Artificial intelligent provides diverse solutions for the complex problems in agriculture research. The study aimed to use three models of artificial neural networks (Feed Forward Neural Network (FFNN), Generalized Regression Neural Network (GRNN) and Radial-Basis Neural Network (RBNN)) in the field of wheat yield prediction. 27-year data for the period were utilized to improve the models and four-year data (2013 and 2016) were used to estimate the models, to compare their outputs with the measured data. Prediction data was not entered in the process of building neural network models. The results showed that the optimal configuration of the FFNN model consists of 40 neurons in the hidden layer (8-40-1). The Tan Sigmoid activation function was used in both the hidden layer and the output layer using all of these models (anterior neural feeding network and the regression neural network and radial base neural network) in the 4-year wheat yield forecast field for production (2013)(2014)(2015)(2016) by applying 8 input parameters that were result of NMMS (8.6%, 7.6% and 15.7% resp.), To find that FFNN and GRNN provide the best result from BRNN because while the information set was large or in a wide range, then the range data ranges from -1 to +1 (normalization data) , GRNN gives better outcomes after the information or sample data were in large range.

This study investigates the application of artificial neural networks (ANNs) on the prediction of daily grass reference crop evapotranspiration (ET0) and compares the performance of ANNs with the conventional method (Penman-Monteith). The use of ANNs was examined number of hidden layers and the activation function were also tested. The best ANN architecture for estimation of daily ET0 was obtained for different data set for Nubaria. Using these data, the networks were trained with daily climatic data (maximum and minimum temperature, dew point and wind speed) as input and the Penman-Monteith (PM) estimated ET0 as output. The analysis was carried out with MATLAB software. Feed forward onelayer networks with sigmoid function were used. Performance evaluation of the models have been carried out by calculating root mean square error (RMSE), The network were selected based on maximized R and R 2 value and minimized RMSE values which were 0.98, 0.957 and 0.44 mm/day, respectively in testing. The optimal ANN (4-12-1) for Nubaria regions showed a satisfactory performance in the ET0 estimation. These ANN models may therefore be adopted for estimating ET0 in the study area with reasonable degree of accuracy.

Linear programming is used to select systems based on minimization of total annual cost using aeration devices for pond to give optimum oxygen. By linear programming model results, one aerator of the type air-injector (0.75 kW) was found suitable for a pond of sizes 0.1, 0.2, and 0.4 ha by least-cost. Splash aerator (0.75 kW) was found suitable for single use for pond of sizes 0.8 ha by least-cost, and suitable in addition to diesel paddlewheel (9 kW) for large pond size (3.2 ha). Diesel paddlewheel aerators (6 kW and 9 kW) were found suitable for large pond sizes (1.6, 3.2, 4.0, and 4.8 ha). Diesel paddlewheel aerators are suitable for large ponds under Egyptian conditions.

The study aimed to evaluate the performance of the Food and Agriculture Organization "AquaCrop" model (version 6.1) in simulating the productivity and biomass of wheat crops in old lands under the surface irrigation system. Field data for the period from 2013-2016 were used to calibrate the model's through matching productivity and biomass observed using root mean square error (RMSE) 0.05 and 0.2 ton/ha and Nash coefficient values of 0.9 and 0.8, resp. A calibrated model was simulated the grain yield to generate an irrigation schedule with the aim of developing an appropriate irrigation management strategy for wheat. Results showed that the highest value of wheat water production was achieved through the application of five irrigations when applying fixed net application (80 mm) with a total of 400 mm for the season and different interval between irrigation ranges between 30-39 days depended on depilation of the 80% of Readily Available Water (RAW) threshold while taking into rainy mind. This sequence was superior to the normal used irrigation sequence conducted from 2013 to 2016 (fixed net application 80,96mm and fixed interval 27,33 days on six and five irrigations application resp. with a total of 480 mm per season). 16.7% less water use with increasing water productivity is one of the important things at the present time. The results will help determine an irrigation management option appropriate to the prevailing weather conditions and farm resources, and thus this model can be used as a decision support tool in increasing water productivity.

The impact of climate on crop production has vital importance. Climate variables affect the different crops during different stages of the growth and the development. This research aims to study the environmental factors affecting the growth and production of barley (Hordeum Sp., Gramineae) in a hydroponic system, to provide information to farmers and decision makers by using Artificial Neural Network (ANN) Model for production prediction. Multilayer feed-forward ANN (fully connected) was used in supervised manner and the training method was the back-propagation algorithm by using MATLAB program. The inputs in the ANN model of barley were: seeds density (kg/m 2), lighting duration (h/day), light intensity (Lux), temperature (cº), relative humidity (%) and growing period (days). The outputs were: plant length (cm), yield (kg/m 2), protein (%), dry matter (%), and conversion factor. Results revealed that the optimal configuration for the ANN model consisted of four layers (6-25-30-5). The hidden layers had 25 and 30 nodes in the first and second hidden layers respectively for the ANN model. Hyperbolic tangent transfer function was employed in hidden and output layers of the ANN model. The learning rate and the momentum parameter were 0.005 and 0.9 respectively for the ANN model. Iterations were 10000 epochs during training process for the ANN model. The results showed that the variation between target and predicted outputs was small while the correlation coefficient (R) was 0.99. Also, the results revealed that the major parameters affecting on all the outputs were seeds density and the duration of the lighting followed by the other factors i.e. temperature (cº), relative humidity (%), growing period (days) and light intensity (Lux). Seeds density has a higher percent relative importance, on yield, plant length, protein (%), DM (%) and conversion factor equal to 22.8%, 24%, 25%, 24% and 22.8% respectively. The developed ANN model was beneficial tool for barley production prediction. The barley yield prediction could be helpful for farmers, decision makers and planning to manage their crop better by providing a series of recommendations about crops planting and clarifying its impact on changes to these factors under the study in order to avoid losses and reach the best benefit (maximization of yield).