基于ConvNet模型的玉米叶片病害鉴定

K. K, S. M, D. G, I. R, Yawanikha. T
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引用次数: 1

摘要

农业是决定一个国家发展的最关键因素之一。农业雇佣了印度60%以上的人口。植物病害是小农的一个长期问题,随着季节的变化对收入和粮食安全构成威胁。这些疾病最初攻击植物的叶片,然后感染整个植物,降低作物的质量和产量。由于农场里有大量的植物,人类不可能识别和诊断每种植物的状态。因为这些疾病是会传染的,所以识别每一种植物是至关重要的。深度卷积神经网络模型在疾病识别中的准确性结果显示了它的发展前景,并将对疾病识别效率产生重大影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of Maize Plant Leaf Disease Detection using ConvNet Model
Agriculture is one of the most crucial determinants of a country's development. Agriculture employs more than 60% of the population in India. Plant disease is a persistent problem for smallholder farmers, posing a threat to income and food security as the seasons change. These diseases attack the leaves of the plant initially, then infect the entire plant, reducing the quality and quantity of the crop produced. It is impossible for a human to recognize and diagnose each plant's status due to a large number of plants on the farm. Because these ailments are contagious, it's vital to identify each plant. The deep convolutional Neural Network model's accuracy results in disease identification revealed that it is promising and can have a major impact on disease identification efficiency.
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