SiaRDFNet: Leveraging Siamese Architecture With ResNet‐DenseNet Fusion for Accurate Root Disease Classification

ABSTRACT Early identification of root disease in crops is a highly significant but challenging activity because the disease is likely to be manifested late and is also hidden in the ground. The traditional techniques of detection are slow and intrusive and lack the precision needed to act in a timely manner in precision agriculture. To overcome this, the proposed work presents a new deep learning framework called SiaRDFNet, which involves the addition of a Siamese network with a ResNet and DenseNet backbone to enhance the accuracy of root disease classification. The data set of this study was 244 high‐resolution root images (126 healthy and 118 diseased samples) that were augmented by the methods of rotation, flipping, cropping, and zooming to create diversity. The framework takes the form of wavelet‐based denoising, multi‐scale histogram equalization, and adaptive spatial filtering, and the root segmentation when using FusionSegNet++ as the framework that introduces CBAM attention to find the exact localization of features. The segmented roots are then fed into a Siamese ResNetDenseNet fusion backbone, which allows comparative learning to be done of the healthy and diseased samples. To achieve effective disease discrimination, we use joint embedding optimization consisting of contrastive and triplet loss functions that generate a disease‐sensitive and discriminating feature space. This special combination of segmentation, Siamese comparative learning, and dual embedding loss functions predetermines the originality of our approach. The model proposed showed an accuracy of 99.12%, precision of 98.02%, sensitivity of 99.16%, specificity of 99.46%, F1‐score of 99.22%, and MCC of 99.15%; therefore, demonstrating that it is an effective, scalable, accurate, noninvasive solution to early root disease detection in smart agriculture.