###### Abstract  
Water is essential for agriculture in many world regions and for achieving sustainability in production systems. Maximizing net returns with the available resources is significant, but doing so is a complex problem, owing to the many factors that affect this process. In this study, a decision support system (DSS) incorporating a crop planning model is developed for identifying optimal cropping plans and irrigation management. The model estimates crop yield, production, water requirement, and net income. In this system, the Simulated Annealing algorithm (SA) is used as an optimization tool inside the DSS developed, and the result is as robust as the exact solution with higher computational efficiency. From the model applied, it is found that the current crop pattern and water distribution plan used in the study area should be improved. The computational analysis also found that of the five plans proposed, three plans could produce the highest generated income. On contrary, the current strategy used by Tak's province farmer has the lowest generated income. This result shows that if a better-designed and more efficient crop planning method was, should be used instead. optimization; crop planning; water management; decision support system; simulated annealing +
Footnote †: journal: Applied Sciences  
Agricultural sectors are influenced by a number of factors. Climate change, water availability, soil fertility, and economic variables all have an impact on the agriculture sector [17]. Seasons, as one of the contributing components, have a significant impact on agricultural crop production. A Decision Support System (DSS) can offer a list of suggestions that could assist farmers as decision-makers to achieve better performances with available information. Some existing DSS utilized mathematical optimization in agriculture and concentrated on a single sort of analysis, such as water distribution [16; 18; 19], land assignment [20; 21], weather and climate forecast [22; 23], co-learning process in agricultural sector [24], etc. DSS takes in different kinds of information and processes them with models and data to come up with specific recommendations. DSSs can also be designed to support farmers and related parties [22], such as government and farm advisors, based on data availability. However, it is important that the DSS involved stakeholders effectively. The involvement could happen by using simplified models to make informed and timely decisions [25]. Thailand's agriculture sector provided up to 12 percent of the country's Gross Domestic Product (GDP) and employed over 30 percent of the country's whole workforce [26; 27]. Rice, cassava, rubber, sugar, corn, and palm oil accounted for around 75 percent of the market value of Thai agriculture. There are currently 23.9 million hectares of cultivable land in use, of which 68 percent is for field crops [28]. Despite the fact that agriculture can employ a substantial proportion of the labor force, it has the lowest value-added per worker compared to other industries. According to United Nations Thailand, there are still a number of challenges that must be addressed in order to improve the performance of Thailand's agricultural industry, including inefficiencies in small-plot farming, a lack of current technology, and a lack of awareness of contemporary farming techniques [29]. Since most crop farmers are smallholders, it's possible that the limited resources are not being shared well, which could lead to lower crop productivity. As Thailand has a climate, it only rains about 70 mm on average during the hot season from March to April and 19 mm on average during the cool season from November to February [30]. In the rainy season from May to October, the lowest precipitation is 149 mm and the greatest is 279 mm [30]. Given the limited supply of water, it is necessary to implement a water management and irrigation system to ensure that the crop receives the right amount of water.