Scholarlink

To enhance crop yield, it is important to identify and prevent crop diseases. This paper utilizes deep convolutional neural networks (CNNs) to detect and diagnose plant diseases from their leaves. Conventional CNN models typically need a substantial number of parameters and computationally expensive operations. Therefore, we employed transfer learning to replace the standard CNN with models such as VGG-16 and RESNET-34 using the PYTORCH framework. The trained model was tested using a dataset comprising 14 different plant species and 38 categorical classes, achieving an accuracy of 90.42%.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Plant Leaf Disease Detection Using Machine Learning

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • B. Tirapathi Reddy,
  • V. N. Janardhan K,
  • N. Likhitha,
  • P. Keerthana,
  • P. Dhaarini,
  • Siva Sankar Namani

摘要

To enhance crop yield, it is important to identify and prevent crop diseases. This paper utilizes deep convolutional neural networks (CNNs) to detect and diagnose plant diseases from their leaves. Conventional CNN models typically need a substantial number of parameters and computationally expensive operations. Therefore, we employed transfer learning to replace the standard CNN with models such as VGG-16 and RESNET-34 using the PYTORCH framework. The trained model was tested using a dataset comprising 14 different plant species and 38 categorical classes, achieving an accuracy of 90.42%.