Explainable AI for Precision Agriculture: Fine-Grained Plant Pathology Localization Using ConvNeXt-Tiny and Residual Spatial Attention Module Palvi Sharma

Multi-crop plant disease automatic classification in precision agriculture requires an effective solution but the state-of-the-art deep learning architectures are prone to suffering background shortcut problem, higher inference latency, and inadequate visualization interpretability. This paper presents a better performing framework combining ConvNeXt-Tiny with an innovative Residual Spatial Attention Module (RSAM) to solve the fine-grained diagnosis problem for 38 plant pathology targets. On a dataset of 10,876 unseen images, the proposed framework demonstrates a Top-1 classification accuracy of 96.85%, a precision of 96.95%, a recall of 96.48%, and a macro F1-score of 96.71% while being superior to ResNet- 50 (+2.20%) and being 0.97 ms faster per image (7.15 ms/img with 28.12 M parameters). Explainable AI (XAI) analysis with the help of Grad-CAM shows the ability of the RSAM block to suppress background and soil artifacts, directing 88.42% of activation energy 𝐸𝑙𝑒𝑠𝑖𝑜𝑛