Machine Learning based Crop Recommendation System for Local Farmers of Pakistan

Authors

  • Sayed Mazhar Ali
  • Bhagwan Das
  • Dileep Kumar

DOI:

https://doi.org/10.47059/revistageintec.v11i4.2613

Abstract

Farming is one of the most fundamental and generally rehearsed work in Pakistan and it plays an imperative part in fostering the country. In Pakistan, the most part of the land is used for agriculture cultivation to meet the desires of nearby people and export want as properly. Therefore, the need of increasing crop production is the significant challenge for farmers. Crop cultivation anywhere in the world depends on the climate so called seasons and soil properties, however, the enhancing the production of crops depend on various factors like mainly on temperature. In order to address the issue of increasing crop production for Pakistan, a crop recommendation system is proposed in this work. In this work, idea of ideal harvest prior to planting it, it would be of extraordinary assistance to the farmers and others required to settle on fitting choices on upgrading the creation of yields for neighborhood utilization needs and may prompt the capacity and expanded fare choice for business. Our framework utilized Machine Learning procedures with the end goal that it proposes the appropriate corps dependent on the temperature. This framework subsequently diminishes the monetary misfortunes looked by the farmers brought about by establishing the ominous harvests and furthermore it gives the information on the occasional characterization of yields what harvest is reasonable for which season. It is concluded that proposed algorithm has an average accuracy of 90% on the given dataset. The achieved accuracy is more in comparison to existing work.

Downloads

Published

2021-10-18

Issue

Section

Articles