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Herausgeber: 
  • Kishore Chandra Swain
  • Satiprasad Sahoo
  • Chiranjit Singha
  • Quoc Bao Pham
  • Pradhan Biswajeet
  • Armin Moghimi
  • Machine Learning and AI Technology for Agricultural Applications 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Vorankündigung
    Veröffentlichung:  ANGEKÜNDIGT (November 2026)  
    Genre:  Naturwissensch., Medizin, Technik 
     
    3-D / Agribusiness and primary industries / Agricultural Engineering & Machinery / Agriculture & Related Industries / Agriculture and farming / BUSINESS & ECONOMICS / Industries / Agribusiness / COMPUTERS / Information Technology / Data analysis, Deep learning
    ISBN:  9780443450501 
    EAN-Code: 
    9780443450501 
    Verlag:  Elsevier 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 191 mm / D 
    Bewertung: Keine Bewertung vor Veröffentlichung möglich.
    Inhalt:
    Machine Learning and AI Technology in Agricultural Applications offers a comprehensive overview of how artificial intelligence and machine learning are transforming the agricultural industry. By delving into image processing and advanced data analysis, the book demonstrates how technology addresses modern agricultural challenges, including climate change, urbanization, and increasing global populations. It emphasizes the importance of integrating sensors and data collection methods to generate vast pools of information, which can be efficiently analyzed through AI-driven solutions. The text lays a strong foundation for understanding the role of technological innovation in supporting sustainable and secure food production.

    Beyond introducing core machine learning models such as random forest, support vector machines, logistic regression, and decision trees, the book highlights the centralization of critical agricultural data in the cloud. This resource benefits both students and seasoned agricultural scientists, providing practical insights for optimizing crop yields, monitoring soil and weather conditions, and managing resources like fertilizers and pesticides. The book also explores the rapid analysis of complex datasets, empowering users to make informed, timely decisions in real-world agricultural scenarios.

      



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