Real time iot enabled automated leaf disease identification using deep learning models - A review

Abstract

Timely identification of diseases in crops and taking immediate actions to prevent them from being completely destroyed is a key part of agricultural production. Environmental challenges like unfavorable weather conditions and climate changes directly affect the plant growth and it causes viral infections that can lead to the eradication of the crops. Microorganisms such as bacteria and fungi can also cause various diseases in plants. In order to provide the accurate and better solution for a plant problem, the farmers first need to identify the exact kind of illness that the plant is having. The existing method of manual diagnosis of plant diseases by a field expert is time consuming as well as costly. Farmers cannot afford the cost of frequent visits to remote laboratories for diagnosis. The use of advanced and effective deep learning models can solve this problem and predict plant diseases at an early stage. IoT has become very popular in the agricultural industry as it provides state of the art solutions to plant problems. This paper aims to study the recent related works on this area and to analyze the advanced methods and models used in the implementation and to allow future research to learn larger capabilities of IoT and Deep Learning in proactively detecting the plant diseases with improved system performance and accuracy.