Irrigation System Automation Using Finite State Machine Model and Machine Learning Techniques

被引：0

作者：

Pradeep, H. K. ^{[1
]}

Jagadeesh, Prabhudev ^{[2
]}

Sheshshayee, M. S. ^{[3
]}

Sujeet, Desai ^{[4
]}

机构：

[1] Visvesvaraya Technol Univ, JSS Acad Tech Educ, Bangalore, Karnataka, India

[2] JSS Acad Tech Educ, Bangalore, Karnataka, India

[3] Univ Agr Sci, GKVK, Bangalore, Karnataka, India

[4] Indian Council Agr Res, Cent Coastal Agr Res Inst, Goa Velha, Goa, India

来源：

INTELLIGENT COMPUTING AND COMMUNICATION, ICICC 2019 | 2020年 / 1034卷

关键词：

Finite state machine; Irrigation system; Machine learning; Soil texture; Water use efficiency (WUE);

D O I：

10.1007/978-981-15-1084-7_47

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：495 / 501

页数：7

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