Plant Disease Detection Using Hybrid MobileNetV2- Compact CNN Architecture with LIME Integration

Abstract

This paper presents an advanced approach to plant disease detection by implementing explainable AI techniques that combine MobileNetV2 architecture with transfer learning and compact convolutional neural networks (CNN). The study compares three distinct models' performance on a plant leaf disease dataset, revealing MobileNetV2's superior accuracy of 95% with 94% precision in disease classification, despite requiring 850 seconds for training. The Compact CNN achieved 82% accuracy with minimal training time of 420 seconds, demonstrating its efficiency for resource-constrained applications. Disease-specific analysis showed exceptional detection rates for common plant diseases, with Apple Scab at 96.5%, Black Rot at 94.8%, and Cedar Rust at 95.2%. The integration of LIME (Local Interpretable Model-agnostic Explanations) provided transparent insights into the model's decision-making process, while the Compact CNN demonstrated 45% reduced memory usage compared to MobileNetV2. This implementation establishes a robust framework for practical agricultural applications, balancing high accuracy with computational efficiency and interpretability.

Code and data availability

The paper uses the public PlantVillage dataset but provides no paper-specific public assets: no author code/scripts, trained model checkpoints, or supplementary data deposits are mentioned, and no availability statements or URLs are given. PlantVillage is a pre-existing third-party dataset, not an authors' paper-phenot

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