Fusion of Deep Learned and Handcrafted Features for Paddy Disease Recognition

Rice is a fundamental food grain worldwide, playing a vital role in both agriculture and public health. However, rice leaf diseases significantly threaten cultivation, affecting farmers globally. Early identification and effective management of these diseases are critical for ensuring healthy rice crops and sufficient food supply for the growing population. Traditional manual diagnosis of paddy diseases remains prevalent but is often inefficient, time-consuming, and susceptible to errors. To address this, our study introduces a novel end-to-end framework for accurately diagnosing paddy diseases through advanced image analysis of paddy leaves. This approach combines deep learning and handcrafted feature extraction techniques. The InceptionResNetV2 pre-trained network is employed to extract deep features from each image, while the Local Neighborhood Encoded Pattern (LNEP) captures texture features. These combined features are then used to identify discriminative patterns, which are fed into a multi-scale 1D Convolutional Neural Network (CNN) classifier. Extensive investigations performed on the Paddy Doctor dataset reveal that the proposed method exhibits promising performance in comparison to state-of-the-art methods.