Precision farming practices with data-driven analysis and machine learning-based crop and fertiliser recommendation system

¹ Department of Information Technology, GRIET, Hyderabad, Telangana, India.
² Department of Computer Science and Engineering, Chaitanya Bharathi Institute of Technology (A), Gandipet, Hyderabad, Telangana, India.
³ Department of CSE-AI&ML, KG Reddy College of Engineering and Technology, Hyderabad, Telangana, India.
⁴ Radiology Techniques Department, College of Medical Technology, The Islamic University, Najaf
⁵ Department of Mechanical Engineering, New Horizon College of Engineering, Bangalore, Karnataka, India.
⁶ Lovely Professional University, Phagwara, Punjab, India
⁷ Lloyd Institute of Engineering & Technology, Knowledge Park II, Greater Noida, Uttar Pradesh, India.

^* Corresponding author: gurramvijendarreddy@gmail.com

Abstract

Agriculture forms a major occupation in countries like India. More than 75% people rely on farming for their daily wages. Food security on a global scale is mostly dependent on agriculture. Hence, achieving good yield in the crops grown by farmers is the major concern. Various environmental factors have a significant impact on the crop yield. One such component that contributes majorly to the crop yield is soil. Due to urbanization and enhanced industrialization, the agricultural soil is getting contaminated, losing fertility, and hindering the crop yield. One exciting new way to maximise crop yields while decreasing input costs is precision farming, which makes use of machine learning (ML) and the IoT. Machine Learning (ML) is employed for agricultural data analysis. The goal of this research is to optimise agricultural practices by presenting an integrated crop and fertiliser recommendation system. The proposed ML based model “Precision Agriculture” aims at predicting the suitable crops that can be grown based on the class which the soil sample belongs to and suggests the fertilizers that can be used to further enhance the fertility of soil. Using proposed model, farmers can make decisions on which crop to grow based on the soil classification and decide upon the nitrogen–phosphorous– potassium (NPK) fertilizers ratio that can be used. Comparison of the SVM algorithm with Naive Bayes, and LSTM has shown that SVM performed with a higher accuracy. Decision support tools that integrate AI and domain knowledge are provided by the study, which is a substantial contribution to precision agriculture.