Detection of plant root diseases using computational intelligence and advanced image processing techniques

This study presents an advanced Siamese-based Squeeze Googlenet and LeNet-5 (SSGL) model for the early detection of root diseases in plants, which is crucial for agricultural productivity and crop management. The methodology integrates state-of-the-art image processing techniques and Deep Learning (DL) algorithms across several key stages. In the pre-processing phase, techniques like median filtering, histogram equalization, i-CLAHE, and Z-score normalization are applied to enhance image quality and prepare the data for segmentation. The segmentation process utilizes a modified DeepLab V3 model, renowned for its precision in isolating root structures from complex backgrounds, ensuring accurate subsequent analysis. Feature extraction focuses on deriving comprehensive characteristics from segmented root images, including texture features using GLCM, color histograms, geometric metrics like area and perimeter, and shape descriptors such as circularity and rectangularity. A hybrid optimization approach employs the Hybrid Pufferfish-based Coati Optimization (HPCO) algorithm for feature selection, effectively identifying the most relevant features crucial for disease detection. For detection, the SSGL model integrates optimized architectures from SqueezeNet, GoogleNet, and LeNet-5 within a Siamese network framework. This setup enables efficient classification of root images into healthy and diseased categories based on cosine similarity. The proposed SSGL model outperforms other models with the highest accuracy (0.9856), precision (0.9760), sensitivity (0.9764), and F-measure (0.9771), while also achieving the lowest FPR (0.0149) and FNR (0.0109).