Jie Chen
ABSTRACT
With the development of intelligent control technology in the field of agriculture, the research of intelligent irrigation decision control has been widely concerned by scholars. Soil moisture, crop physiology and environment all have important influence on the precision of irrigation decision, and the influence is nonlinear and fuzzy. Therefore, it is difficult to build an accurate mathematical model to abtain an accurate irrigation scheme. How to combine the crop with other influencing factors, use sensor technology and intelligent control technology to build an intelligent irrigation decision-making system is an urgent problem need to be solved in irrigation decision-making. This article presents a comprehensive review of irrigation decision control from the aspects of expert system, fuzzy control and neural network. And pointing out the problems that need to be solved by various intelligent methods in irrigation decision making.
- Yan Hua. 2016. Research and implementation of irrigation decision system for typical crop facility agriculture [D]. China Agricultural University,2016.Google Scholar
- Zhao Lei. Research and development of greenhouse intelligent irrigation system based on cloud platform [D]. Lanzhou University of Technology,2020.Google Scholar
- Cai Jiabin, Zhang Baozhong, Liu Yu. Precision irrigation decision and control technology [J]. Water conservancy in China,2016(09):66-67.Google Scholar
- Xu Chen, Yan Weiping, sun Ning, Liu Xiaolong, Zhao Hongxiang, Tan Guobo, Wu Zhihai, Zhang Jiangan, Zhang Lihua, Bian Shaofeng. Effects of different irrigation treatments on physiological characteristics of spring maize [J]. Journal of irrigation and drainage, 2021,40 (01): 7-14Google Scholar
- Nurul Fahmi, Eko Prayitno, Siti Fitriani. Web of Thing Application for Monitoring Precision Agriculture Using Wireless Sensor Network[J]. Jurnal Infotel, 2019, 11(1):22-28.Google ScholarCross Ref
- Francesco Morari, Luigi Giardini. Irrigation automation with heterogeneous vegetation: the case of the Padova botanical garden[J]. Agricultural Water Management,2002,55(3).Google Scholar
- Cai Jiabing, Bai Liangliang, Xu Di, Li Yinong, Liu Yu. Remote sensing retrieval of surface temperature in irrigation area based on verification of ground infrared detection system [J]. Journal of agricultural engineering, 2017,33 (05): 108-114.Google Scholar
- Cao Yuanjun, Wang Xinzhong. Research on wireless sensor network irrigation system based on crop canopy temperature change [J]. Agricultural Mechanization Research, 2010,32 (09): 126-129Google Scholar
- Tomas Poblete, Samuel Ortega-Farías, Dongryeol Ryu. Automatic Coregistration Algorithm to Remove Canopy Shaded Pixels in UAV-Borne Thermal Images to Improve the Estimation of Crop Water Stress Index of a Drip-Irrigated Cabernet Sauvignon Vineyard[J]. Sensors, 2018, 18(2):397-397.Google ScholarCross Ref
- Charturong Chansetis, Yutaka Shinohara. Et al. Effect of optimization of fertigation management on growth, yield, nitrate and water use efficiency in tomato bag culture based on integrated solar radiation and vapor pressure deficit values[J]. Environment Control biolony., 2005,43(1):13-20.Google Scholar
- Hublet A, Bacquer D D, Valimaa R, Development of the Fertigation Control Based on Cumulative Solar Radiation to Decrease the Nitrate Concentration in Spinach[J]. Hort.res, 2007, 6(2):189-193.Google ScholarCross Ref
- Liu Hao, Duan aiwang, sun Jingsheng, Ning Huifeng, Wang Feng. Evaluation of water saving and conditioning irrigation scheme for greenhouse tomato [J]. Journal of drainage and irrigation mechanical engineering, 2014,32 (06): 529-534 + 540.Google Scholar
- Gu Zhe, Yuan Shouqi, Qi Zhiming, Wang Xinkun, Cai bin, Zheng Zhen. Real time precision irrigation decision and control system for solar greenhouse based on ET and water balance [J]. Journal of agricultural engineering, 2018,34 (23): 101-108Google Scholar
- Qin Huaibin, Li Daoliang, Guo Li. Development and key technology application progress of Agricultural Internet of things [J]. Agricultural Mechanization Research, 2014,36 (04): 246-248 + 252Google Scholar
- Ge Wenjie, Zhao Chunjiang. Research and application status and Development Countermeasures of Agricultural Internet of things [J]. Journal of agricultural machinery, 2014,45 (07): 222-230 + 277.Google Scholar
- Alexandros Kaloxylos, Robert Eigenmann, Frederick Teye, Zoi Politopoulou, Sjaak Wolfert, Claudia Shrank, Markus Dillinger, Ioanna Lampropoulou, Eleni Antoniou, Liisa Pesonen, Huether Nicole, Floerchinger Thomas, Nancy Alonistioti, George Kormentzas. Farm management systems and the Future Internet era[J]. Computers and Electronics in Agriculture,2012,89.Google Scholar
