Leveraging Deep Learning for Accurate Weed Identification

Year : 2025 | Volume : 14 | Issue : 02 | Page : 90 99
    By

    Sharmila A.G,

  • Midhun Murali,

  • Muhil Kumaran R,

  • Nambi Krishna N,

  • Sourav S.R,

  1. Assistant Professor, Department of Computer Science and Engineering, Karpagam College of Engineering Coimbatore, Tamil Nadu, India
  2. Research Scholar, Department of Computer Science and Engineering, Karpagam College of Engineering Coimbatore, Tamil Nadu, India
  3. Research Scholar, Department of Computer Science and Engineering, Karpagam College of Engineering Coimbatore, Tamil Nadu, India
  4. Research Scholar, Department of Computer Science and Engineering, Karpagam College of Engineering Coimbatore, Tamil Nadu, India
  5. Research Scholar, Department of Computer Science and Engineering, Karpagam College of Engineering Coimbatore, Tamil Nadu, India

Abstract

Weed control is very important for all types of agricultural businesses. The project here revolves around the application of computer vision techniques and, more concretely, deep learning techniques, for the effective recognition and classification of weeds. The EfficientNetB4 architecture is an appropriate backbone as its scalability and performance optimization is adequate. The modifier used is Adam optimization algorithm which will serve as a pre- processor for the model. Weeds at different empirical conditions are optimized in the dataset, and some ‘data augmentation’ techniques have been implemented to improve the image. Accuracy from the measures of precision and recall is indeed a metric to ascertain how effective the model would be when conducting image analysis and validation. I would posit this model saves time and resources to better an agricultural worker’s productivity. Moreover, the developed system is intuitive, it enables the farmer to diagnose the issue effortlessly, and it is largely self-sufficient which increases the efficacy of the farmer. All these point to the role of complex peasant in crop yield increases.

Keywords: Weed Detection, Deep Learning, EfficientNetB4, Computer Vision, Data Augmentation, Adam Optimizer, Precision and Recall, Smart Farming, Image Classification, Agricultural Automation

[This article belongs to Research & Reviews : Journal of Agricultural Science and Technology ]

How to cite this article:
Sharmila A.G, Midhun Murali, Muhil Kumaran R, Nambi Krishna N, Sourav S.R. Leveraging Deep Learning for Accurate Weed Identification. Research & Reviews : Journal of Agricultural Science and Technology. 2025; 14(02):90-99.
How to cite this URL:
Sharmila A.G, Midhun Murali, Muhil Kumaran R, Nambi Krishna N, Sourav S.R. Leveraging Deep Learning for Accurate Weed Identification. Research & Reviews : Journal of Agricultural Science and Technology. 2025; 14(02):90-99. Available from: https://journals.stmjournals.com/rrjoast/article=2025/view=230745


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Regular Issue Subscription Original Research
Volume 14
Issue 02
Received 30/05/2025
Accepted 25/07/2025
Published 08/11/2025
Publication Time 162 Days
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