Spectral reflectance indices (SRIs) often show inconsistency in estimating plant traits across different growth conditions; thus, it is still necessary to develop further optimized SRIs to guarantee the performance of SRIs as a simple and rapid approach to accurately estimate plant traits. The primary goal of this study was to develop optimized two- and three-band vegetation- and water-SRIs and to apply different multivariate regression models based on these SRIs for accurately estimating the relative water content (RWC), gravimetric water content (GWCF), and grain yield (GY) of two wheat cultivars evaluated under three irrigation regimes (100%, 75%, and 50% of crop evapotranspiration (ETc)) for two seasons. Results showed that the three plant traits and all SRIs showed significant differences (p < 0.05) between the three irrigation treatments for each wheat cultivar. The three-band water-SRIs (NWIs-3b) showed the best performance in estimating the three plant traits for both cultivars (R² > 0.80), and RWC and GWC_F under 75% ETc (R² ≥ 0.65). Four out of six three-band vegetation-SRIs (NDVIs-3b) performed better than any other SRIs for estimating GY under 100% ETc and 50% ETC, and RWC under 100% ETc (R² ≥ 0.60). All types of SRIs demonstrated excellent performance in estimating the three plant traits (R² ≥ 0.70) when the data of all growth conditions were combined and analyzed together. The NWIs-3b coupled with Random Forest models predicted the three plant traits with satisfactory accuracy for the calibration (R² ≥ 0.96) and validation (R² ≥ 0.93) datasets. The overall results of this study elucidate that extracting an optimized NWIs-3b from the full spectrum data and combined with an appropriate regression technique could be a practical approach for managing deficit irrigation regimes of crops through accurately, timely, and non-destructively monitoring the water status and final potential yield.

Accurately predicting crop yield in different environmental conditions and irrigation regimes plays a vital role in optimizing agricultural practices and ensuring food security. This research aims to develop a tomato yield estimation model using machine learning (ML) models such as artificial neural network (ANN), random forest (RF), and decision tree (DT) models, based on climate and plant variables. To enhance the models' performance and prevent overfitting, a hyper-parameter tuning technique was implemented through cross-validation. Field experiments were conducted during the 2022 and 2023 growing seasons, implementing three irrigation regimes: 100%, 75%, and 50% of the full irrigation requirements (FIR). The results demonstrate that the ML models effectively captured the relationship between input variables and tomato production under deficit irrigation, achieving a desirable level of accuracy. Impressively, these models showcased predictive prowess 3-7 weeks before the harvest period. The artificial neural network models yielded an average root mean squared error (RMSE) of 3.9 ton/ha and a coefficient of determination (R2) of 0.95 for tomato yield prediction. The RF model displayed even better accuracy, with an RMSE of 3.50 ton/ha and an R2 of 0.96. The DT model forecasted tomato yield with an RMSE of 3.77 ton/ha and an R2 of 0.95. These findings highlight the practicality and reliability of utilizing climate and plant variables in combination with machine learning models to effectively manage tomato crop production, particularly when facing limited water availability for irrigation.

Sugar cane is the main source for refined sugar and the sole source for the molasses industry in Egypt. However, it consumes huge amount of water that could be used to produce other high value crops. Sugar beet might be a good alternative to sugar cane. Yet, due to limited water sources in Egypt, it is important to conduct an integrated water management for both crops. Therefore, in this study, water footprint was estimated to enhance water use efficiency and overcome water scarcity problems. Water footprint (WF) and virtual water trade were estimated, during the period from 2012 to 2016, to select the best crop to produce sugar that reduces the gap between production and consumption of sweetens and achieves high income for farmers. Results showed that the average total water footprint for sugar cane and sugar beet were 428.69 and 232.53 m3/ton, respectively. The energetic and economic water productivity for sugar cane were 1354.18 kcal/m3and 1.48 $/m3; while for sugar beet they were 3338.33 kcal/m3and 3.67 $/m3, respectively. Therefore, it is recommended to increase the cultivated area of sugar beet in Egypt. On the other hand, sugar cane had an imported energetic water productivity of 1812.3 kcal/m3 that was lower than the exported one (2304.94 kcal/m3). This suggests that exporting sugar cane is beneficial for Egypt than importing it. However, importing sugar beet is preferable than exporting it because it had a lower exported energetic water productivity (2557.25 kcal/m3) and exported economic water productivity (2.81 $/m3) than imported ones.