- Yu Guoxiong, Wang Weixing, Xie Jiaxing, Lu Huazhong, Lin Jinbin, Mo Haofan. Litchi garden information acquisition and intelligent irrigation expert decision system based on Internet of things [J]. Journal of agricultural engineering, 2016,32 (20): 144-152Google Scholar
- Ma Shengli. Research on intelligent irrigation system based on Grey Theory and fuzzy control [D]. Shaanxi University of science and technology, 2013Google Scholar
- Zhang min. research on melon disease and pest diagnosis and treatment expert system based on fuzzy theory [J]. Anhui Agricultural Sciences, 2011,39 (24): 15170-15171 + 15174Google Scholar
- Xiao Guiyun, Qin Shuwen. Development of greenhouse weed identification expert system based on fuzzy reasoning in Bashang area [J]. Agricultural Mechanization Research, 2012,34 (05): 188-190 + 195.Google Scholar
- Chu Liping, Yang maishun, Liu Xiaodong, Zhang Lixia, Zhang Xiaodi. Inference mechanism of Wheat Water Saving Expert System [J]. Computer engineering and application, 2004 (12): 216-219.Google Scholar
- Liu Dong, Zhang Changming. Decision algorithm of irrigation time based on fuzzy logic system [J]. Water saving irrigation, 2018 (10): 74-77.Google Scholar
- Wang Jian, Xie Nan, Huang Chunying. Greenhouse grape planting irrigation algorithm based on fuzzy control theory [J]. Jiangsu Agricultural Sciences, 2017,45 (14): 184-188.Google Scholar
- Tian Qiang Ming, Wen Zong Zhou, Li Li Min, Zhang Shun Feng. Irrigation time decision based on apso-elm and fuzzy logic [J]. China Rural Water Conservancy and hydropower, 2020 (04): 124-128.Google Scholar
- Li Bowen. Design and implementation of agricultural irrigation control and management system based on Internet of things [D]. University of information engineering, strategic support force, 2019.Google Scholar
- Zhao Lei. Research and development of greenhouse intelligent irrigation system based on cloud platform [D]. Lanzhou University of technology, 2020Google Scholar
- Yang Hao. Design of intelligent irrigation system based on BP neural network and fuzzy control [D]. Anhui University of science and technology, 2019.Google Scholar
- Qiao Wenwen, Wu Chen, Ma Tian. Research on water saving irrigation model based on fuzzy neural network [J]. Computer and digital engineering, 2019,47 (07): 1618-1621Google Scholar
- Li zaopeng, Li Wenzhu, Liu Jingran, Liu Xin. Large scale greenhouse water saving irrigation system based on Lora and GA-BP [J]. Renmin Changjiang River, 2020,51 (07): 225-230.Google Scholar
- Jiang xianqun, Chen Wufen. Comparison of BP neural network and GA-BP crop water demand prediction model [J]. Journal of drainage and irrigation machinery engineering, 2018,36 (08): 762-766.Google Scholar
- Liu Xiaoyan. Agricultural irrigation prediction based on Grey Relational Analysis and BP neural network [J]. Practice and understanding of mathematics, 2020,50 (08): 287-291.Google Scholar
- Wu Guowan. Research on the construction of smart irrigation system based on big data [J]. Automation and instrumentation, 2021 (02): 148-152Google Scholar
- Wang Lei. Research on intelligent irrigation decision-making mechanism based on crop water field data mining [D]. Northwest University of agriculture and forestry science and technology, 2020Google Scholar
Index Terms
- Review of Research on Irrigation Decision Control
Index terms have been assigned to the content through auto-classification.
Recommendations
A Decision Support System for irrigation water allocation along the middle reaches of the Heihe River Basin, Northwest China
To improve the water resource management of the inland river basins of northwestern China, a Decision Support System (DSS) is developed to provide an operative computer platform for decision makers. The DSS is designed according to actual water resource ...
Optimizing Irrigation Efficiency using Deep Reinforcement Learning in the Field
Agricultural irrigation is a significant contributor to freshwater consumption. However, the current irrigation systems used in the field are not efficient. They rely mainly on soil moisture sensors and the experience of growers, but do not account for ...
Intelligent IoT-multiagent precision irrigation approach for improving water use efficiency in irrigation systems at farm and district scales
Highlights- An Intelligent IoT precision irrigation approach was implemented at farm and district scales.
AbstractThe fourth industrial revolution in agriculture seeks the automation of traditional practices, using modern smart technologies. Advances in electronics, computation and the internet of things are integrated for improving field inputs ...
Comments