Open AccessA Novel Automating Irrigation Techniques based on Artificial Neural Network and Fuzzy Logic
Author(s) -
Manu Phogat,
Ajay Kumar,
Deepak Nandal,
Jyoti Shokhanda
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1950/1/012088
Subject(s) - irrigation , agricultural engineering , environmental science , deficit irrigation , irrigation scheduling , evapotranspiration , fuzzy logic , water content , computer science , irrigation management , soil water , agronomy , soil science , artificial intelligence , engineering , ecology , geotechnical engineering , biology
India is primarily an agrarian society and it is important to develop various irrigation techniques as the nature of soil varies drastically spatially. Irrigation scheduling are mainly depends upon monitoring of soil, crop, water available and weather conditions. Major limitation of this system is the under-irrigation or over-irrigation, which affects the SAR ratio of soil or excess runoff generation respectively. Effective implementation of irrigation technique is required to enhance the productivity of less fertile soil. Irrigation with variable rate and intelligent control-based system is required for the enhancement of irrigation system and higher crop yield. Study was conducted for the performance evaluation of irrigation system using ANN and Fuzzy Logic Toolbox of MATLAB. Moisture available, rainfall, evapotranspiration and water supply for wheat crop were taken as input and output variable were performance of irrigation system. Results of different irrigation techniques were evaluated and best technique were identified under their specification.