利用机器学习算法检测叶片病害

D. Babu, Syed Mizbahuddin, Thouti Bharath Kumar, S. Supreeth, Goud Arukala, Naredla Phaneendra Reddy, A. .. S. Kumar
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引用次数: 0

摘要

植物病害主要影响叶片。在大多数情况下,由于各种疾病的症状相似,人工疾病识别方法无法正确识别疾病。人们缺乏足够的植物病害知识。无法发现植物病害导致作物生产损失。此外,由于缺乏解决这一问题的充分理解和指导,农民遭受了重大损失。这就需要开发一种新的植物病害检测技术。本研究试图利用卷积神经网络(CNN)开发一种有效的植物病害检测模型。所提出的模型具有检测单一植物物种中发生的多种疾病的能力。结果表明了该模型的有效性。
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 using Machine Learning Algorithms
Plant diseases are mostly affecting leaves. In most of the cases, manual disease identification method fails to identify the disease correctly due to the similar symptoms of various diseases. People lack sufficient knowledge of plant diseases. The inability to detect the plant disease leads to crop production loss. Moreover, farmers have suffered significant losses as a result of a lack of sufficient understanding and direction to address the issue. This necessitates the need to develop a novel technology to detect the plant diseases. This study has attempted to develop an effective plant disease detection model using Convolutional Neural Networks (CNN). The proposed model has the ability to detect multiple diseases that occur in a single plant species. The results show the efficiency of the proposed model.
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