Prethodno priopćenje

https://doi.org/10.31803/tg-20240919073402

Implementation of a Real-Time Maize Leaf Disease Detection System Using Raspberry Pi 5 and YOLOv8

Samuel Owoeye ; Department of Mechatronics Engineering, Federal University of Agriculture, Alabata Road, 110111 Abeokuta, Nigeria *
Folasade Durodola ; Department of Mechatronics Engineering, Federal University of Agriculture, Alabata Road, 110111 Abeokuta, Nigeria
Chibuzor Evwidonor ; Department of Mechatronics Engineering, Federal University of Agriculture, Alabata Road, 110111 Abeokuta, Nigeria
Sikirulahi Abdulkareem ; Department of Mechatronics Engineering, Federal University of Agriculture, Alabata Road, 110111 Abeokuta, Nigeria
Emmanuel Popoola ; Department of Mechatronics Engineering, Federal University of Agriculture, Alabata Road, 110111 Abeokuta, Nigeria
Idowu Akinode ; Department of Mechatronics Engineering, Federal University of Agriculture, Alabata Road, 110111 Abeokuta, Nigeria

* Dopisni autor.

Sažetak

Maize, a staple cereal crop globally, faces significant yield challenges due to foliar diseases. This study presents an innovative real-time maize leaf disease detection system integrating a YOLOv8 deep learning model with a custom-designed Unmanned Aerial Vehicle (UAV). The system targets three prevalent maize diseases: Grey leaf spot, common rust, and northern leaf blight. Trained on 10,239 images, the YOLOv8 model, comprising 168 layers and 3,006,233 parameters, achieves 8.1 GFLOPs with an inference speed of 6.3ms per image. Performance evaluation reveals a mean Average Precision (mAP@0.5) of 0.836 and mAP@0.5:0.95 of 0.681 across all classes, with high accuracy for common rust (Precision: 0.953, Recall: 0.979) and grey leaf spot (Precision: 0.919, Recall: 0.892). The custom UAV, designed for agricultural surveying, features a thrust-to-weight ratio of 5.85, ensuring stable flight with the 140g payload of a Raspberry Pi 5 and Camera Module V2. With a total weight of 605g and a 3S 5000mAh LiPo battery, the drone achieves an estimated flight time of 5.3 minutes, balancing survey coverage with real-time disease detection capabilities. The integration of this high-performance model with an efficient UAV platform represents a significant advancement in precision agriculture, enabling early disease intervention and targeted treatment strategies, thus promoting sustainable farming practices through optimized resource allocation and potential reduction in pesticide usage.

Ključne riječi

crop management; deep learning; disease detection; maize; real-time system; UAV; YOLOv8

Hrčak ID:

348868

URI

https://hrcak.srce.hr/348868

Datum izdavanja:

15.9.2026.

Posjeta: 0 *