Robust Leaf Disease Classification via Deep Feature Concatenation and EfficientNetV

Plants play a vital role in global food security, ecological balance, and economic sustainability. However, their susceptibility to a wide range of leaf diseases poses a major threat to crop yield and quality. Early and accurate identification of these diseases is essential for effective management. Manual inspection methods, though widely used, are inefficient, subjective, and unsuitable for large-scale agricultural operations. To address this challenge, we propose a deep learning-based classification system that significantly improves disease recognition performance. The model is designed to classify leaf images from six plant species—including both healthy and diseased samples—across 14 distinct categories. Our architecture leverages EfficientNetV2B0 as the feature extractor, enhanced with custom dense layers and feature concatenation to capture subtle visual cues. Through preprocessing and balanced dataset splitting, combined with training techniques such as dropout, adaptive learning rate scheduling, and early stopping, the model achieves robust generalization. Experimental evaluation shows that our approach improves classification accuracy from a baseline of 90.18% to 92.86%.