Open AccessImplementation of Machine Learning Technique for Prediction Agriculture Produce
Author(s) -
Miss. Komal K Khandare,
S. R. Gupta
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.2022.41724
Subject(s) - agriculture , productivity , agricultural engineering , crop productivity , crop , decision tree , support vector machine , setback , computer science , precision agriculture , crop yield , rainfed agriculture , yield (engineering) , face (sociological concept) , agricultural economics , machine learning , economics , agronomy , engineering , geography , forestry , economic growth , social science , civil engineering , materials science , archaeology , sociology , metallurgy , biology
In Indian economy and employment agriculture contributes a major role. Probably most common problem faced by the Indian farmers is they do not optimize crop based on the necessity of soil, as a result they face serious setback in productivity. This problem can be addressed through precision agriculture. This method takes three parameters into consideration, viz: soil characteristics, soil types and crop yield data collection based on these parameters suggesting the farmer suitable crop to be cultivated. Precision agriculture helps in reduction of non suitable crop which indeed increases productivity, apart from the following advantages like efficacy in input as well as output and better decision making for farming. Keywords: Crop prediction, Support Vector Machine, Decision tree, NaïveBayes algorithm