Rice leaf disease identification and classification using machine learning techniques: A comprehensive review

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-14 DOI:10.1016/j.engappai.2024.109639

Rashmi Mukherjee , Anushri Ghosh , Chandan Chakraborty , Jayanta Narayan De , Debi Prasad Mishra

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引用次数: 0

Abstract

In recent times, various researchers attempted to develop artificial intelligence (AI) assisted techniques in the field of agriculture for early detection, surveillance and treatment related to plant leaf, seed, root, and stem diseases. Rice leaf disease detection is one of such important areas, where the crop is frequently affected by various diseases. Farmer inspects usually at a later stage causing enormous damage. This manual inspection is subjective, time-consuming and error prone. Under such situation, AI-enabled tools and techniques play crucial role for early and more precise prediction of rice diseases.

This paper demonstrates a comprehensive review on application of AI-assisted rice leaf disease detection in the last two decades. Research studies were searched using relevant keywords through the online databases [PubMed: 246; Science Direct: 100; Scopus: 56; Web of Science: 8; Willey online library:16; Cochrane:0; Cross references:20]. A total of 446 titles and abstracts were identified as suitable for this study and finally, 48 most-appropriate state-of-art articles were considered. Furthermore, this study summarizes the visual characteristics of rice leaf diseases, imaging modalities and image acquisition techniques. Various image processing techniques for infected leaf area segmentation and feature extraction were also summarized. Finally, the reported machine learning (ML) algorithms were discussed and compared in respect to their advantages and limitations. In addition, AI-enabled mobile applications for rice disease detection have been discussed.

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利用机器学习技术进行水稻叶病识别和分类：综述

近来，许多研究人员尝试在农业领域开发人工智能辅助技术，用于植物叶片、种子、根茎病害的早期检测、监控和治疗。水稻叶病检测就是其中一个重要领域，作物经常受到各种病害的影响。农民通常在后期才进行检查，造成巨大损失。这种人工检查主观、耗时且容易出错。在这种情况下，人工智能工具和技术在早期、更精确地预测水稻病害方面发挥着至关重要的作用。本文全面回顾了过去二十年中人工智能辅助水稻叶病检测的应用。本文通过在线数据库[PubMed: 246; Science Direct: 100; Scopus: 56; Web of Science：8；Willey 在线图书馆：16；Cochrane：0；交叉引用：20]。共确定 446 篇标题和摘要适合本研究，最后考虑了 48 篇最合适的最新文章。此外，本研究还总结了水稻叶片病害的视觉特征、成像模式和图像采集技术。还总结了用于感染叶区分割和特征提取的各种图像处理技术。最后，讨论并比较了所报告的机器学习（ML）算法的优势和局限性。此外，还讨论了用于水稻病害检测的人工智能移动应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Engineering Applications of Artificial Intelligence 工程技术-工程：电子与电气

CiteScore

9.60

自引率

10.00%

发文量

505

审稿时长

68 days

期刊介绍： Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.