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Updated: Jun 10, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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Resource-optimized cnns for real-time rice disease detection with ARM cortex-M microprocessors.

Hermawan Nugroho1, Jing Xan Chew2, Sivaraman Eswaran3

  • 1Electrical and Electronic Engineering Department, University of Nottingham Malaysia, Jln Broga, 43500, Semenyih, Malaysia. hermawan.nugroho@nottingham.edu.my.

Plant Methods
|October 16, 2024
PubMed
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This summary is machine-generated.

This study uses Artificial Intelligence (AI) and Convolutional Neural Networks (CNNs) on microcontrollers for rice plant disease detection. MobileNetV2 achieved 97.5% accuracy, offering a viable smart agriculture solution.

Area of Science:

  • Agricultural Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Rice is a crucial staple food, facing declining self-sufficiency in regions like Malaysia.
  • Effective rice plant disease detection is vital for enhancing agricultural productivity and sustainability.
  • Resource-constrained environments necessitate efficient AI models for on-site agricultural monitoring.

Purpose of the Study:

  • To explore the application of Convolutional Neural Networks (CNNs) on ARM Cortex-M microprocessors for detecting rice plant diseases.
  • To evaluate the performance and computational efficiency of MobileNetV2 and FD-MobileNet models for this task.
  • To investigate resource optimization strategies for AI models in smart agriculture applications.

Main Methods:

  • Utilized two large datasets (5,932 and 10,407 images) covering multiple rice disease classes for training and validation.
Keywords:
ARM Cortex-M microprocessorsAgricultural productivityConvolutional neural networksRice disease detection

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  • Implemented and optimized MobileNetV2 and FD-MobileNet models for the ARM Cortex-M4 microprocessor.
  • Evaluated model performance based on accuracy, computational efficiency, and resource consumption (RAM, flash memory).

    • Main Results:

    • MobileNetV2 achieved a high accuracy of 97.5%, outperforming FD-MobileNet (90% accuracy).
    • MobileNetV2 demonstrated superior performance in detecting complex diseases like tungro (93% accuracy).
    • Resource optimization, even minor, significantly improved validation accuracy, highlighting the trade-off between efficiency and performance.

    Conclusions:

    • Deploying CNNs on microcontrollers offers a practical solution for real-time, on-site rice plant disease detection.
    • The study demonstrates the potential for improved accuracy and operational efficiency in smart agriculture.
    • This research contributes to addressing food security challenges through the integration of AI in agriculture.