Machine/deep learning techniques for disease and nutrient deficiency disorder diagnosis in rice crops: A systematic review

Abstract

Disease and nutrient deficiency disorders significantly impact the productivity of rice crops. Timely identification of these conditions is essential for effective mitigation of potential crop damage. To address this challenge, considerable research is happening in the field of rice crop monitoring and maintenance, using cutting-edge techniques like Machine learning (ML)/Deep learning (DL). This study aims to address critical aspects of the research landscape, including publication trends, data modalities, ML/DL models, pre-processing methods, segmentation techniques, and feature selection approaches in the context of rice crop's health. By presenting both research findings and existing gaps, this systematic literature review (SLR) offers valuable insights to direct future research endeavours in this domain. Our investigation involves a comprehensive review of articles sourced from Scopus, IEEE Xplore, Science Direct and Google Scholar resulting in a dataset of 91 unique articles spanning from the year 2013–2023. Following rigorous selection criteria, these 91 articles have been considered for in-depth analysis. Through an extensive examination of this corpus, our study seeks to provide answers to seven key questions pertaining to the past, present, and future directions of research of ML/DL application in rice crop health monitoring and disease/disorder diagnosis. The review adheres to the agricultural science-based PRISMA systematic review methodology and incorporates statistical analysis to explore relationships among variables such as dataset sample size, experimental accuracy, and classification models employed in various studies.