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Agriculture is important to society and needs to be planned, researched, and carried out. Investigating novel approaches, cutting-edge techniques, and potential accelerators is crucial. LATEX. With some technologies that improve search quality, the farmer can lessen the amount of labour. In the realm of convolution neural networks, it is crucial to determine the growth estimation of marijuana utilizing deep learning technology. This review study lists the various weed species that are detrimental to crops. This review paper summarizes the advancements in artificial intelligence and image processing approaches for the detection of weed and its classification. It uses the most recent techniques available. The four steps which are involved in weed detection and classification are (i) Preprocessing, (ii).Segmentation, (iii).Feature extraction, (iv).Classification which were specifically described in depth. Lastly, the difficulties and solutions that researchers had offered for classifying and identifying weeds in the field were covered.

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Intelligent Detection of Weeds in Crops Using Deep Learning Approach

  • 会议论文

  • 发表日期：

    2026年

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  • K. Ragasritha,
  • N. Navatha,
  • Hemanth Surya Sai Sunkara,
  • B. Shailesh Chowdary,
  • Madala Sreshta,
  • Rajitha Ala

摘要

Agriculture is important to society and needs to be planned, researched, and carried out. Investigating novel approaches, cutting-edge techniques, and potential accelerators is crucial. LATEX. With some technologies that improve search quality, the farmer can lessen the amount of labour. In the realm of convolution neural networks, it is crucial to determine the growth estimation of marijuana utilizing deep learning technology. This review study lists the various weed species that are detrimental to crops. This review paper summarizes the advancements in artificial intelligence and image processing approaches for the detection of weed and its classification. It uses the most recent techniques available. The four steps which are involved in weed detection and classification are (i) Preprocessing, (ii).Segmentation, (iii).Feature extraction, (iv).Classification which were specifically described in depth. Lastly, the difficulties and solutions that researchers had offered for classifying and identifying weeds in the field were covered.