Title: An extensive three-tiered architecture for comprehensive crop and fertiliser prediction using supervised learning

Authors: Abhinav Singh Roy; Sarvika Tiwari; Shubham Wawale; Soham Talekar; Pallavi Vijay Chavan

Addresses: Department of Information Technology, Ramrao Adik Institute of Technology, D.Y. Patil (Deemed to be University), Nerul, Navi Mumbai, India ' Department of Information Technology, Ramrao Adik Institute of Technology, D.Y. Patil (Deemed to be University), Nerul, Navi Mumbai, India ' Department of Information Technology, Ramrao Adik Institute of Technology, D.Y. Patil (Deemed to be University), Nerul, Navi Mumbai, India ' Department of Information Technology, Ramrao Adik Institute of Technology, D.Y. Patil (Deemed to be University), Nerul, Navi Mumbai, India ' Department of Information Technology, Ramrao Adik Institute of Technology, D.Y. Patil (Deemed to be University), Nerul, Navi Mumbai, India

Abstract: Agriculture accounts for a fifth of India's GDP, however the research in this field does not reflect this significant contribution. Farming practices remain archaic, with little emphasis on data-driven approaches to maximising yield and profits. Predicting crop yield is crucial for maximising profits in agronomy, with suitable fertiliser selection vital for maintaining soil health. This paper presents an extensive three-tiered architecture for comprehensive crop and fertiliser prediction using historical data with features such as soil pH, moisture, and temperature. The first tier predicts crops based on the area under cultivation and geographical region, with an accuracy of 99.54% using the random forest classifier. The yield for the given crop is predicted using linear regression with an accuracy of 89.57%. The second tier predicts the cost of cultivation, and the third predicts an appropriate fertiliser based on soil nutrients and environmental factors using Naïve Bayes with 100% accuracy.

Keywords: Naïve Bayesian; random forest classification; linear regression; supervised learning; prediction.

DOI: 10.1504/IJAISC.2023.137342

International Journal of Artificial Intelligence and Soft Computing, 2023 Vol.8 No.1, pp.1 - 20

Received: 01 Sep 2022
Received in revised form: 17 Feb 2023
Accepted: 16 Mar 2023
Published online: 13 Mar 2024 *

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