Smart Computing- Book ISBN- 978-81-953658-1-4

Authors:- Jigisha Patel, Brijesh Vala, Makhduma Saiyad

Abstract :-Since ancient times, agriculture is one of the most critical occupations in India. India is considered the world’s second-largest in outputs. Agriculture is the most critical sector and plays a vital role in the Indian economy, which contributes around 17% of total GDP and 60% of the entire workforce, according to the assessment of 2018. Different seasonal, economic and biological patterns affect crop production, but sudden changes in these patterns lead to a significant loss to farmers. Crop yield prediction is a fundamental issue in agriculture. By analysing various relevant factors, like location, crop type, PH value of soil, type of soil, percentage of nutrients like Phosphorous (P), Nitrogen(N), Potassium(K), humidity, temperature and amount of rainfall, we can forecast crop yield using Machine Learning. Different approaches have been used to produce models and interpret final effects, such as Regression, K-means, Decision Trees, Random Forests, Support Vector Machines, Bayesian Networks, Artificial Neural Networks, etc. Such approaches help analyse the environment, soil and water processes heavily involved in crop growth and precision farming. An overview of some of the latest supervised and unsupervised machine learning models linked to crop yield in literature is highlighted in this survey paper.

Keywords: - Crop yield Prediction, Machine Learning, Agriculture, Classification, Regression