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Evapotranspiration of important indicator for management and planning of water resources. It is essential to analyze the evapotranspiration in order to improve water resources planning. The main goal of the study was to analyze the evapotranspiration based on several input parameters. It is important to estimate the influence of the input parameters on the evapotranspiration. For such a purpose evolutionary algorithm was applied. The algorithm applied in this article has space solution of genetic programs. Therefore this methodology is known as genetic programming. The input parameters in the model are monthly minimum and maximum air temperatures, sunshine hours, actual vapour pressure, minimum and maximum relative humidity and wind speed. Results presented in this study could be used for practical application of water resources planning and management based on the input parameters influence on the evapotranspiration. [ABSTRACT FROM AUTHOR]

Water cycle shows the continuous movement of water above and below surface. The water moves could involves the energy exchange which can lead to temperature changes. It is crucial to elaborate the energy exchange in relation to climate changing. Evapotranspiration is one of the most important part of the water cycle. Evaporation presents the water movement to the air and transpiration presents the water movements within a plant. Since the evapotranspiration is very important parameter for climate change, in this article the main aim was to estimate the evapotranspiration based on different climatic parameters such as air temperature, vapor pressure and humidity. Neuro-fuzzy approach was used for the process modeling since the evapotranspiration is very unpredictable factor with strong fluctuation through year. The results could be used for evapotranspiration estimation based on the climate data in order to improve water resources management for agricultural production and irrigation scheduling. [ABSTRACT FROM AUTHOR]

Today’s world depends largely on information and communication technologies. These technologies are in use in different areas of human life and work. Each day, more examples of possible application of information and communications technology are being discovered. In most cases, computer science is used to solve complex problems which have mathematical background. The most important and challenging job in agriculture is plant protection. This is due to its complexity and the lack of specialized tools that could predict when the conditions for specific infections are fulfilled. In this paper authors use different mathematics-based techniques for data processing and prediction of possible fruit disease infection. Six significant weather variables and one variable representing the month in the year are selected as predictor variables. Implemented techniques are compared with each other in order to select the best. Prediction includes two most important diseases of cherry fruit: monilinia laxa and coccomyces hiemalis . Data sets used in this research include data of eight year time period, collected over the region of Toplica in Serbia. The best achieved prediction accuracy is 95.8%. Additionally, the same implemented methods can be applied on other fruit species and other diseases for which data are known. [ABSTRACT FROM AUTHOR]

Highlights • Effective chemical protection of fruit is a complex task. • It is important to automate and optimize the fruit protection process. • Different data mining techniques can be used to identify pattern diseases. • To prevent the excessive use of chemicals. • Results shown that the prediction accuracy is 89%. Abstract Effective chemical protection of fruit is a complex task which can enable production of healthy food without chemical residues. As food health is a growing global concern, it is important to automate and optimize the fruit protection process. Different data mining techniques can be used to identify pattern diseases so as to prevent the excessive use of chemicals. However, application of data mining systems in this field is very complex task. Besides that, these systems are often designed for just one specific plant species. One solution for prediction a risk of fruit infection based on data that represent weather (meteorological) conditions and data pathogens is presented in this paper. The research is performed on the data collected at the region of Toplica in Republic of Serbia during five year period. In this paper data mining based tool for early prediction of fruit pathogen infection is performed. The application is based on the open source engine WEKA with GUI created in C#, and uses several data mining algorithms which are evaluated in this paper. Results shown that the prediction accuracy is 89%. [ABSTRACT FROM AUTHOR]