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{ "item_title" : "Identification of Rotten and Healthy Fruit Images Using CNN and Image Class", "item_author" : [" Sonu Rana "], "item_description" : "This book provides a comprehensive introduction to Image Processing and Computer Vision, covering fundamental concepts, algorithms, and real-world applications. Designed for students, researchers, and professionals, it bridges theory with practical implementation.This project explores the use of deep learning techniques for fruit quality detection using image processing. The proposed system demonstrates efficient classification with improved accuracy, offering practical relevance in agricultural automation.An insightful guide that blends knowledge, experience, and real-world examples to help readers master modern technology with confidence.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/9/99/933/551/9999335519_b.jpg", "price_data" : { "retail_price" : "42.50", "online_price" : "42.50", "our_price" : "42.50", "club_price" : "42.50", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Identification of Rotten and Healthy Fruit Images Using CNN and Image Class|Sonu Rana

Identification of Rotten and Healthy Fruit Images Using CNN and Image Class

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Overview

This book provides a comprehensive introduction to Image Processing and Computer Vision, covering fundamental concepts, algorithms, and real-world applications. Designed for students, researchers, and professionals, it bridges theory with practical implementation.This project explores the use of deep learning techniques for fruit quality detection using image processing. The proposed system demonstrates efficient classification with improved accuracy, offering practical relevance in agricultural automation.An insightful guide that blends knowledge, experience, and real-world examples to help readers master modern technology with confidence.

This item is Non-Returnable

Details

  • ISBN-13: 9789999335515
  • ISBN-10: 9999335519
  • Publisher: Eliva Press
  • Publish Date: January 2026
  • Dimensions: 9 x 6 x 0.07 inches
  • Shipping Weight: 0.13 pounds
  • Page Count: 32

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