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Introduction to Computer Vision and Machine Learning Applications in Agriculture

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Computer Vision and Machine Learning in Agriculture

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Agricultural sustainability can be attained through vision-enabled autonomous machines that work together as a phenomenon to ensure global food security. The demand for efficient as well as reliable food production techniques is rapidly increasing day by day. Computer vision tagged with machine learning approaches grabbed considerable attention for research to meet this demand through analyzing and understanding the input images from humans, robots, drones, sensors, satellites, etc. This chapter gives insights into the integration of computer vision and machine learning as well as deep learning techniques for attaining increased agricultural productions. Additionally, with the help of the above-mentioned techniques different agricultural activities, such as crop health monitoring, weed, disease, pest detection, etc. have also been reviewed to overcome the current challenges and explore the future opportunities for smart farming with low cost and high efficiency.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jahangirnagar University, Savar, Dhaka, Bangladesh

    Mohammad Shorif Uddin

  2. Department of Applied Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

Authors
  1. Mohammad Shorif Uddin
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  2. Jagdish Chand Bansal
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Correspondence to Mohammad Shorif Uddin .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh

    Mohammad Shorif Uddin

  2. Department of Applied Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

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Uddin, M.S., Bansal, J.C. (2021). Introduction to Computer Vision and Machine Learning Applications in Agriculture. In: Uddin, M.S., Bansal, J.C. (eds) Computer Vision and Machine Learning in Agriculture. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-6424-0_1

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