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Plant Disease Detection: A Comprehensive Survey - 2020

Plant Disease Detection: A Comprehensive Survey

Research Area:  Machine Learning

Abstract:

Plant diseases can affect vast produce of crops posing a major menace to food security. To avoid this risk, an approach is needed which performs early diagnosis, lacking in copious parts of the globe due to the dearth of essential infrastructure. This paper discusses several experiments and techniques performed for plant disease detection. Each set of methods has its own advantages, limitations and the parameters affecting the results. In this paper, we have shown a general flow observed in most of the Plant Disease Detection techniques and have given a detailed overview and comparison at stages such as selected dataset, pre-processing methods, feature selection and extraction, classification and performance metrics utilized. This paper aims to get an in-depth understanding of algorithm selection and key challenges faced in adopted approaches. Using this analysis, we have identified different techniques that can be used in different stages of a plant disease detection system to give the best results at each stage and identified key challenges that can be faced during detection.

Keywords:  

Author(s) Name:  Radhika Chapaneri; Maithili Desai; Anmolika Goyal; Shreya Ghose; Sheona Das

Journal name:  

Conferrence name:  3rd International Conference on Communication System, Computing and IT Applications (CSCITA)

Publisher name:  IEEE

DOI:  10.1109/CSCITA47329.2020.9137779

Volume Information: