A two-stage model for enhanced mango leaf disease detection using an innovative handcrafted spatial feature extraction method and knowledge distillation process

The economic stability of many countries is closely tied to agriculture, where crop quality and yield are heavily affected by weather conditions and disease control. Unpredictable climate patterns and plant diseases remain major challenges, emphasizing the need for more reliable disease detection methods. Traditionally, plant disease identification has relied on visual examination, a method that is often inaccurate. To address this, our study proposes a two-stage model for improved disease detection in mango leaves. In the first stage, we implement an innovative, block-based feature extraction technique using Local Directional Patterns (LDP) and Local Directional Pattern variance (LDPv) on a comprehensive dataset, MangoLeafBD, consisting of 4000 images, achieving satisfactory results in terms of detection accuracy, sensitivity, specificity, and false negative rate. In the second stage, we introduce a Knowledge Distillation (KD) process to further enhance model performance by transferring knowledge from a larger teacher model to a smaller student model. Our results demonstrate significant advancement, with the KD-enhanced model achieving an improvement in detection accuracy from 89.2% to 95.6%, sensitivity from 7.8% to 4.1%, and specificity from 71.2% to 88.9% for Anthracnose disease. Similar improvements were observed in detecting other diseases, making our approach a more robust and efficient solution for mango plant disease detection. • Novel two-stage model detects mango leaf disease with high accuracy and low cost. • Uses LDP and LDPv for fast, robust feature extraction with minimal resources. • Knowledge distillation boosts accuracy by training a compact student model. • Designed for resource-limited devices, avoiding heavy deep learning models. • Outperforms ML and DL baselines on MangoLeafBD across all performance metrics.