A Disease and Pest Identification Method Based on Multi-feature Fusion

Abstract

Crop leaf diseases often differ by small lesion regions and similar color-texture patterns, which can limit the feature selectivity of compact CNN backbones when annotated images are insufficient. To address this problem, this study develops an improved ResNet18 for PlantVillage-based leaf disease recognition. The model combines three changes: an SE channel-recalibration unit for emphasizing lesion-related channels, Leaky ReLU for preserving gradient flow when activations are negative, and ImageNet-based transfer learning for faster adaptation to the target classes. Under the same training and evaluation protocol, the proposed network achieved 97.86% accuracy, with precision, recall, and F1-score of 97.63%, 97.41%, and 97.52%, respectively. These values exceeded the original ResNet18 and several commonly used CNN baselines. Ablation experiments showed stepwise gains from the three modifications, and Grad-CAM visualization indicated more concentrated responses over lesion regions. The results suggest that the modified ResNet18 is an effective compact baseline for crop leaf disease recognition, although validation under complex field conditions remains necessary.