A Hybrid Deep Multistacking Integrated Model for Plant Disease Detection

Plant disease detection is a critical challenge in agriculture, as undetected or poorly managed diseases can lead to significant yield losses, economic setbacks for farmers, and compromised food security. Therefore, accurate and efficient models for timely identification and mitigation are imperative to address these challenges and ensure sustainable agricultural practices. In this study, we introduce a deep multistacking integrated model for plant leaf disease detection that leverages fine-tuned transfer learning (TL) models, multistacking feature generation, and an ensemble XGBoost meta-classifier. Our approach involves specialized pipelines for image preprocessing, augmentation, and fine-tuning TL models, resulting in a robust hybrid model. The multistacking feature generation process aggregates the prediction probabilities from fine-tuned models, which are then used as input for the XGBoost classifier, enhancing both accuracy and efficiency. We evaluate the proposed model on three benchmark datasets: Tomato Disease Dataset (TDDS), Potato Pepper Dataset (PPDS), and Apple Grape Dataset (AGDS). Our experimental results demonstrate that the multistacking integrated model significantly outperforms traditional single-model approaches, achieving high accuracy scores of 99.78%, 99.86%, and 99.82% on TDDS, PPDS, and AGDS, respectively. These results underline the effectiveness of our approach, particularly in multilevel classification, where integrating features from multiple fine-tuned models allows for better generalization and higher precision. By leveraging multistacking techniques and an ensemble XGBoost classifier, we have advanced the state-of-the-art in plant disease detection. Moreover, the model shows superior computational efficiency, with lower build and prediction times compared to existing methods. These findings indicate that the integration of advanced ensemble techniques with fine-tuned models can enhance the generalization and robustness of plant disease detection systems. Our work provides a scalable and effective solution for improving plant disease detection, contributing to the broader application of deep learning (DL) in precision agriculture.