Location: Sustainable Water Management ResearchTitle: A real-time fuzzy decision support system for alfalfa irrigation
|LI, MAONA - Chinese Agricultural University|
|YANGYANG, MENG - Chinese Agricultural University|
|YAN, HAIJUN - Chinese Agricultural University|
Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/23/2019
Publication Date: 7/2/2019
Citation: Li, M., Sui, R., Yangyang, M., Yan, H. 2019. A real-time fuzzy decision support system for alfalfa irrigation. Computers and Electronics in Agriculture. 163:104870.
Interpretive Summary: Irrigation is critical for alfalfa production due to its extravagant water use. Irrigation decision support system (IDSS), a fundamental tool for precision irrigation, can provide scientific and reasonable suggestions for efficient irrigation management. The researcher at USDA-ARS Crop Production Systems Research Unit in Stoneville, MS with collaborators in China developed an irrigation decision support system based on the combination of soil water, alfalfa height and forecast weather data, provided a software for real-time irrigation management for alfalfa production. The results showed that the predictive models in the IDSS had a good performance in estimating alfalfa phenophases and soil water. The IDSS was able to generate the final timing and amounts of irrigation and could achieve a promising performance in facilitating alfalfa irrigation and harvest management.
Technical Abstract: An irrigation decision support system is a critical component in precision water management. In this study, a real-time irrigation decision support system (IDSS) based on a fuzzy inference system and an equipped software were developed and evaluated for irrigation management in alfalfa. A comprehensive model with inputs of soil water, alfalfa growth and weather conditions was proposed. On the basis of soil moisture and difference of alfalfa height, the IDSS determined the most appropriate irrigation action to ensure maintaining the soil moisture above a pre-defined value. In the fuzzy inference system, all variables were fuzzified using triangular membership functions. In the fuzzification, max–min inference and Rule-based Mamdani-type fuzzy modeling were adopted in order to generate the amount of irrigation. Using weather forecast, incorporating with the models of alfalfa growth and soil water, the IDSS provided users predictions of alfalfa phenophases, height, and soil water status. The tested results showed that the predictive models had a good performance in estimating alfalfa phenophases and soil water with NRMSE of 8.28% and 6.29%, respectively. The IDSS produced the final timing and amounts of irrigation in combination of irrigation efficiency of the alfalfa irrigated system. The test in Zhuozhou, Hebei Province, China indicated that this IDSS could achieve a promosing performance in facilitating alfalfa irrigation and harvest management.