A Support System For Crop Water Requirement Diagnosis And Irrigation Decision Making

A crop water requirement diagnosis and irrigation water requirement decision-makingsupport system was developed which comprised of a data import module, a databasemodule, a model library, a crop water requirement data collection module and diagnosis anddecision-making modules. Depending on the information acquisition method, the systemcan adopt one of the following two modes: online real-time diagnosis and decision makingbetween the monitoring and decision-making systems or offline diagnosis and decisionmaking. Using software technology to jointly use the basic database and the diagnosis anddecision-making model library produces more comprehensive decision-making supportfunctions in the system. The decision-making requirements of different users are supportedby setting different diagnostic and decision-making strategies. The diagnosis anddecision-making support system was developed using Java language, and it passed the casetest. The results from this study provide methods and tools to improve our knowledge ofagricultural irrigation and irrigation water use efficiency. The main research contents areincluded as follows:(1) The model library module summarizes at present commonly used crop waterrequirement diagnosis and irrigation decision making methods. It includes the crop waterrequirement calculation and irrigation water requirement calculation models as well asrelated parameters. The model library can be used for all farmland and greenhouse crops.(2) The database module is primarily intended for storing various types of datarequired for diagnosis and decision making, including data in various formats (text, imageand graph), such as meteorological, crop and soil data, as well as model parameters. Thedatabase module functions are relatively independent and support queries, retrieval, addition,deletion, sorting, printing and other operations.(3) Analysis of the Penman-Monteith equation and water balance equation, I determinewhat elements to detect. According to the monitoring element and system requirements tochoose the appropriate sensor and power supply, I build the hardware system. I develop thedata acquisition software for the accurate, real-time acquisition of data.(4) The three primary types of decision-making strategies are as follows: water-saving benefit, yield-increase/high-quality gains and comprehensive water-saving benefits.(5) The real-time performance, power consumption and accuracy of this system istested for a long time in agricultural environment.