PestReKNet-X: integrating explainable artificial intelligence to enhance pest disease detection and combat crop senescence

Abstract Crop pests and diseases remain a significant obstacle to sustainable agriculture, necessitating innovative and eco-friendly detection solutions. This study introduces PestReKNet-X, a state-of-the-art explainable deep learning (DL) framework that combines a ResNet18 backbone with a custom Kolmogorov–Arnold network linear (KANLinear) layer which captures complex nonlinear patterns, surpassing the limits of traditional fully connected (FC) layers. The framework is evaluated on the benchmark cashew, cassava, maize and tomato (CCMT) crop pest and disease dataset, containing 102 097 images across 22 classes. To tackle class imbalance, the mean Intersection over Union (IoU) metric is used alongside accuracy for robust performance evaluation. The model achieves a testing accuracy of 95.09% and a mean IoU of 0.9133, reflecting strong generalization across diverse categories. Moreover, PestReKNet-X surpasses advanced architectures like Swin Transformer and MobileNetV3Large concerning performance and reliability. Monte Carlo dropout for uncertainty estimation and model calibration are used to achieve reliable predictions with well-calibrated probabilities. A strong focus on explainable artificial intelligence (XAI) ensures transparency and interpretability, addressing gaps in recent studies. The inclusion of Grad-CAM and LIME provides intuitive visualizations and localized insights, enhancing understanding of predictions. With high accuracy, efficiency and interpretability, PestReKNet-X provides a scalable solution for precise pest and disease monitoring.