Trends in vision-based machine learning techniques for plant disease identification: A systematic review. (1st December 2022)

• Record Type: Journal Article
• Authors: Thakur, Poornima Singh; Khanna, Pritee; Sheorey, Tanuja; Ojha, Aparajita
• Is Part Of: Expert systems with applications. Volume 208(2022)
• Publication Date: 2022-12-01
• Subjects: Plant disease detection -- Convolutional neural network -- Image processing -- Machine learning -- Deep learning; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118117
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Globally, all the major crops are significantly affected by diseases every year, as manual inspection across diverse fields is time-consuming, tedious, and requires expert knowledge. This leads to significant crop loss in different parts of the world. To provide effective solutions, several smart agriculture solutions are deployed for the control of pests and plant diseases using vision-based machine learning techniques. Despite rapid growth in the field, not many methods have been explored for their suitability in real-time applications. Several open challenges need to be addressed for the applicability of machine learning techniques in IoT-based smart agriculture solutions. Starting from data capturing methods and the availability of public datasets, the present paper provides a comprehensive review of vision-based machine learning techniques for plant disease detection. Initially, 1337 articles were selected from various scholarly resources to perform the survey. Based on the saliency of approaches, 148 articles are reviewed in this paper. Interestingly, a significant amount of research in this direction is taken up by Chinese and Indian researchers, and deep learning is the current research trend, as in other fields. The review concludes that a majority of existing methods exhibit their efficacy on public datasets captured mostly in controlled environmental conditions, but their generalization capability for in-field plant disease detection has not been explored. Lightweight CNN-based methods, on the other hand, have been designed for a limited number of diseases only, and are generally trained on small datasets. The scarcity of large-scale, in-field public datasets is one of the major bottlenecks in developing solutions that can work for a wide variety of plant diseases.