基于机器学习的叶片病害检测与分类

Sandeep Kumar, K. Prasad, A. Srilekha, T. Suman, B. Rao, J. V. Vamshi Krishna
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引用次数: 30

摘要

植物病害检测是农业中必须完成的一项重要任务。这是经济深深依赖的东西。植物感染的发现是农业综合领域的一项重要工作,因为植物病害是非常普遍的。为了识别叶片的疾病,需要对植物进行持续的观察。这种对植物的观察或持续监测需要大量的人力,而且也很耗时。简单地说,需要某种程序化的策略来观察植物。基于程序的植物病害识别更容易发现受损叶片,减少了人力劳动,节省了时间。与现有技术相比,所提出的算法可以区分植物中的疾病,并更准确地对它们进行分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Leaf Disease Detection and Classification based on Machine Learning
Detection of diseases in plants is a significant task that has to be done in agriculture. This is something on which the economy profoundly depends. Infection discovery in plants is a significant job in the agribusiness field, as having diseases in plants is very common. To recognize the diseases in leaves, a continuous observation of the plants is required. This observation or continuous monitoring of the plants takes a lot of human effort and it is time-consuming too. To make it simply some sort of programmed strategy is required to observe the plants. Program based identification of diseases in plants makes easier to detect the damaged leaves and reduces human efforts and time-saving. The proposed algorithm distinguishing sickness in plants and classify them more accurately as compared to existing techniques.
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