Deformable spatial pyramid pooling-enhanced EfficientNet with weighted feature fusion for pomegranate fruit disease diagnosis

Abstract

Pomegranate is a fruit of high nutritional and economic importance. Still, it is highly susceptible to different diseases during its growing stages, leading to significant yield losses and financial setbacks for farmers. This article proposes a novel disease detection model that integrates handcrafted features with deep features extracted using a developed deformable spatial pyramid pooling (DSPP)-EfficientNet architecture. Handcrafted features such as color (RGB and HSV histograms), texture features from gray level co occurrence matrix (GLCM), and shape attributes extracted from contour descriptors and Hu moments are captured and fused with deep features by weighted fusion strategy, resulted in the most discriminative information. The fused features are categorized using a support vector machine (SVM) in a classification phase, which effectively classifies different classes of pomegranate fruit diseases. The combined deep and handcrafted features obtained 96.66% accuracy, 96.26% precision, 96.50% recall, 96.37% F1 score, and 95.64% specificity on the pomegranate fruit disease dataset which compared to existing techniques.