A High-Precision Approach of Coconut Tree Disease Detection using Enhanced Yolov9

Accurate detection and categorisation of coconut tree diseases are vital for enhancing agricultural output and sustaining farming practices.This study presents an advanced framework combining Enhanced YOLOv9 with EfficientNet for robust disease feature extraction.Our approach employs pseudo-labelling via Region Proposal Network (RPN) during the training phase to generate approximate bounding boxes for unannotated images.The EfficientNet backbone improves inter-class distinction and intra-class variability handling.During inference, the anchor-free detection head directly predicts disease region centers and dimensions without anchor priors, while Feature Pyramid Network (FPN) enables multi-scale feature aggregation for comprehensive detection of lesions ranging from small spots to large infected areas.Advanced data augmentation and a hybrid loss function enhance model generalization and detection capabilities.Experimental results demonstrate that our model achieves 97.34% accuracy, 99.8% precision, 94.67% recall, and a 97.17% F1 score, outperforming state-of-the-art architectures.This work presents an effective tool for fieldbased disease detection and classification, supporting timely interventions in coconut cultivation.