Abstract:
Leaf galls are abnormal plant growths resulting from insect parasitism, which can cause
major agricultural losses if not managed in a timely manner. Precise early detection of leaf
galls is crucial for enabling targeted treatment of affected plants and precision crop
management. However, manual monitoring and identification of leaf galls across large
cultivated areas can be extremely labor-intensive, slow and error-prone. This necessitates
the development of automated computer vision techniques using deep learning to
accurately detect leaf galls at scale for crop health monitoring. This work develops and
systematically compares two state-of-the-art convolutional neural network architectures -
YOLOv8 and YOLOv5 for automated detection of leaf galls on plumeria leaves. A dataset
of 489 high resolution images of plumeria leaves exhibiting leaf galls of various shapes,
sizes, textures and colors was collected through extensive field surveys. Each image was
annotated by an experienced researcher using bounding boxes demarcating each gall
instance. 73% of the images were utilized for training, 12% for validation, while the
remaining 15% were held-out for testing model performance. Both YOLOv8 and YOLOv5
models were optimized by tuning key hyperparameters and leveraging data augmentation
techniques to minimize overfitting. On the 142-image test set, YOLOv8 achieved a higher
mean Average Precision (mAP) of 92.1%, compared to 89.1% attained by YOLOv5,
demonstrating YOLOv8's superior accuracy. YOLOv8 also attained higher precision of
90.3% and recall of 88.3%, versus 89.4% and 84.8% for YOLOv5, indicating improved
classification and localization capabilities. However, YOLOv5 exhibited slightly faster
inference time versus YOLOv8. Overall, this rigorous comparative evaluation highlights
YOLOv8 as a more robust and accurate solution for automated leaf gall detection, while
YOLOv5 may be more suitable for real-time analysis. The findings provide meaningful
insights on deep learning advancements for agriculture applications.
