A Robust Ensemble Deep Learning Approach for Early and Accurate Tomato Leaf Disease Identification
Keywords:
Disease classification, Agricultural practices, Yield loss, Deep learning, Interpretability and GCAM.Abstract
Farmers and stakeholders stand to significantly minimize potential yield losses from disease outbreaks through the efficient and prompt identification of tomato diseases using easily accessible leaf images. This research introduces an inventive solution to this challenge by presenting a new method capable of visually distinguishing nine distinct infectious tomato leaf diseases from healthy leaves. These include blights, mold on leaf, bacterial spot, Septoria spot, mosaic virus, target two-spotted spider, and the virus like yellow leaf curl. To achieve this, the research employs an ensemble learning approach that combines the strengths of EfficientNetB5, DenseNet169, and VIT architectures. The method is evaluated using a comprehensive tomato leaf disease (TLD) dataset and yields impressive results. During training, it achieves an average accuracy of 99.6% with minimal deviation, and validation accuracy averages at 98.3%. Cross-validation tests demonstrate an average test accuracy of 99.1%, further emphasizing the model's reliability and consistency. In addition to accuracy, the research prioritizes model interpretability, utilizing gradient-weight based classified activation maps (GCAM) and global interpretable method-agnostic explanations. This transparency not only enhances predictive accuracy but also instills trust and facilitates the model's integration into agricultural processes. The ensemble learning model, combining transfer learning and efficient network architectures, emerges as a leading solution, boasting remarkable performance in terms of accuracy during training and testing. This research provides agricultural professionals with a practical and efficient methodology for early plant disease diagnosis, contributing significantly to disease outbreak prevention and economic loss mitigation.
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