基于改进VGGNet19结构的苹果、樱桃和桃子病害分类

Sanika Kapoor, Dipanshu Kumar, A. Sinha
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引用次数: 1

摘要

食物是人类的必需品。植物病害对每个人的粮食供应构成威胁,立即查明这些病害以使作物生长不受影响是一项重要任务。以前,疾病的鉴定是一项繁琐的手工工作。但随着人工智能领域的进步,这项任务能够通过使用神经网络实现自动化。利用6349张图像的公共数据集,尝试对苹果、樱桃和桃子叶片的疾病进行分类,准确率为99.76%。通过扩展数据集,训练后的算法具有进一步检测和分类其他作物疾病的潜力。因此,神经网络的使用正在成为快速准确识别和分类植物疾病的可靠来源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of Various Diseases Affecting Apple, Cherry, and Peach Plants using Modified VGGNet19 Architecture
Food is a vital need for humans. Diseases in plants pose a threat to the availability of food to everyone and it is an important task to identify these diseases immediately so that the growth of the crop remains unaffected. Earlier, the identification of disease was a tedious and manual task. But after the advancements in the domain of artificial intelligence, this task is capable of automation with the use of neural networks. With a public dataset of 6349 images, an attempt has been made to classify diseases in apple, cherry, and peach leaves with an accuracy of 99.76%. The trained algorithm has potential to further detect as well as classify disease in other crops as well by expanding the dataset. Thus, the use of neural networks is becoming a reliable source for quick plus precise identification as well as classification of diseases in plants.
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