Crop price and yield prediction using data science technique

Ritwik Saxena; J. Selvin Paul Peter; Shwetha Saxena; Anubhav Sapra

doi:10.53730/ijhs.v6nS3.6953

Authors

Ritwik Saxena Dept of Computer Science and Engineering, SRM Insitute of Science and Technology, Kattankulathur
J. Selvin Paul Peter Associate Professor, SRM Insitute of Science and Technology, Kattankulathur
Shwetha Saxena Assistant Professor, Amity Business School, MP
Anubhav Sapra Dept of Computer Science and Engineering, SRM Insitute of Science and Technology, Kattankulathur

Abstract

Agriculture is primarily responsible for increasing the state's economic contribution around the world. The most significant agricultural fields, however, remain underdeveloped because of the absence of ecosystem control technology adoption. Crop output is not improving as a result of these issues, which has an impact on the farm economy. As a result, the plant yield prediction helps to support the growth of agricultural productivity. To address this issue, agricultural industries must use machine learning algorithms to forecast crop yield from a given dataset. In order to capture various pieces of information, the supervised machine learning technique must be employed to analyse the dataset. Some examples include variable identification, uni-variate analysis, bi-variate and multi-variate analysis, missing value treatments, and so on. A comparison of machine learning algorithms was performed to see which one was the most accurate at forecasting the most basic crop. The results reveal that the effectiveness of the proposed machine learning algorithm technique is commonly compared to the best accuracy using entropy calculation, precision, recall, F1 Score, sensitivity, and specificity.

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