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A Systematic Review on Image Processing and Machine Learning Techniques for Detecting Plant Diseases

机译：用于检测植物病害的图像处理和机器学习技术的系统综述

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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 Agriculture industry is still thriving on outdated technical methodologies.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.
机译：预测影响其寿命影响植物的疾病类型的技术将对农业的帮助是巨大的帮助。本文以全面覆盖各种基于图像的植物叶片疾病进行的调查形式的这种技术抛光。这种疾病大多是严重的，他们在植物的任何部门袭击。由于这种疾病，带来了生产力，增加了农业产业的经济损失，存在巨大的累计损失。农业行业需要维持和发展从这种障碍得到高效的障碍。这可以通过精确监测健康并在植物终身时间的适当阶段检测疾病来完成。技术在日常生活中的每一天的各个领域都散布了它的翅膀，但它仍然在农业领域的范围不起作用。农业产业仍在过时的技术方法中蓬勃发展。植物病的诊断不当可能导致生产，时间，成本和产品质量方面的巨大损失。需要在整个日益增长的阶段进行植物的条件，导致成功培养。作为技术创新的一部分，研究人员一直在应用图像处理技术进行监测，并在其各个阶段诊断植物疾病。正在设计适当的机器学习算法，以精确地识别其在其生命周期内的各种感染以及可提供克服损失的处理类型。

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