Major international nutrition organizations are becoming increasingly concerned about global agriculture production. In particular, food insecurity has emerged in Egypt as a result of population expansion and rising food demand. Climate change and its variability are significant factors to the worldwide food insecurity problem. Therefore, cutting-edge techniques like IOT and machine learning which have shown its worth in several ways are needed by farmers and agricultural decision-makers to assist it in making timely judgments that will affect the quality of agricultural harvests. A novel open-source technology based on Arduino Board has been developed to predict onion crop irrigation needs. By utilizing three irrigation levels (100%, 85%, and 70% ETc) and integrating machine learning, the system predicts onion output in Talkha city, Egypt for 2023/2024. Meteorological and agronomic data are combined to aid farmers and decision-makers in predicting seasonal onion yields. Our system was constructed using Artificial Neural Network (ANN), Random Forest (RF), and Decision trees (DT) models. We found that, Using Arduino device at T85% and T70% helps to rationalize amount of water by 13% and 28% comparing with T100%. Furthermore, Arduino design with algorithm model gain a good value for (CWP) by (14.5 and 16 kg/m3) at T85% and T70%. Additionally, the results indicated that the ANN model works well during the testing phase, with a R2 of 94.3%, while the RF model and DT perform 90% and 86.1%, respectively. Our results showcase the effectiveness of the technology in enhancing agricultural decision-making and crop management.

Current And Predicted Irrigation Management (CAPIM) model was designed to predict and improve irrigation water management. Its inputs include daily weather data and the outputs are daily evapotranspiration that could be estimated under current conditions using Penman-Monteith equation (P-M), and under climate change using Hargreaves-Samani equation (H-S); and determine the irrigation timing and the amount of water needed for each irrigation event. The CAPIM model was used to develop irrigation schedule for corn crop at different geographic regions in Egypt as (El-Dakahlia; El-Fayoum and Assuit) under RCP8.5 scenario and MIROC- ESM climate model during 2018s, 2040s, 2080s. The model was also used to determine the best adaptive strategy for conforming to the predicted climate changes. CAPIM results were verified by comparing them with results from two known programs (CropWat-8 and ETo-Estimation). The results show that CAPIM predicted the ETo values under current and predicted climate conditions accurately as the agreement value was 0.99. Gross irrigation results during the projected periods 2040s and 2080s are higher than 2018s in the three selected sites. El- Fayoum Governorate is the highest in terms of the number of irrigations and the percentage of increasing GIR. As GIR increased by 28% in 2080s and 29 % in 2040s with 18 hills totaling (2008 and 2077 mm/season), respectively. Moreover, increasing system efficiency by 5 % or 10 % is the best adaptive strategy as it increases the potential yield by about 9 % and 18 % and the WP by 9.4 % and 18.7 %.

Due to the serious water shortages the saving of irrigation water is very important, particularly in countries with limited water like as Egypt. A field experiment was conduct during the summer season of 2012 at El-Karada Water Research Station, Water Management and Irrigation Systems Research Institute, Kafr El-Sheikh, Egypt. This research aims to study the effect of new method of mulching on water saving and yield of maize. Four treatments were tested : no mulch (control) (A); straw mulch (B); 30 % plastic mulch (C), and 60 % plastic mulch (D). Mulching was performed with 30 and 60 % furrows preimeter, along 50 % furrows length. Applied irrigation water (AIW), water advance time (WAT), soil moisture content (SMC), water use efficiency (WUE), application water efficiency (Ea), ground water level (GWL), and grain yield (GY) were computed. Results showed that both treatments D and C decreased AIW about of 24.65 % and 21.65 %, while straw mulch treatment increased AIW about of 3.37 % compared with control treatment. Both treatments of C and D reduced (WAT) with 20.9 and 21.3 %, while treament of B increased it with 19.1 % compared with control treatment. Plastic mulch had siginficantly effect on SMC which the minimum values were recorded under treatments of D and C, respectively. On the other hand, highest values of SMC were obtained under straw mulch treatment. It was observed that WUE had the highest value under treatments C and D, respectively. While, the lowest value was obtained under straw mulch treatment. Application water efficiency invistegated the highest values under treatment of C and B, respectively. But, it had the lowest values under control treatment. Results of GWL indicated that the highest values were obtained under treatments D, A, B and C, respectively. Mulching affected grain yield non-significantly, but the highest grain production was observed in treatment of C (3034.2 Kg/fed.), and minimum in treatment of B (2401.1Kg/fed.). It was concluded that plastic mulch is the best method to save water under surface irrigation system.