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Agriculture has witnessed a paradigm shift with the combination of artificial intelligence, particularly computer vision, to enhance productivity, efficiency, and sustainability. Machine vision methodologies in farming encompass a wide range of applications, including crop monitoring, pest and disease detection, weed classification, harvest estimation, and autonomous machinery navigation. These techniques leverage advanced image processing, deep learning, and hyperspectral imaging to provide real-time insights for precision agriculture. This chapter explores various machine vision-based approaches that aid in smart farming, discussing their impact on decision-making and resource optimization. Additionally, it highlights recent advancements, challenges, and future research directions in deploying machine vision technologies for agricultural applications.

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Machine Vision and Deep Learning for Smart Agriculture

  • 章节

  • 发表日期：

    2025年

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  • P. Raghavendra Prasad,
  • Nagajyothi Dimmita,
  • T. Swapna,
  • S. Kanakaprabha,
  • K. Shanthi Latha

摘要

Agriculture has witnessed a paradigm shift with the combination of artificial intelligence, particularly computer vision, to enhance productivity, efficiency, and sustainability. Machine vision methodologies in farming encompass a wide range of applications, including crop monitoring, pest and disease detection, weed classification, harvest estimation, and autonomous machinery navigation. These techniques leverage advanced image processing, deep learning, and hyperspectral imaging to provide real-time insights for precision agriculture. This chapter explores various machine vision-based approaches that aid in smart farming, discussing their impact on decision-making and resource optimization. Additionally, it highlights recent advancements, challenges, and future research directions in deploying machine vision technologies for agricultural applications.