Scholarlink

<p>Agricultural productivity is severely hampered by crop diseases, particularly when detection is done by labour-intensive and error-prone manual visual inspection techniques. This is why the current study suggests a new artificial intelligence (AI)-based method for identifying mustard crop diseases from leaf photos. Four cutting-edge convolutional neural network architectures—DenseNet121, ResNet50V2, MobileNetV2, and VGG16—are used in the study to classify different mustard crop diseases, such as alternaria blight, downy mildew, powdery mildew, and white rust, by utilising deep learning techniques. With a precision of 97.359%, recall of 97.682%, F1 score of 97.490%, and accuracy of 97.387%, MobileNetV2 outperformed the others. Without the need for specialised knowledge, this AI-driven solution allows for quick and precise disease identification, facilitating prompt treatment and enhancing crop quality and yield.</p>

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

A Novel Deep Learning Framework for Mustard Crop Disease Identification Using Leaf Images

  • 期刊论文

  • 发表日期：

    2026年3月7日

查看全文
  • Alok Sahu,
  • Umesh Chandra,
  • Gaurav Shukla,
  • Himani Maheshwari

摘要

Agricultural productivity is severely hampered by crop diseases, particularly when detection is done by labour-intensive and error-prone manual visual inspection techniques. This is why the current study suggests a new artificial intelligence (AI)-based method for identifying mustard crop diseases from leaf photos. Four cutting-edge convolutional neural network architectures—DenseNet121, ResNet50V2, MobileNetV2, and VGG16—are used in the study to classify different mustard crop diseases, such as alternaria blight, downy mildew, powdery mildew, and white rust, by utilising deep learning techniques. With a precision of 97.359%, recall of 97.682%, F1 score of 97.490%, and accuracy of 97.387%, MobileNetV2 outperformed the others. Without the need for specialised knowledge, this AI-driven solution allows for quick and precise disease identification, facilitating prompt treatment and enhancing crop quality and yield.