Open AccessReview of Machine Learning Techniques for Crop Recommendation System
Author(s) -
Prof. Meena Ugale,
Nimeesha Venkatavelu,
Pranay Patil,
Suraj Rane
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.40559
Subject(s) - agriculture , precision agriculture , crop , agricultural engineering , crop yield , yield (engineering) , population , business , environmental science , agricultural economics , engineering , geography , agronomy , economics , biology , materials science , demography , archaeology , sociology , metallurgy
The Indian population is highly dependent on agriculture for vegetables, fruits, grains, natural textile fibres like cotton, jute, and many more. Also, the agricultural sector plays a vital role in the economic growth of the country. The agriculture sector is contributing around 19.9 percent since 2020-2021. As a result, agricultural production in India has a significant impact on employment. The soil in India has been in use for thousands of years, resulting in depletion and exhaustion of nutrients and minerals, which leads to a reduction of crop yield. Also, there is a lack of modern applications, which causes a need for precision agriculture. Precision Agriculture, also known as Satellite farming is a series of strategies and tools to manage farms based on observing, measuring, and responding to crop variability both within and between fields. One of the main applications of precision agriculture is the recommendation of accurate crops. It helps in increasing crop yield and gaining profits. This paper aims to review and analyse the implementation and performance of various methodologies on crop recommendation systems. Keywords: Machine Learning, Precision Agriculture, Crop Recommendation System, Classification.