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A Systematic Review on Image Processing and Machine Learning Techniques for Detecting Plant Diseases
N. Gobalakrishnan, , C.J. Raman, L.J. Ali,
Published in Institute of Electrical and Electronics Engineers Inc.
Pages: 465 - 468
Techniques predicting the type of diseases affecting plants in their lifetime will be of immense help to agriculturists. This article throws light upon such techniques in the form of a survey that had been carried out comprehensively covering various image based plant leaf diseases. Such diseases are mostly grievous and they strike at any part of the plant. There are huge accumulated losses due to such diseases that bring down the productivity and increase the economic losses in the agricultural industry. Agriculture industry needs to sustain and evolve from such obstacles to be highly profitable. This can be done by precisely monitoring the health and detecting the diseases at appropriate stages of the plant's life time. Technology has spread its wings in every field of day to day life but still its reach in the field of agriculture is not up to the mark. Agriculture industry is still thriving on outdated technical methodologies. Improper diagnosis of plant disease may lead to huge losses in terms of production, time, cost and product quality. The condition of the plant needs to be tracked throughout its growing stages leading to successful cultivation. As part of technological innovation, researchers had been applying the image processing techniques for monitoring as well as diagnosing the plant diseases in its various stages. Appropriate machine learning algorithms are being designed and applied for precisely identifying the various infections on plants throughout its life cycle and the type of treatment that can be afforded for overcoming loss. © 2020 IEEE.