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{ "item_title" : "AI and Machine Learning for Detecting Turmeric Leaf Diseases", "item_author" : [" V. Pream Sudha", "K. Sathiyakumari "], "item_description" : "This book explores the application of machine learning (ML) and artificial intelligence (AI) techniques for detecting, diagnosing and classifying diseases in turmeric leaves. It offers a detailed exploration of image processing, feature extraction and ML models applied in precision agriculture to enhance turmeric crop health. This book serves as a valuable resource for researchers, agronomists and AI practitioners looking to implement machine learning solutions in turmeric farming. It bridges the gap between agriculture and AI-driven automation, enhancing crop monitoring and early disease detection.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/6/20/843/037/6208430372_b.jpg", "price_data" : { "retail_price" : "48.00", "online_price" : "48.00", "our_price" : "48.00", "club_price" : "48.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
AI and Machine Learning for Detecting Turmeric Leaf Diseases|V. Pream Sudha

AI and Machine Learning for Detecting Turmeric Leaf Diseases

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Overview

This book explores the application of machine learning (ML) and artificial intelligence (AI) techniques for detecting, diagnosing and classifying diseases in turmeric leaves. It offers a detailed exploration of image processing, feature extraction and ML models applied in precision agriculture to enhance turmeric crop health. This book serves as a valuable resource for researchers, agronomists and AI practitioners looking to implement machine learning solutions in turmeric farming. It bridges the gap between agriculture and AI-driven automation, enhancing crop monitoring and early disease detection.

This item is Non-Returnable

Details

  • ISBN-13: 9786208430375
  • ISBN-10: 6208430372
  • Publisher: LAP Lambert Academic Publishing
  • Publish Date: February 2025
  • Dimensions: 9 x 6 x 0.17 inches
  • Shipping Weight: 0.24 pounds
  • Page Count: 72

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