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Crop Recommendation using Machine Learning Techniques
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b1106.1292s19
Subject(s) - agriculture , agricultural engineering , field (mathematics) , crop , productivity , precision agriculture , computer science , interdependence , quality (philosophy) , crop yield , crop productivity , set (abstract data type) , machine learning , mathematics , engineering , agronomy , geography , economics , philosophy , macroeconomics , archaeology , epistemology , political science , law , pure mathematics , biology , programming language
Precision agriculture (PA) allows precise utilization of inputs like seed, water, pesticides, and fertilizers at the right time to the crop for maximizing productivity, quality and yields. By deploying sensors and mapping fields, farmers can understand their field in a better way conserve the resources being used and reduce adverse affects on the environment. Most of the Indian farmers practice traditional farming patterns to decide crop to be cultivated in a field. However, the farmers do not perceive crop yield is interdependent on soil characteristics and climatic condition. Thus this paper proposes a crop recommendation system which helps farmers to decide the right crop to sow in their field. Machine learning techniques provide efficient framework for data-driven decision making. This paper provides a review on set of machine learning techniques to support the farmers in making decision about right crop to grow depending on their field’s prominent attributes.

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