Improved plant leaf disease detection architecture using unmanned aerial vehicle images and hybrid YOLOv11

Abstract

Plant diseases affect crop quality and quantity, which significantly reduces crop production. Grapes are a popular fruit, and pumpkin is a widely consumed vegetable in the world. Early detection of grape and pumpkin leaf disease is crucial to avoiding large losses in crop quality and yield. Even though the use of unmanned aerial vehicle (UAV) remote sensing can assist in reaching a broader scale for plant leaf disease identification, the work of detection is significantly hampered by the overlapping leaves, and blurring of UAV images. This paper suggests combining object detection techniques with Real-World Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN) for enhancing image quality to detect grape and pumpkin leaf diseases in UAV-captured images. The Atrous Spatial Pyramid Pooling (ASPP) technique is incorporated into YOLOv11 architecture to enhance the network’s feature representation capabilities by efficiently incorporating depth-wise separable convolutions and dilated convolutions with different rates. The proposed approach has shown remarkable effectiveness. In the validation, the ASPP-YOLOv11 outperforms the baseline YOLOv11 architecture in precision achieving 84% with 5.2% increase, 94.7% with 3.3% increase in mAP50, and 74% with 0.9% increase in mAP50-95. Additionally, in the testing set the ASPP architecture significantly enhances mAP50 to 91.6%, attaining 1.6% gain and obtaining 1.6% increase in mAP50-95.

Plant phenotyping relevance

UAV画像から植物葉の病害を推定する画像解析アーキテクチャを開発し、既存モデルと性能比較しており、植物病害状態の取得方法が中心である。

Code and data availability

公開論文であることは確認できましたが、現在の公式API・許可済み取得経路では本文を自動取得できませんでした。

No evidence-backed public reproduction asset is currently recorded.