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

• Published in: Engineering applications of artificial intelligence Vol. 139; p. 109639
• Main Authors: Mukherjee, Rashmi, Ghosh, Anushri, Chakraborty, Chandan, De, Jayanta Narayan, Mishra, Debi Prasad
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-01-2025
• Subjects: Artificial intelligence; Computer assisted screening; Image analysis; Machine learning; Plant imaging; Rice disease; Image analysis; Plant imaging; Rice disease; Computer assisted screening; Artificial intelligence; Machine learning
• ISSN: 0952-1976
• DOI: 10.1016/j.engappai.2024.109639

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.
•Demonstrated a comprehensive review of machine learning algorithms published between 1999 and 2022 for rice leaf disease detection.•Summarized visual characteristics of rice leaf diseases, imaging modalities and image acquisition techniques.•Comparative study amongst litertature in respect to infected area segmentation and feature extraction.•Explored ML algorithms for rice leafe disease detectiojn and compared in respect to its advantages and limitations.