<![CDATA[This study develops a corn leaf disease detection system using the YOLOv11 algorithm to overcome the limitations of manual identification, which is often subjective and slow. The dataset from Roboflow was converted to the object detection format with four classes (Leaf Spot, Blight, Rust, Healthy), annotated with bounding boxes, split in a 70:20:10 proportion, and optimized through preprocessing and data augmentation. The model was trained for 150 epochs, yielding an average precision of 0.785, a recall of 0.662, and an mAP@0.5 of 0.717 from 80 test images. The Healthy class performed superiorly (mAP 0.988), while the Leaf Spot class was the lowest (mAP 0.471) due to the variation of complex lesions. The confusion matrix confirmed prediction consistency. The main advantage is the detection of specific disease locations via bounding boxes, complementing previous classification approaches. This system has the potential to support automatic diagnosis and effective precision agriculture management.]]>
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