Multi-Model Machine Learning for Automated Identification of Rice Diseases Using Leaf Image Data

Abstract

Purpose Rice is grown almost everywhere in the world but is notably prevalent in Asian nations where it serves as the main food source for nearly half of the world’s population. Yet, enduring agricultural problems like various rice diseases have been a problem for farmers and planting specialists for ages. A fast, efficient, less expensive, and reliable approach to detecting rice diseases is urgently required in agricultural information since severe rice infections could result in no harvest of grains. Automated disease monitoring of rice plants using leaf images is critical for transitioning from labor-intensive, experience-based decision-making to an automated, data-driven strategy in agricultural production. In the modern era, Artificial Intelligence (AI) is being widely investigated in various areas of the medical and plant sciences to assess and diagnose the types of diseases. Methods This work proposes a hybrid deep-machine learning system for the automated detection of rice plant diseases using a leaf image dataset. Benchmarked MobileNetV2 architecture is employed to extract the deep features from the input images. Obtained features are fed as input to various machine learning classifiers with different kernel functions using a 10-fold validation strategy. Results The developed hybrid system attained the highest classification accuracy of 98.6%, specificity of 98.85%, and sensitivity of 97.25% using a medium neural network. The results demonstrate that the established system is computationally faster and more efficient. The proposed system is ready for testing with more databases. Conclusions The suggested technology accurately diagnoses various rice plant illnesses, reducing manual labor and allowing farmers to receive prompt treatment. Future research topics include incorporating cloud-based monitoring for leaf image capture in non-connected farms, as well as building mobile IoT platforms for continuous screening.

Plant phenotyping relevance

イネ葉画像から病害状態を自動推定する画像・機械学習手法の開発と検証が研究の中心であり、植物病害表現型の定量的判定に該当する。

Code and data availability

The paper describes a hybrid CNN-ML rice disease classification system using leaf images aggregated from UCI, Kaggle, and IEEE sources, but no block contains an explicit data or code availability statement, no authors' public repository URL, and no deposited dataset, code, or trained model is identified. The only asset

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