Scholarlink

Potato cultivation is a milestone in global Agriculture. It is a vital staple food source rich in essential vitamins and minerals. However, risk of diseases is a big challenge to agricultural sustainability. Early detection of these diseases is very important for effective crop management. In the previously explored solution, the classification of potato leaf diseases was conducted using established architectures such as VGG-16, VGG-19, and ResNet-50. Among these, VGG-16 exhibited the highest accuracy, achieving an accuracy of 97. However, in our proposed solution, we introduce MobileNet-V2 architecture along with Transfer Learning, which demonstrates superior performance with an accuracy of 98.33.

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

MobileNet-Based Deep Learning for Accurate Potato Leaf Disease Detection

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • C. Sireesha,
  • Gouri Manasa Shanam,
  • Kaushal Attaluri

摘要

Potato cultivation is a milestone in global Agriculture. It is a vital staple food source rich in essential vitamins and minerals. However, risk of diseases is a big challenge to agricultural sustainability. Early detection of these diseases is very important for effective crop management. In the previously explored solution, the classification of potato leaf diseases was conducted using established architectures such as VGG-16, VGG-19, and ResNet-50. Among these, VGG-16 exhibited the highest accuracy, achieving an accuracy of 97. However, in our proposed solution, we introduce MobileNet-V2 architecture along with Transfer Learning, which demonstrates superior performance with an accuracy of 98.33.