论文标题
基于卷积神经网络的红外图像上的帕迪叶疾病鉴定
Paddy Leaf diseases identification on Infrared Images based on Convolutional Neural Networks
论文作者
论文摘要
农业是人类社会的支柱，因为它对每个生物体都是必需的。就人类而言，帕迪种植非常重要，主要是亚洲大陆，这是主食食品之一。但是，农业中的植物疾病导致生产力枯竭。植物疾病通常是由害虫，昆虫和病原体引起的，如果在特定时间内不受控制，它们会降低生产力至大规模。最终，人们看不到稻田产量的增加。准确，及时识别植物疾病可以帮助农民减轻由于害虫和疾病而导致的损失。最近，深度学习技术已被用来识别稻田疾病并克服这些问题。本文基于模型实现了卷积神经网络（CNN），并测试了由636个红外图像样本组成的公共数据集，其中有五个帕迪病类别和一个健康的类别。拟议的模型熟练识别和分类的五种不同类型的帕迪疾病，准确度为88.28％
Agriculture is the mainstay of human society because it is an essential need for every organism. Paddy cultivation is very significant so far as humans are concerned, largely in the Asian continent, and it is one of the staple foods. However, plant diseases in agriculture lead to depletion in productivity. Plant diseases are generally caused by pests, insects, and pathogens that decrease productivity to a large scale if not controlled within a particular time. Eventually, one cannot see an increase in paddy yield. Accurate and timely identification of plant diseases can help farmers mitigate losses due to pests and diseases. Recently, deep learning techniques have been used to identify paddy diseases and overcome these problems. This paper implements a convolutional neural network (CNN) based on a model and tests a public dataset consisting of 636 infrared image samples with five paddy disease classes and one healthy class. The proposed model proficiently identified and classified paddy diseases of five different types and achieved an accuracy of 88.28%
