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Using Deep Learning Models for Crop and Weed Classification at Early Stage

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Sentiment Analysis and Deep Learning

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1432))

Abstract

Agriculture is essential for human existence, and it plays an important role in the world economy. There is increasing demand for food to feed the ever-increasing world population. Agriculture is affected by climate changes along with weed control. Weeds are unwanted plants that compete with plants for nutrition, and sunlight and adversely affect crop quality and production. Manual weeding is a tedious and labor-intensive task because both crop and weed look the same by visual appearance. Artificial intelligence techniques like deep learning can address this problem of crop and weed classification. In this research work, a deep learning-based classification system has been proposed to classify the weed and crop based on RGB images. We investigated two popular deep learning-based transfer learning models, namely DenseNet169 and MobileNetV2, and assessed their performances for crop and weed recognition. These models perform excellently with an accuracy of 97.14 and 94.92%, respectively. The significant accuracy results make the model an important tool for farmers to identify weeds.

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Author information

Authors and Affiliations

  1. ICAR-Indian Institute of Soil and Water Conservation, Dehradun, Uttarakhand, India

    Akshay Dheeraj

  2. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India

    Satish Chand

Authors
  1. Akshay Dheeraj
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  2. Satish Chand
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Corresponding author

Correspondence to Akshay Dheeraj .

Editor information

Editors and Affiliations

  1. Institute of Engineering, Tribhuvan University, Kirtipur, Nepal

    Subarna Shakya

  2. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada

    Ke-Lin Du

  3. University of West Attica, Greece, Greece

    Klimis Ntalianis

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Dheeraj, A., Chand, S. (2023). Using Deep Learning Models for Crop and Weed Classification at Early Stage. In: Shakya, S., Du, KL., Ntalianis, K. (eds) Sentiment Analysis and Deep Learning. Advances in Intelligent Systems and Computing, vol 1432. Springer, Singapore. https://doi.org/10.1007/978-981-19-5443-6_69

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