ABSTRACT One of the new crop varieties that have been adopted for high yield is the Egyptian faba bean. However, poor-quality faba bean has reduced economic value. Quality evaluation is thus important and can be performed using computational intelligence. We developed a robust method based on morphological features and artificial neural network for quality grading and classification of Egyptian faba-bean seeds, covering five varieties: Giza3, Giza461, Misr1, Nobarya1, and Sakha1. Fifteen seed morphological features were then calculated, and artificial neural networks classified faba beans into different varieties. The results indicated an overall classification accuracy of 77.5% was achieved in training phase and it was 100% when testing dataset was used. The preliminary work presented in this paper could be further enhanced by real time faba beans identification by capturing seed morphological features through the help of digital images.

Principal component analysis was merged with the artificial neural network (ANN) technique to predict wind drift and evaporation losses (WDEL) from a sprinkler irrigation system. For this purpose, field experiments were conducted to determine WDEL under different conditions. Data from field experiments and previous studies were used as sample data to train the ANN model. Three models were developed to predict WDEL. In the first model (ANN1), 9 neurons (riser height, main nozzle diameter, auxiliary nozzle diameter, discharge rate of the main nozzle, discharge rate of the auxiliary nozzle, operating pressure, wind speed, air temperature and relative humidity) were used as the input layer. In the second model (ANN2), 7 neurons (riser height, operating pressure, wind speed, air temperature and relative humidity, diameter ratio and discharge ratio) were used as the input layer. The third model (ANN3) used a multivariate technique (PC1, PC2, and PC3). Results revealed that the ANN3 model had the highest coefficient of determination (R² = 0.8349). The R² values for the ANN1 and ANN2 models were 0.7792 and 0.4807, respectively. It can be concluded that the ANN3 model has the highest predictive capacity.

ABSTRACT Soil texture and its characteristics besides water characteristics can play an eminent role in potato (Solanum tuberosum L.) production. Therefore, a soil and water quality index (SWQI, %) was derived to investigate the effect of combination of soil and water characteristics (sodium adsorption ratio for water and soil, electric conductivity of water and soil, pH for water and soil, organic matter in the soil, and soil texture index) on yield and properties of ‘Spunta’ potatoes produced under center pivot irrigation system. This index was formed studying separately the effect of irrigation water quality and soil texture on yield, water use efficiency, tuber modulus of elasticity, and tuber shape index of ‘Spunta’ potatoes. Field results demonstrated that the lowest potato yield was approximately 34.12 t ha-1 at SWQI 79.63%, and the highest potato yield was 37.79 t ha-1 at SWQI of 30.93%. The lowest water use efficiency was approximately 6.09 kg m-3 at SWQI 30.93%, and the highest water use efficiency was 6.83 kg m-3 at SWQI 79.63%. The lowest tuber modulus of elasticity was approximately 3.98 N mm-1 at SWQI 21.7%, and the highest tuber modulus of elasticity was 4.74 N mm-1 at SWQI 79.63%. Finally, the tuber shape index was approximately 342% at SWQI 79.63%, 418% at SWQI 21.72%, and 403% at SWQI 30.93%, which belongs to long and very long shapes. The soil and water quality index could be a useful tool to get relationships among water and soil characteristics, yield, and other properties of the potato crop.

Automated irrigation systems (AISs) are critical for the sustainability of irrigated farming systems, considering the present water crisis in Saudi Arabia. This study investigated whether electronic controllers in irrigation systems effectively save water. The study also assessed the effect of these controllers on crop yield using drip and sprinkler irrigation systems in severely arid climate conditions. Evapotranspiration (ET) controllers were installed in experimental fields of wheat (Triticum aestivum) and tomato (Solanum lycopersicum Mill.) crops for 2 successive seasons. The results revealed that the water use efficiency (WUE) and irrigation water use efficiency (IWUE) were typically higher in the AIS than in the conventional irrigation control system (CIS). Under the AIS treatment, the WUE and IWUE values were 1.64 and 1.37 k·gm-3 for wheat, and 7.50 and 6.50 kg·m-3 for tomato crops; under the CIS treatment the values were 1.47 and 1.21 kg·m-3 for wheat and 5.72 and 4.70 kg·m-3 for tomatoes, respectively. Therefore, the AIS provided significant advantages in both water savings and crop yields by utilising up to 26% less water than the CIS, and simultaneously generating higher total yields. The automated irrigation system technique may be a valuable tool for conserving water and scheduling irrigation for wheat and tomato crops, and may be extendable to other similar agricultural crops.