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Deep learning system for paddy plant disease detection and classification.

Amritha Haridasan¹, Jeena Thomas¹, Ebin Deni Raj²

¹Department of Computer Science and Engineering, Indian Institute of Information Technology, Kottayam, Kerala, India.

Environmental Monitoring and Assessment

|November 18, 2022

Summary

This summary is machine-generated.

This study introduces an automated system for detecting and classifying five major rice crop diseases using computer vision. The deep learning approach achieved high accuracy, aiding farmers in disease management and improving crop yield.

Keywords:

Computer vision Convolutional neural network Deep learning Image segmentation Machine learning Support vector machine

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Area of Science:

Agricultural Science
Computer Science
Plant Pathology

Background:

Rice crop diseases cause significant yield losses and economic impact globally.
Accurate and timely disease detection is crucial for effective crop management and food security.
Conventional disease diagnosis methods can be labor-intensive and subjective.

Purpose of the Study:

To develop an automated system for detecting and classifying five common rice crop diseases.
To leverage computer vision, machine learning, and deep learning for accurate disease identification.
To provide a foundation for predictive remedy recommendations to combat rice plant diseases.

Main Methods:

Utilized image processing techniques for pre-processing and segmentation of rice plant images.
Employed a hybrid approach integrating Support Vector Machine (SVM) classifier and Convolutional Neural Networks (CNNs).
Applied ReLU and softmax activation functions within the deep learning model.

Main Results:

The proposed deep learning-based strategy achieved a high validation accuracy of 0.9145.
Successfully identified and classified five primary rice diseases: bacterial leaf blight, false smut, brown leaf spot, rice blast, and sheath rot.
Demonstrated the potential of computer vision for automated disease analysis in agriculture.

Conclusions:

The automated system offers an efficient and accurate method for rice crop disease detection and classification.
This technology can significantly reduce resource wastage and improve treatment efficiency in farming.
The findings support the integration of AI in agriculture for enhanced crop health and yield.