基于机器学习的农业植物叶片病害检测与诊断

Aadhitya S V, Ashwin Hariharan R, Sriharipriya K C
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引用次数: 0

摘要

农业及其相关活动仍然是世界上的主要职业之一。各种现代方法和发明被纳入其中,使其更加有效和成功。农民面临的主要问题之一是植物病害。这可能会影响整个季节的产量,所以为了解决这个问题,我们提出了一个基于ResNet的卷积神经网络模型,它可以在植物早期发现各种疾病。为此,采用“新厂村”数据集对模型进行训练和测试。本文提出的基于Resnet的方法在植物病害检测方面具有较高的准确性,并能提出合理的解决方案和可能的病害原因。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Disease Detection and Diagnosis of Agricultural Plant Leaf Using Machine Learning
Agriculture and allied activities still continue to be one of the major occupations in world. Various modern methods and inventions have been incorporated to make it more efficient and successful. One of the main problems the farmers are facing are plant diseases. This can affect the entire yield of a season, so to tackle that problem we are proposing a ResNet based Convolutional neural network model which can detect the various disease in plants in early stage itself. For this purpose, ‘New plant village’ dataset to train and test the model. The proposed Resnet based approach has achieved high accuracy in detecting diseases as well as suggesting a proper solution and possible causes for a plant disease.
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