基于深度学习的番茄叶片病害检测技术

A. Karegowda, R. Jain, G. Devika
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引用次数: 1

摘要

在印度,番茄是一种受欢迎的主食,具有很高的商业价值和相当大的生产能力;然而,由于各种疾病,番茄收获的质量和数量下降,因此给农民带来了巨大的经济损失。在缺乏农业专业人员帮助农民的情况下,提出了一种基于深度学习(DL)的用户友好、即时的作物病害检测手机,帮助农民了解番茄病害的类型和治疗方法。两种基于深度学习的方法:YOLO和Faster RNN被用于检测;然后使用SVM和随机森林树进行分类。YOLO和随机森林树的准确率在90%到95%之间。开发的应用程序为农民提供了英语和印度卡纳塔克邦当地语言卡纳达语的操作选项。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
State-of-Art Deep Learning Based Tomato Leaf Disease Detection
In India, the tomato plant is a popular staple food with high commercial value and considerable production capacity; however, the quality and quantity of the tomato harvest decreases due to a variety of diseases and henceforth leads to great financial loss for farmers. With lack of agricultural professions to assist the farmers, a deep learning (DL) based user friendly, just-in-time mobile is proposed for the detection of crop diseases for assisting farmers to know about the type of tomato disease and the remedy for the same. Two DL based methods: YOLO and Faster RNN have been used for detection; followed by classification using SVM and Random forest tree. YOLO and Random forest tree resulted in accuracy in the range of 90% to 95%. The developed app provides option to the farmer to operate in English as well as in local language Kannada of Karnataka state of India.
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