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LeafAI: Interpretable plant disease detection for edge computing.

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This summary is machine-generated.

This study introduces a hybrid AI model for efficient plant disease detection. It significantly speeds up analysis by first identifying healthy leaves, reducing computational load and resource use in agriculture.

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Area of Science:

  • Agricultural AI
  • Computer Vision
  • Machine Learning

Background:

  • Real-world agriculture exhibits significant class imbalance, with healthy plant leaves outnumbering diseased ones.
  • This imbalance poses challenges for computationally intensive deep learning models in automated plant disease detection, leading to inefficiency.
  • Sustainable AI solutions are needed to optimize resource consumption and improve detection accuracy.

Purpose of the Study:

  • To present an iterative, hybrid AI approach for enhanced computational efficiency, interpretability, and scalability in real-time plant disease detection.
  • To address the challenges of class imbalance and high resource consumption in automated plant disease identification.
  • To develop a trustworthy and sustainable AI solution for precision agriculture.

Main Methods:

  • A two-stage hybrid system: a lightweight traditional classifier for initial healthy leaf exclusion, followed by deep learning models (ResNet, DenseNet, MobileNet, EfficientNet) for diseased leaf classification.
  • Implementation of Explainable AI (XAI) methods, specifically Gradient-weighted Class Activation Mapping (Grad-CAM), to generate predictive heatmaps.
  • Evaluation of a hybrid model combining Logistic Regression and Mobilenetv3 for performance and efficiency.

Main Results:

  • The hybrid model achieved up to 77.6% faster inference compared to conventional deep learning models, with a minimal accuracy loss of approximately 3%.
  • In a large-scale test (1,227 images), the hybrid model reduced total inference time from 4,548 seconds to 1,010.13 seconds on an entry-level laptop, with minimal CPU load.
  • Grad-CAM heatmaps provided transparency by highlighting image regions critical for model predictions, aiding feature refinement.

Conclusions:

  • The proposed hybrid AI approach offers a scalable, sustainable, and trustworthy solution for plant disease detection in precision agriculture.
  • This method effectively addresses class imbalance and optimizes inference efficiency, making AI solutions more practical for real-world agricultural applications.
  • The integration of XAI enhances model interpretability and trust, crucial for the adoption of AI in agricultural practices.