Novel deep learning approaches for crop leaf disease classification: A review

Abstract

To encourage sustainable progress, it is suggested that in a world connected by virtual platforms, modern society should merge big data, artificial intelligence, machine learning, information and communication technology (ICT), as well as the “Internet of Things” (IoT). When real-life problems are considered, the above technology processes are essential in solving the issues. Food is an essential need of human beings. Food supply has become crucial, and it is very important to increase the adequate cultivation of plants for large populations due to huge population growth. At the same time, farmers are struggling with a variety of food plant diseases that significantly affect the harvesting and production in agricultural fields. Nevertheless, the agricultural productivity of rural areas is directly involved with the increase in the economic growth of developing countries such as Sri Lanka, India, Myanmar and Indonesia. Early identification of crop disease, using a well-established modern technique, is vital. It necessitates a number of processes observing large-scale agricultural fields as a disease can infect different parts of the plant such as leaf, roots, stem and fruit. Most diseases appear in plant leaves and have the potential to spread them all over the field within a very short time. This paper reviews several state-of-the-art methods that can be used for plant leaf disease recognition with a special reference to deep learning-based methods.