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DOI:
https://doi.org/10.15662/IJEETR.2026.0802388Keywords:
Plant Disease Detection, Deep Learning, YOLOv11, Object Detection, Classification, Precision Agriculture.Abstract
Indeed, precise and timely detection of diseases in plants is one of the most vital elements in maximizing crops to uphold global food security. Traditional methods are often time-consuming, subjective, and sometimes require expert knowledge. This project involves the use of a Deep Learning framework for automatic Field Plant Disease Detection and Classification, making use of the advanced YOLOv11 object detection model. Namely, YOLOv11 is utilized because of its superior balance of detection speed and accuracy in comparison with earlier models. This makes it ideal for real-time applications on the field. The plant image dataset is proposed to be collected, preprocessed, and annotated, involving different crops and common diseases in a large dataset.
The YOLOv11 architecture is trained to simultaneously locate the disease regions (bounding boxes) on leaves, stems, or fruits and classify the specific type of disease. This may involve techniques for improving model robustness, such as data augmentation and transfer learning, which should enable better generalization across diverse environmental conditions. Such performances will then be checked using metrics such as Mean Average Precision and the speed of inferences: Frames Per Second.
The system will be reliable, efficient, and scalable for farmers and agricultural experts. Implementation challenges include model size optimization for mobile deployment and continuous retraining on new disease strains, but the integration of YOLOv11 has great potential to revolutionize precision agriculture and smart farming by providing the ability for instantaneous disease management.
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