Journal
INTELLIGENT COMPUTING AND COMMUNICATION, ICICC 2019
Volume 1034, Issue -, Pages 495-501Publisher
SPRINGER-VERLAG SINGAPORE PTE LTD
DOI: 10.1007/978-981-15-1084-7_47
Keywords
Finite state machine; Irrigation system; Machine learning; Soil texture; Water use efficiency (WUE)
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Irrigation practices can be upgraded by the aid of finite statemachines and machine learning techniques. The low water use efficiency (WUE) is the universal problem encountered by the existing irrigation systems. The finite automata model provides an efficient irrigation system with input features such as soil properties, crop coefficient, and weather data. The K-Nearest Neighbor (KNN) algorithm predicts crop water requirement based on crop growth stage with accuracy of 97.35% and for soil texture classification with accuracy of 93.65%. The proposed irrigation automation model improves water productivity.
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