Image Processing based Robotic Car for Agricultural Ploughing using Machine Learning Approach

Authors

  • A. Deepak Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamilnadu
  • Kamal Sharma Department of Mechanical Engineering, Institute of Engineering and Technology, GLA University, Mathura, Uttar Pradesh
  • Guruprasad Ramakrishna Naik Assistant Professor, Department of Economics, Government College of Arts, Science and Commerce, Sanquelim, Goa
  • Devangkumar Umakant Shah Principal and Professor, Department of Electrical Engineering, K. J. Institute of Engineering & Technology, Savli, Vadodara
  • Gajanand Modi Associate Professor, Faculty of Basic and Applied Science, RNB Global Universitya Bikaner, Rajasthan
  • S. Poonguzhali Assistant Professor, VIT School of Agricultural Innovations and Advanced Learning, Vellore Institute of Technology, Vellore, Tamilnadu,
  • Durgeshwar Pratap Singh Department of Mechanical Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand

Keywords:

Machine Learning, Image Processing, Medical Robotics, Mixed Cropping, Disease Detection, Computer Vision

Abstract

For a significant amount of time, agriculture was conducted in a traditional way; only more recently have mechanical technology been utilized to aid. The adoption of intelligent farming practices made possible by the development of robotic technology and sensors is the area on which the experts are concentrating their efforts. With a focus on a heterogeneous robotic system, we provide improved algorithms in this study for both the categorization of fields and the detection of viruses in leaf samples. The basic machine learning approach known as k-means clustering was used to identify the field and image processing techniques were used to the plant leaves. This was done in order to determine the proportion of affected crops. In order to provide a variety of crops utilizing the mixed cropping approach, which has an advantage over other farming techniques, the agricultural sector has been classified. Because of this, agriculture has been categorized. Early diagnosis of a disease may aid in the creation of more effective preventative measures while it is still in its early stages. We have skillfully combined 3,150 photos of crop illnesses for three different types of crops using a variety of tried-and-true techniques. This study's main goals are to do a qualitative examination of infection detection algorithms and to offer additional details about the proposed work's possible uses in intelligent farming.

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References

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Published

27.10.2023

How to Cite

Deepak, A. ., Sharma, K. ., Naik, G. R. ., Shah, D. U. ., Modi, G. ., Poonguzhali, S. ., & Singh, D. P. . (2023). Image Processing based Robotic Car for Agricultural Ploughing using Machine Learning Approach. International Journal of Intelligent Systems and Applications in Engineering, 12(2s), 718–724. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3854

Issue

Vol. 12 No. 2s (2024)

Section

Research Article

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