Scholarlink

Mango as a fruit has many varieties, and identification of the same with the naked eye can be tiresome. Pilot Super-Resolution Network (PSRN) model is proposed to produce super-resolution images from that of the low-resolution mango fruit images and identify the varieties in this paper. The novel PSRN model is executed on the Mango Variety and Grading dataset for super-resolution factors 2, 4, and 6. The metrics recorded include PSNR values of 30.37, 32.936, and 35.428, SSIM values of 0.8275, 0.9146, and 0.9279, and classification accuracy values of 99.45, 98.86, and 97.64 on factors 2, 4, and 6, respectively.

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

Pilot Super-Resolution Network (PSRN)-Based Mango Fruit Classification

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • P. V. Yeswanth,
  • Sammeta Kushal,
  • V. Tharun Kumar,
  • N. R. Ackshay,
  • Ravindra Gangudi,
  • Molapally Tharun Kumar,
  • S. Deivalakshmi,
  • Y. Thanya,
  • K. M. Lokesh Kumar

摘要

Mango as a fruit has many varieties, and identification of the same with the naked eye can be tiresome. Pilot Super-Resolution Network (PSRN) model is proposed to produce super-resolution images from that of the low-resolution mango fruit images and identify the varieties in this paper. The novel PSRN model is executed on the Mango Variety and Grading dataset for super-resolution factors 2, 4, and 6. The metrics recorded include PSNR values of 30.37, 32.936, and 35.428, SSIM values of 0.8275, 0.9146, and 0.9279, and classification accuracy values of 99.45, 98.86, and 97.64 on factors 2, 4, and 6, respectively.