A Systematic Analysis of DL and ML Models for Plant Disease Detection

Plants play a requisite role in human growth and survival. Since time immemorial, the farmers have faced the problem of crop failure due to diseases or pests which has resulted in a huge loss of both money and effort to the farmers. Plant Disease is a major threat to almost all types of plants and affecting food security across the world. The long-established plant disease detection system is a wearisome process and is also not applicable to the wider plantation size, thus reducing crop production. Thus, it is essential to incorporate smart agricultural methodologies to detect plant disease at an early stage and applicable to mass production resulting in finer quality of plants. Through this paper we aim to research on various techniques which are now in use in development of a system to spot the different types of plant diseases and suggest recommended actions to the farmers that can improve the quality of their crops at an initial stage and help in better yield. This paper presents an extensive review of deep machine learning models which are proposed to spot various plants disorders using modern detection techniques.