Iraqi greenhouses require an active microcontroller system to ensure a suitable microclimate for crop production. At the same time, reliable and timely Water Consumption Rate (WCR) forecasts provide an essential means to reduce the amount of water loss and maintain the environmental conditions inside the greenhouses. The Arduino micro-controller system is tested to determine its effectiveness in controlling the WCR, Temperature (T), Relative Humidity (RH), and Irrigation Time (IT) levels and improving plant growth rates. The Arduino micro-controller system measurements are compared with the traditional methods to determine the quality of the work of the new control system. The development of mathematical models relies on T, RH, and IT indicators. Based on the results, the new system proves to reliably identify the amount of WCR, IT, T, and RH necessary for plant growth. A t-test for the values from the Arduino microcontroller system and traditional devices for both conditions show no significant difference. This means that there is solid evidence that the WCR, IT, T, and RH levels for these two groups are no different. In addition, the linear, two-factor interaction (2FI), and quadratic models display acceptable performance very well since multiple coefficients of determination (R2) reached 0.962, 0.969, and 0.977% with IT, T, and RH as the predictor variables. This implies that 96.9% of the variability in the WCR is explained by the model. Therefore, it is possible to predict weekly WCR 14 weeks in advance with reasonable accuracy.

Energy is an essential component of meeting social needs and economic growth. The international energy agency (IEA) estimates that a 53% increase in global energy consumption is expected by 2030. Pollution of environment, climate change, and the growing of demand energy worldwide require serious attention. Moreover, fossil fuels will significantly contribute to greenhouse gas emissions from combustion and exacerbate climate change. Renewable energies (REs) such as solar, wind, hydropower, geothermal, and biofuels are the right solution to running out of fossil fuels, protecting the environment, and stopping climate degradation. Many countries have jumped into the field of energy production from photovoltaic panels to reduce dependence on fossil fuels and recorded success stories. This paper reviews the great efforts developed countries and the rest of the world made in investing in solar energy. Especially photovoltaic energy, and compares it with the reality of the situation concerning the neighboring countries of Iraq in general and in Iraq in particular, and shows the determinants of developing this industry and the difficulties it faces, especially in the field of providing electricity to cities and farms. Moreover, providing appropriate solutions based on the success stories achieved by other countries.

This study was conducted in greenhouses belongs to the College of Agriculture and Forestry at the University of Mosul, for the autumn season 2019. The research included adding a ventilation system that included air circulation inside the greenhouse to eliminate excessive moisture and heat, as well as uniformity of the internal environment of the greenhouse. The greenhouse was divided according to Randomize Complete Block Design in a factorial experiment. The study has been divided into five ridges; the three intermediate ridges were divided into 6 blocks. A sampling of replications was taken according to the vegetative indicator of the studied yield. The two Greenhouses were planted with the yield of the cucumber. Dimensions of the greenhouse were area of 396m 2, length of 44m, a width of 9m, and a height of 3.45m. The first greenhouse was chosen to be without mechanical ventilation. As for the second greenhouse, ventilation and air circulation system (three exhaust fans and other five fans for air circulation) have been added, which works automatically using a low-cost controller (Arduino). The purpose of adopting the Arduino is to ensure the operation and shutting down the ventilation system through sensors reading according to the need of the crop, thus saving electrical energy. The greenhouse was not warm and the time for planting was late. The readings were taken for the vegetative growth indicator obtained during the growth and production period of the cucumber. From the results collected, it was found that the growth and yield indicator of the cucumber crop gave the best results in the greenhouse with mechanical ventilation in comparison with natural ventilation. The results obtained showed that growth indicator for the greenhouse with mechanical ventilation were better in terms of growth. The difference was significant in the height of the plant, whereby the average length of plants for the automated house was 191.93 cm. As for the traditional greenhouse the length recorded 131.70 cm. the percentage of chlorophyll in the leaves of the plant was 1.6 % higher. The leaf surface area of the plant in the automated house reached 14070 cm 2 . Plant -1 , whereas in the traditional house, recorded 11281 cm 2 .plant -1 . The total crop yield achieved was 126% higher in the greenhouse with mechanical ventilation in comparison with the greenhouse of natural ventilation.

This study aims to analyse the energy of cucumber production in a greenhouse and examine the application of a multilayer perceptron to predict the productivity of an agricultural region in Nineveh Governorate. The research data were collected from experiments including fuel, fertilisers, pesticides, seeds, workers, electricity, and the number of hours worked in agricultural processes to produce cucumber crops. The results showed that the total energy consumption of the cucumber was 46,432.013 MJ·ha−1, while the output energy was 53,127.727 MJ·ha−1. The fungicide energy consumption, herbicide energy consumption and electricity energy consumption are considered the most critical variable in cucumber plantation procedures; its significance is the relative values of 100%, 99.7% and 93.3%. The impacts of human labour, P fertiliser, diesel fuel and N fertiliser on cucumber operation were 25,725 MJ·ha−1, 548.596 MJ·ha−1, 3,011.178 MJ·ha−1 and 7,244.545 MJ·ha−1, respectively. This research concludes that a multilayer perceptron neural network algorithm helps predict cucumber production and shows that the trained neural network produced minimal errors, indicating that the test model could predict a cucumber crop yield in Nineveh province.