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Computer Vision and Machine Learning in Agriculture, Volume 2 - Jagdish Chand Bansal - Bog

Bag om Computer Vision and Machine Learning in Agriculture, Volume 2

This book is as an extension of previous book ¿Computer Vision and Machine Learning in Agriculture¿ for academicians, researchers, and professionals interested in solving the problems of agricultural plants and products for boosting production by rendering the advanced machine learning including deep learning tools and techniques to computer vision algorithms. The book contains 15 chapters. The first three chapters are devoted to crops harvesting, weed, and multi-class crops detection with the help of robots and UAVs through machine learning and deep learning algorithms for smart agriculture. Next, two chapters describe agricultural data retrievals and data collections. Chapters 6, 7, 8 and 9 focuses on yield estimation, crop maturity detection, agri-food product quality assessment, and medicinal plant recognition, respectively. The remaining six chapters concentrates on optimized disease recognition through computer vision-based machine and deep learning strategies.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9789811699931
  • Indbinding:
  • Paperback
  • Sideantal:
  • 276
  • Udgivet:
  • 15. Marts 2023
  • Udgave:
  • 23001
  • Størrelse:
  • 155x16x235 mm.
  • Vægt:
  • 423 g.
  • 2-3 uger.
  • 9. Maj 2024
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Herefter fra 79 kr./md. Ingen binding.

Beskrivelse af Computer Vision and Machine Learning in Agriculture, Volume 2

This book is as an extension of previous book ¿Computer Vision and Machine Learning in Agriculture¿ for academicians, researchers, and professionals interested in solving the problems of agricultural plants and products for boosting production by rendering the advanced machine learning including deep learning tools and techniques to computer vision algorithms. The book contains 15 chapters. The first three chapters are devoted to crops harvesting, weed, and multi-class crops detection with the help of robots and UAVs through machine learning and deep learning algorithms for smart agriculture. Next, two chapters describe agricultural data retrievals and data collections. Chapters 6, 7, 8 and 9 focuses on yield estimation, crop maturity detection, agri-food product quality assessment, and medicinal plant recognition, respectively. The remaining six chapters concentrates on optimized disease recognition through computer vision-based machine and deep learning strategies.

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