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Volume 13 | Issue 10 | Year 2026 | Article Id. IJEEE-V13I10P113 | DOI : https://doi.org/10.14445/23488379/IJEEE-V13I10P113

Review of Machine Learning Approaches for Leaf Disease Detection in Rice Crop: Trends, Challenges, and Future Opportunities

Anshul Jaiswal, Naveen Kumar Dewangan, Amar Kumar Dey

Received Revised Accepted Published
24 Jun 2026 01 Oct 2026 20 Oct 2026 28 Oct 2026

Citation :

Anshul Jaiswal, Naveen Kumar Dewangan, Amar Kumar Dey, "Review of Machine Learning Approaches for Leaf Disease Detection in Rice Crop: Trends, Challenges, and Future Opportunities," International Journal of Electrical and Electronics Engineering, vol. 13, no. 10, pp. 179-192, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I10P113

Abstract

Rice leaf disease is of important concern in developing countries, especially when the livelihood of a major population is dependent on agronomy. Rice is considered a staple crop in South Asian countries, foliar pathogens in rice cause substantial yield loss and economical burden. This review systematically examines the evolution of machine learning and state-of-the-art deep learning approaches for the detection of rice leaf disease. Unlike studies concentrated only on model performance, this review focused on the complete pipeline - spanning challenges observed during image acquisition, preprocessing steps, data characterization, features representation, classification methodology used, along with misused evaluation metrics leading to unsuccessful real-world deployment. This literature reveals a clear paradigm shift from handcrafted feature-based machine learning systems to deep learning architectures, because of their consistently high accuracy, learning discriminative representations directly from raw images without any human intervention. This review also highlights the growing demand for lightweight models for smarter IoT deployments. Overall evidence indicates that the future of automatic rice leaf detection depends on creating standardized benchmark datasets, robust cross-domain validation, and collaborating with smart agriculture frameworks combined with cognitive computer vision and domain adaptive learning, which are capable of accurate disease diagnosis under diverse field conditions.

Keywords

Deep learning, Handcrafted features, Leaf disease, Machine learning, Rice crop.

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