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Abstract-The measurement of evapotranspiration is the most important factor in irrigation scheduling. Evapotranspiration means loss of water from the surface of plant and soil. Evaporation parameters are being used in studying water balances, water resource management, and irrigation system design and for estimating plant growth and height as well. Evapotranspiration is measured by different methods by using various parameters. Evapotranspiration varies with the climate change and as the climate has a lot of variation geographically, the pre-developed systems have not used all available meteorological data hence not robust models. In this research work, a model is developed to estimate evapotranspiration with more authentic and accurate reduced meteorological parameters using different machine learning techniques. The study reveals to learn and generalize the relationship among different parameters. The dataset with reduced dimension is modeled through time series neural network giving the regression value R=83%.

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Abstract-Wheat has been a prime source of food for the mankind for centuries. The final wheat grain yield is the multitude of the complex interaction among the various yield attributes such as kernel per plant, Spike per plant, NSpt/s, Spike Dry Weight (SDW), etc. Different approaches have been followed to understand the non-linear relationship between the attributes and the yield to manage the crop better in the context of precision agriculture. In this study, Principle Component analysis (PCA) and Stepwise regression used to reduce the dimension of the original data to get the critical attributes under study. The reduced dataset is then modeled using the Radial Basis neural network. RBNN provides the regression value more than 0.95 which indicates the strong dependence of the yield on the critical traits.

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