Title: Prediction of the right crop for the right soil and recommendation of fertiliser usage by machine learning algorithm

Authors: P.E. Rubini; P. Kavitha

Addresses: Department of Computer Science and Engineering (VTU RC), CMR Institute of Technology, Bengaluru, Karnataka, India ' Department of Computer Science and Engineering (VTU RC), CMR Institute of Technology, Bengaluru, Karnataka, India

Abstract: Crop production is a crucial aspect of farming and it depends on many factors like soil nutrients, fertiliser usage, water resources, etc. The critical factor for effective agriculture is soil. The composition of soil varies from one land to another which muddles the farmers to choose the appropriate crop for their farmland. The proposed study focuses on recommending the right crop for the right soil and also signifies the required composition of fertiliser. Nonetheless, the work requires analysis of a huge volume of data which can be accomplished by applying five machine learning techniques and to enhance the accuracy and precision in the prediction of the crop, the solution of all these algorithms is integrated into a proposed model through ensemble learning which provides the aggregated output i.e., the recommended crop and fertiliser dosage. The intention of the proposed model is to improve farmers' growth by increasing their productivity and profit.

Keywords: agriculture; machine learning; fertiliser usage; ensemble learning; prediction; crop productivity.

DOI: 10.1504/IJCAT.2022.126885

International Journal of Computer Applications in Technology, 2022 Vol.69 No.2, pp.163 - 172

Received: 12 May 2021
Accepted: 14 Sep 2021
Published online: 11 Nov 2022 *

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