Optimization of Irrigation and Herbicides Using Artificial Intelligence in Agriculture

Authors

  • Atluri Vani Vathsala Professor, Department of Computer Science and Engineering, CVR College of Engineering, Hyderabad, India.
  • Lakshmi H. N. Professor, Computer Science and Engineering (AIML, CS and DS), CVR College of Engineering, Hyderabad, India.
  • Gangolu Yedukondalu Associate Professor, Department of CSE, Anurag University, Hyderabad, India
  • Channapragada Rama Seshagiri Rao Department of CSE, Professor & Principal, Vignana Bharathi Engineering College Hyderabad, India.
  • Mahesh Kotha Associate professor, Department of CSE (AI&ML), CMR Technical Campus Hyderabad, India.
  • Ravindra Changala Assistant Professor, IT Department, Guru Nanak Institutions Technical Campus, Hyderabad, India.

Keywords:

Herbicides, pesticides, AI, irrigation, agriculture, soil management, disease management, crop management

Abstract

Technological aspects play a key role in the economy of country. The usage of technologies in various fields makes automation strong. The integration of new technologies for agriculture era gives a great yield. Demand for food with respect to the population is a great deal. Huge population required tremendous food requirement which cannot be possible with the conventional agriculture methods. In this paper we introduced a new method for agriculture with Artificial Intelligence became a new trend set. Our approach saved crop yield from various geological factors. The primary objective of our work was how various AI applications used in the domain of agriculture sector and increases the fertility of the soil. The vase survey we conducted for this paper was helped us for current set ups for the agriculture through weeding, robots and drones. We focused mainly on automated weeding techniques and sensing issues of water of soil. 94% of the pesticides produced are to protect he crop and to increase the production of crop. But this leads many hazardous issues of environments and humans. By using KNN (K-nearest neighborhood) and LRC (Logical Regression Classification) algorithms we got the predicted value of 88.5%.

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Published

16.07.2023

How to Cite

Vathsala , A. V. ., H. N., L. ., Yedukondalu, G. ., Seshagiri Rao, C. R. ., Kotha, M. ., & Changala, R. . (2023). Optimization of Irrigation and Herbicides Using Artificial Intelligence in Agriculture. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 503–518. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3204

Issue

Vol. 11 No. 3 (2023)

Section

Research Article

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