Plant Leaf Disease Classification and Detection System Using Machine Learning
Plant Leaf Disease Classification and Detection System Using Machine Learning
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使用机器学习的植物叶病分类和检测系统
DOI：
10.1088/1742-6596/1712/1/012012
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
M. Nithya
中科院分区：
文献类型：
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作者：
G. Geetha;S. Samundeswari;G. Saranya;K. Meenakshi;M. Nithya
在印度这样的发展中国家，农业扮演着值得注意的角色。农业对印度农村生计的干预约占58%。在农产品中，番茄是使用量最大的作物之一。因此，防止番茄数量和产量的显著损失在很大程度上取决于对番茄植株可能存在的病害的识别和分类。像图像处理这样的最新和促进的技术被用来使用不同类型的技术和算法来纠正这些问题。最初，当番茄植株发生一种特定类型的疾病时，植株的叶片会受到影响。在这个项目中，使用了四个连续的阶段来发现疾病的类型。这四个阶段包括预处理、叶片分割、特征提取和分类。为了去除噪声，我们进行了预处理，并利用图像分割来分割叶片的受损或受损区域。K-近邻(KNN)算法是一种有指导、有监督和先进的机器学习算法，用于寻找与分类和回归相关的问题的解。在终末期，建议用户进行治疗。大多数活的植物都受到这些疾病的不利影响。本文提出了一种基于颜色、边界和纹理的叶部病害图像处理方法，能够从图像中识别番茄植株的缺陷，为农民提供快速、可靠的检测结果。
In a developing country like India agriculture plays a noteworthy role. Agricultural intervention in the livelihood of rural India indulges by about 58%. Among the agricultural products, tomato is one of the most used crops. Thus, preventing significant loss in quantity and yield of tomato is majorly dependent on recognition and classification of diseases a tomato plant might possess. Latest and fostering technologies like Image processing is used to rectify such issues using different types of techniques and algorithms. Initially, the leaves of a tomato plant get affected, when plant develops a particular type of disease. In this project, four consecutive stages are used to discover the type of disease. The four stages include pre-processing, leaf segmentation, feature extraction and classification. To remove the noise we are doing the pre-processing and to part the affected or damages area of the leaf, image segmentation is used. The k-nearest neighbors (KNN) algorithm, which is a guided, supervised and advance machine learning algorithm, is implemented to find solutions for both the problems related to classification and regression. During the terminal stage, user is recommended with the treatment. Mostly live plants are adversely affected by the diseases. This paper imparts representation of leaf disease detection employing image processing that can identify drawbacks in tomato plant from images, based on color, bound and texture to give the brisk and reliable results to the farmer.