Precision Agriculture Using Artificial Intelligence & Machine Learning Techniques

Authors

  • Radhakrishnan K.R Department of Computer Science & Engineering, KCG College of Technology, Chennai, India
  • KohilaKanagalakshmi.T Department of MCA(BU), Dayananda Sagar Institutions, Bangalore, India
  • Agarwal M Department of Computer Applications, Dayananda Sagar Institutions, Bangalore, India

Keywords:

Machine learning, Soil nutrient, Deep learning, Fertilizers

Abstract

Many sensors have emerged for different applications nevertheless only rare of the sensor are in use for agriculture field to identify soil type and nutrients specifications this provides a vast space in research. Numerous agricultural research centers are developed and are still on work as an equipped lab for monitoring these data for farmer’s necessity. Getting soil from farmers processing in the lab and resulting in the required data is a common feature but realistic field monitoring sensors are a challenging task. This framework is to develop an easy man - handle sensor for identifying parameters such as: type of the soil, water scarcity, amount of nutrient present in the soil, type of seed for plantation, fertilizer required for the growth of crop, type of diseases that may infect, crop harvesting and cost estimation after cultivation. Classification of these substantial parameters are made using machine learning techniques and to correlate each parameter with its corresponding attributes to provide continuous field monitoring effective precision agriculture is the proposal work. This work focuses on all the parameter fixed together to a sensor listing out the production and cost estimation of any field.

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Published

2025-11-25

How to Cite

[1]
K. Radhakrishnan, T. KohilaKanagalakshmi, and M. Agarwal, “Precision Agriculture Using Artificial Intelligence & Machine Learning Techniques”, Int. J. Comp. Sci. Eng., vol. 7, no. 9, pp. 52–55, Nov. 2025.

Issue

Vol. 7 No. 9 (2019): IJCSE Special Issue April Edition

Section

Survey Article

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