A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools

Abstract

Several factors associated with disease diagnosis in plants using deep learning techniques must be considered to develop a robust system for accurate disease management. A considerable number of studies have investigated the potential of deep learning techniques for precision agriculture in the last decade. However, despite the range of applications, several gaps within plant disease research are yet to be addressed to support disease management on farms. Thus, there is a need to establish a knowledge base of existing applications and identify the challenges and opportunities to help advance the development of tools that address farmers' needs. This study presents a comprehensive overview of 70 studies on deep learning applications and the trends associated with their use for disease diagnosis and management in agriculture. The studies were sourced from four indexing services, namely Scopus, IEEE Xplore, Science Direct, and Google Scholar, and 11 main keywords used were Plant Diseases, Precision Agriculture, Unmanned Aerial System (UAS), Imagery Datasets, Image Processing, Machine Learning, Deep Learning, Transfer Learning, Image Classification, Object Detection, and Semantic Segmentation. The review is focused on providing a detailed assessment and considerations for developing deep learning-based tools for plant disease diagnosis in the form of seven key questions pertaining to (i) dataset requirements, availability, and usability, (ii) imaging sensors and data collection platforms, (iii) deep learning techniques, (iv) generalization of deep learning models, (v) disease severity estimation, (vi) deep learning and human accuracy comparison, and (vii) open research topics. These questions can help address existing research gaps by guiding further development and application of tools to support plant disease diagnosis and provide disease management support to farmers.