, could offer a reliable solution for real-time pest detection, demonstrating significant potential to optimize and improve agricultural production within a drone-centric ecosystem.