Intelligent Weed Detection for Sustainable Agriculture Using Deep Learning

References

[1] Kughur, P.G.: The effect of herbicides on crop production and environment in Makurdi local government area of Benue state, Nigeria. Journal of Sustainable Development in Africa 14(4), 206- 216 (2012).

[2] Ahmad, M.T.: Development of an automated mechanical intra-row weeder for vegetable crops. Graduate Theses and Dissertations, paper 1512141 (2012).

[3] Kaya, R., Buzluk, S.: Integrated weed control in sugar beet through combinations of tractor hoeing and reduced dosage of herbicide mixture. Turkish Journal of Agriculture and Forestry 30, 137-144 (2006).

[4] Decker, T.G., Devillers, R.W., Gallier, S.: Detecting agglomeration patterns on solid propellant surface via a new curvature-based multiscale method. Acta Astronautica 206, 123- 132 (2023). https://doi.org/10.1016/j.actaastro.2023.02.020

[5] Zeng, J., Liu, M., Fu, X., Gu, R., Leng, L.: Curvature bag of words model for shape recognition. IEEE Access 7, 57163-57171 (2019). https://doi.org/10.1109/ACCESS.2019.2913688

[6] Wang, J., Bai, X., You, X., Liu, W., Latecki, L.J.: Shape matching and classification using height functions. Pattern RecognitionLetters33(2), 134-143 (2012). https://doi.org/10.1016/j.patrec.2011.09.042

[7] Belongie, S., Malik, J., Puzicha, J.: Shape context: A new descriptor for shape matching and object recognition. Advances in neural information processing systems 13, (2000).

[8] Ling, H., Jacobs, D.W.: Shape classification using the inner- distance. IEEE transactions on pattern analysis and machine intelligence 29(2), 286-299 (2007). https://doi.org/10.1109/TPAMI.2007.41

[9] Shu, X., Wu, X.J.: A novel contour descriptor for 2D shape matching and its application to image retrieval. Image and Vision Computing 29(4), 286-294(2011). https://doi.org/10.1016/j.imavis.2010.11.001

[10] Bakhshipour, A., Jafari, A., Nassiri, S.M., Zare, D.: Weed segmentation using texture features extracted from wavelet sub- images. Biosystems Engineering 157, 1-12 (2017). https://doi.org/10.1016/j.biosystemseng.2017.02.002

@article{1707733,
      author = {Pranesh S J, Narendhar Kumar S, Kalai Varshaa G R, Dr. V Anandhkumar},
      title = {Intelligent Weed Detection for Sustainable Agriculture Using Deep Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {10},
      pages = {90-98},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707733.pdf},
      abstract = {It is a study to create intelligent farming technology in sustainable weed classification by using a self-operated device that utilises image processing and CNN in proper classification. The system can deploy in real time, making use of the chemical less while easing farm functions. Through lesser use of herbicide, it minimises contamination of soil and water, defending human health as well as non-pesticidal insects, and encouraging natural methods of weeding. Moreover, precision fertilizer management reduces expenses, making sustainable agriculture more feasible. The model combines environmental protection with farm productivity, leading to a robust food supply chain. The model attains 99.3% accuracy, 99% precision, recall, F1 score, and weighted average of 600 for support with visualized performance graphs.},
      keywords = {CNN (Convolutional Neural Network), Accuracy, Precision, Recall, F1 Score, machine learning, Image processing.},
      month = {April},
  }