PestReKNet-X: Integrating Explainable AI to enhance pest disease detection and combat crop senescence

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 framework that combines a ResNet18 backbone with a custom Kolmogorov Arnold Network (KAN) Linear layer (KANLinear), which captures complex non-linear patterns, surpassing the limits of traditional fully connected layers. The framework is evaluated on the benchmark 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 f..., , # PestReKNet-X: Integrating Explainable AI to enhance pest disease detection and combat crop senescence

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[](https://doi.org/10.5281/zenodo.16829681)

## 📌 Overview

PestReKNet-X is a novel deep learning framework for **crop pest and disease classification** that combines a **ResNet18 backbone** with a **custom Kolmogorov–Arnold Network (KAN) layer** for enhanced feature learning.

The framework integrates **Explainable AI (XAI)** techniques — **Grad-CAM** and **LIME** — alongside **Monte Carlo Dropout** for uncertainty estimation and **model calibration**.

### Key Features

* 🎯 **High Accuracy**: Achieves 95.09% testing accuracy
* 🔍 **Explainable AI**: Built-in...,