Plant Leaf Disease Detection Using CNN Algorithm
Plant Leaf Disease Detection Using CNN Algorithm
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DOI：
10.4018/ijismd.2021010101
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发表时间：
2021-01-01
影响因子：
1.2
通讯作者：
Srinivasu, Parvathaneni Naga
中科院分区：
文献类型：
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作者：
Deepalakshmi, P.;Krishna, Prudhvi T.;Srinivasu, Parvathaneni Naga
农业是印度经济发展的主要来源。土壤的肥力、天气条件和作物的经济价值使农民在每个季节选择合适的作物。为了满足不断增长的人口需求，农业工业寻求改进的粮食生产手段。研究人员正在寻找能够减少投资并显著提高产量的新技术。精准是一项有助于提高农业技术的新技术。病虫害、杂草检测和植物叶片病害检测是精准农业中值得关注的应用。本文的主要目的是利用CNN算法从输入图像中提取特征来识别不同植物的病叶和健康叶。提取的这些特征有助于从数据集中识别最相关的图像类别。作者观察到，该系统识别图像类别的平均时间为3.8秒，准确率超过94.5%。
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.