An Efficient Disease Prediction in Smart Agriculture Using Advanced Deep Learning Methods for Improving Crop Productivity

Abstract

Plant diseases are considered one of the most serious problems in world agricultural production. Regular monitoring and detection are essential to control plant diseases, and effective management methods are used to prevent disease spread and lower pesticide costs. Smart agriculture techniques are one of the key solutions in plant disease prediction and improving crop productivity. Even though various papers have been published on the model for plant disease prediction based on smart agriculture, there is still a lack of an overall systematic model. The proposed approach has been developed to overcome the challenges faced by the existing method. This presented approach uses deep learning and meta-heuristic techniques to detect and classify crop diseases, providing an accurate and efficient solution for farmers to improve crop yield. The process begins with collecting crop disease images from the Kaggle database. Initially, noise removal and contrast enhancement are performed using a Gaussian Amended Wiener Filter (GAWF). Next, the Modified Residual U-Net (MRU-Net) model extracts significant disease regions from the images. Effective features are collected from these segments using a convolutional neural network (CNN) and an improved vision transformer model (IViT). Finally, classification is performed with a stacking ensemble model that incorporates XGBoost (XGB), Gradient Boosting (GB) and AdaBoost-Decision Tree (AdB-DT). The proposed model achieved an accuracy of 99.74% on the PlantVillage dataset, 99.51% on the PlantDoc dataset and 99.57% on the Pigeonpea Leaf Disease dataset, demonstrating its robustness and generalizability across both curated and real-world agricultural image conditions. Also, the proposed approach provided insights into disease identification by utilising Grad-CAM to provide visual explanations.