No abstract

No abstract

Potato growth, yield, and quality under improved irrigation methods and non-uniformity of their irrigation applications are important to enhance water management in arid regions. A field experiment was conducted in 2014 spring and fall growing seasons using potato (Solanum tuberosum) grown in northern Egypt at Shibin El Kom, Menofia, Egypt to evaluate potato response to furrow or trickle irrigation. A Randomized Split-Plot Design with irrigation method randomly distributed and non-uniformity of irrigation applications evaluated along either irrigation furrow or trickle lateral as dependent variables measured at the 3 rd , 13 th , 23 rd , 33 rd , 43 rd and 53 rd m along the 55 m irrigation line. Traditional (TF) and partial (PF) furrows as well as trickle point (TP) and line (TL) sources were used as irrigation methods. Each treatment was repeated three times. For a 33 rd m treatment, seasonal optimum water use by potato was 328, 234, 269 and 292 mm over 118 days in spring and 200, 164, 178 and 186 mm over 122 days in fall under TF, PF, TP and TL irrigation methods, respectively. Potato tuber yield and quality were significantly affected by growing season (S), irrigation method (I) and non-uniformity of irrigation application (U). Tuber yield, total soluble solid (TSS) and leaf area index (LAI) were significantly affected by I and U, and their interaction I * U; harvest index (HI) was not affected by I but U. Except for TSS by S * I and HI by U * I and S * I, results showed no significant differences. Moreover, tuber weight, number and marketable yield were significantly affected by S, I, U and I * U interaction, except medium tuber size and culls by S. A given 33 rd treatment under partial furrow and trickle irrigation, relative to that of traditional furrow, enhanced tuber yield and improved quality in both growing seasons. In non-uniform irrigation application over two growing seasons, potato crop response was developed under varied irrigation methods. Tuber yields were significantly affected in a linear relationship (r 2 ≥ 0.75) by either water deficit or excessive water under irrigation methods.

No abstract

The general objective of this study is to develop and assess the performance of a moving machine, energy battery, and pumping mechanism intended for a solar energy-powered spraying machine. To achieve this aim, a spraying machine was developed and evaluated powered by solar energy via a flexible solar panel system. The development and evaluation process of the spraying machine was done in different steps. The first step was to build and develop the portable unit with remote control unit and the power system. In the second step, the spraying system was hydraulically evaluated under different nozzle types (the full-cone and the Hollow-cone), and nozzle heights by measuring the nozzle distribution efficiency and CV. The third step was to evaluate the spraying machine unit under field conditions. The results of hydraulic evaluation affirmed that, the greatest worth of distribution efficiency was 68.09% with CV% of 3.7% under 60 cm nozzle height and the Hollow-cone single nozzle. The obtained results from the solar panel system showed that, the maximum irradiance was 7783 W m -2 day -1 in the summer. The theoretical performance rate was 0.25 (ha h -1 ) with the operation width of 0.75m and machine velocity of one m s -1 . The daily theoretical performance rate was 1.82, 1.5, and 1.1 ha day -1 . The actual daily performance rate in the spring season was 1.2 ha day -1 with 80% of the machine field efficiency. Therefore, the energy produced from solar panels could a good alternative source for the energy consumption in the smallholding's agricultural machines.

No abstract

Agriculture is closely associated with food and water. Agriculture is the first source of food but the biggest consumer of freshwater. The population is constantly increasing. Smart agriculture is one of the means of achieving food and water security. Smart agriculture can help improve water management and increase agricultural production, thus counteracting rapid population growth requirements. Soil moisture estimation is a critical step in agricultural water management. Soil moisture measurement techniques in situ are point measurements, labor-intensive, time-consuming, tedious, and expensive. We propose, in this research, a new approach to predict soil moisture over vegetation-covered areas from Sentinel-2 images based on a convolutional neural network (CNN). CNN architecture (3) consisting of six convolutional layers, one pooling layer, and two fully connected layers has achieved the highest prediction accuracy. Three well-known criteria including coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) are utilized to measure the accuracy of the proposed algorithm. The Red Edge 3, NIR, and SWIR 1 are the most appropriate Sentinel-2 bands for retrieving soil moisture in vegetation-covered areas. Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI) are the best indicators. The use of the indicator is more proper than the use of the single Sentinel-2 band as input data for the proposed CNN architecture for predicting soil moisture. However, using combinations “that consist of some number of Sentinel-2 bands” as input data for CNN architecture is better than using each indicator separately or all of them as a group. The best values of the performance metrics were achieved using the sixth combination (R2=0.7094, MAE=0.0277, RMSE=0.0418) composed of the Red, Red Edge 1, Red Edge 2, Red Edge 3, NIR, and Red Edge 4 bands as input data to the CNN architecture (3), as well as by using the fifth combination (R2=0.7015, MAE=0.0287, RMSE=0.0424) composed of the Red Edge 3, NIR, Red Edge 4, and SWIR 1 bands.

No abstract