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Plant Leaf Disease Detection Using CNN Algorithm

机译:使用CNN算法植物叶疾病检测

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Agriculture is the primary source of economic development in India. The fertility of soil, weather conditions, and crop economic values make farmers select appropriate crops for every season. To meet the increasing population requirements, agricultural industries look for improved means of food production. Researchers are in search of new technologies that would reduce investment and significantly improve the yields. Precision is a new technology that helps in improving farming techniques. Pest and weed detection and plant leaf disease detection are the noteworthy applications of precision agriculture. The main aim of this paper is to identify the diseased and healthy leaves of distinct plants by extracting features from input images using CNN algorithm. These features extracted help in identifying the most relevant class for images from the datasets. The authors have observed that the proposed system consumes an average time of 3.8 seconds for identifying the image class with more than 94.5% accuracy.
机译:农业是印度经济发展的主要来源。土壤,天气条件和作物经济价值的生育能力使农民为每个季节选择合适的作物。为了满足人口需求不断增加,农业产业寻找改善的食品生产手段。研究人员正在寻找减少投资并显着提高产量的新技术。精度是一种新技术,有助于改善养殖技术。害虫和杂草检测和植物叶病检测是精密农业的值得注意的应用。本文的主要目的是通过使用CNN算法从输入图像中提取特征来鉴定不同植物的患病和健康叶子。这些功能提取有助于识别来自数据集的图像的最相关的类。作者已经观察到所提出的系统使用超过94.5%的精度识别图像类的平均时间为3.8秒。

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