Open AccessML Methods for Crop Yield Prediction and Estimation: An Exploration
Author(s) -
M. Alagurajan,
C. Vijayakumaran
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.c5775.029320
Subject(s) - yield (engineering) , estimation , machine learning , set (abstract data type) , computer science , process (computing) , agricultural engineering , position (finance) , artificial intelligence , crop , crop yield , crop cultivation , outcome (game theory) , statistics , mathematics , engineering , agriculture , agronomy , geography , economics , finance , archaeology , biology , materials science , systems engineering , mathematical economics , metallurgy , programming language , operating system
Machine learning Has performed a essential position within the estimation of crop yield for both farmers and consumers of the products. Machine learning techniques learn from data set related to the environment on which the estimations and estimation are to be made and the outcome of the learning process are used by farmers for corrective measures for yield optimization. This paper we explore various ML techniques utilized in crop yield estimation and provide the detailed analysis of accuracy of the techniques.