Precise and quick estimates of soil moisture content for the purpose of irrigation scheduling are fundamentally important. They can be accomplished through the continuous monitoring of moisture content in the root zone area, which can be accomplished through automatic soil moisture sensors. Commercial soil moisture sensors are still expensive to be used by famers, particularly in developing countries, such as Egypt. This research aimed to design and calibrate a locally manufactured low-cost soil moisture sensor attached to a smart monitoring unit operated by Solar Photo Voltaic Cells (SPVC). The designed sensor was evaluated on clay textured soils in both lab and controlled greenhouse environments. The calibration results demonstrated a strong correlation between sensor readings and soil volumetric water content (θ_V). Higher soil moisture content was associated with decreased sensor output voltage with an average determination coefficient (R²) of 0.967 and a root-mean-square error (RMSE) of 0.014. A sensor-to-sensor variability test was performed yielding a 0.045 coefficient of variation. The results obtained from the real conditions demonstrated that the monitoring system for real-time sensing of soil moisture and environmental conditions inside the greenhouse could be a robust, accurate, and cost-effective tool for irrigation management.

Accurately estimating crop performance under various fertilizer levels in a rapid and non-destructive manner has become a vital aspect of precision agriculture technology for both economic and environmental benefits. This study aimed to estimate different sugar beet parameters, such as total chlorophyll (Chlt), chlorophyll a (Chla), chlorophyll b (Chlb), root yield (RY), sugar yield (SY), and sugar content (SC) under five nitrogen (N) levels (0, 30, 60, 90, and 120 kg N ha⁻¹). This was achieved by using a combination of the gradient boosting regression (GBR) model with published and newly developed two- and three-band spectral indices (2D- and 3D-SRIs). The results showed that the N levels had the highest proportion of variations (80.4–92.9%) for all parameters, except for SC, which had more variation (59.9%) according to year than the N levels (37.2%). All parameters, except SC, showed a significant increase with gradually increasing N levels. Additionally, the N levels displayed linear and strong positive relationships with the chlorophyll parameters, and linear and strong negative relationships with SC, while these relationships were quadratic and strong with RY and SY. Several published and novel 3D-SRIs exhibited moderate to strong relationships (R² = 0.65–0.89) with all parameters. The newly developed 3D-SRIs, which involve wavelengths from the visible, near-infrared, and red-edge regions, such as NDI_{536, 538, 534}, NDI_{738, 750, 542}, and NDI_{448, 734, 398}, were effective in accurately estimating all parameters. Combining 2D-SRIs, 3D-SRIs, and the aggregate of all spectral indices (ASRIs) with GBR models could be a robust strategy for estimating the six observed parameters with reasonable precision. The GBR-ASF-6 SRIs and the GBR-ASF-7 SRIs models performed better in predicting Chl content and SC with R² values of 0.99 and 0.99 (RMSE = 0.073 and 1.568) for the training dataset and R² values of 0.65 and 0.78 (RMSE = 0.354 and 6.294) for the testing datasets, respectively. The obtained results concluded that published and newly developed 3D-SRIs, GBR based on 2D-SRIs or 3D-SRIs, and the aggregate of all ASRIs can be used in practice to accurately estimate the Chl content, production, and quality of sugar beet across a wide range of N levels under semiarid conditions.

The use of automatic systems in the agriculture sector enhances product quality and the country’s economy. The method used to sort fruits and vegetables has a remarkable impact on the export market and quality assessment. Although manual sorting and grading can be performed easily, it is inconsistent, time-consuming, expensive, and highly influenced by the surrounding environment. In this regard, this study aimed to design and optimize the performance of a low-cost, multi-purpose, automatic RGB color-based sensor for sorting fruits. The proposed automatic color sorting system consists of hardware components including a machine frame, belt and pulleys, conveyor belt, scanning zone, plastic boxes, electric components (stepper motors, RGB color sensors, Arduino Mega, motor drivers), and software components (Arduino IDE version 2.2.1 and C++). Calibration was performed for the light intensity sensor to measure the light intensity inside the scanning zone, the conveyor speed sensor, and the RGB color sensors by testing the RGB color channels. The sensor, the height, conveyor belt color, and light intensity should be carefully adjusted to ensure a high performance of the color-based sorting system. The results showed that the appropriate sensor height ranged from 15 to 30 mm, the optimum color of the conveyor belt was black, and scanning the objects at a light intensity of 25 lux achieved the best output signals. The RGB color sensors achieved an analytical performance similar to that obtained with manual sorting without requiring the use of computers for image processing like other automatic sorting systems do in order to gather RGB data.

The current study aimed to design and test the accuracy of a variable-rate control metering mechanism (VRCMM) and a remote smart monitoring system (RSMS) for a precision sugarcane transplanter based on IoT technology. The VRCMM is used to operate the seedling metering device at different speeds using a stepper motor based on the travel speed, and the RSMS was employed to evaluate of the three basic parameters of seedling amount, optimum rate, and missed rate. Two types of sensors were used for detecting the sugarcane seedling (SS) and travel speed, including one ultrasonic sensor and one infrared RPM sensor. The study was performed at five travel speeds and four transplant spacings. The findings of laboratory tests showed that the mean record of the relative error between the desired stepper motor speed of the VRCMM and the real value was 3.39%, and it increased with increasing the travel speed. as Additionally, the speed regulation performance was in agreement with the transplanting index. The change in RSMS accuracy is obvious when the travel speed is high and the transplant spacing is small. The RSMS accuracy drops sharply, revealing a leaping change. In conclusion, the smart and intelligent designed sugarcane transplanter would be very useful in sugarcane production.

Water scarcity is a major constraint for food production, particularly in arid and semi-arid environments. In this regard, selecting the best irrigation technique is crucial to overcome water scarcity and enhance water productivity (WP) with no significant yield loss. This study aimed to assess the impact of irrigation techniques of every furrow irrigation (EFI), alternate furrow irrigation (AFI), and drip irrigation (DI), as well as the flow rate, on irrigation system performance parameters, yield, water productivity of cauliflower crop and soil salinity during the two successive growing seasons of 2017/2018 and 2018/2019 under field conditions. The treatments comprised three different irrigation inflow rates: Q1 = 0.47 L/s, Q2 = 0.95 L/s, and Q3 = 1.43 L/s. For both investigated seasons, the AFI + Q3 treatment produced the best water distribution uniformity (DU) and water application efficiency (AE) of 85.10% and 72.73%, respectively, of the surface irrigation, and DI methods across the two growing seasons produced the highest DU of 95%. DI produced the highest cauliflower curd yield (18.12 Mg/fed), followed by EFI + Q3 (12.285 Mg/fed) and AFI + Q3 (11.905 Mg/fed). The maximum mean WP value of 10.6 kg/m³ was recorded with DI, followed by AFI + Q3 (6.24 kg/m³), across the two growing seasons. DI, AFI + Q3, AFI + Q2, AFI + Q1, EFI + Q3, and EFI + Q2 saved irrigation water by 32.63, 28.71, 21.22, 18.04, 10.48, and 3.18%, respectively, compared with EFI + Q1 across the two growing seasons. During both seasons, the average value using the drip irrigation system was 3.60 dS/m. Considering the annual leaching requirements of soil, climate change conditions, and fixed costs, we recommend the use of a drip irrigation system in clayey soil to produce cauliflower, followed by the use of the alternative furrow irrigation method to enable the aeration of the same soil for a lower cost.

Egypt has limited agricultural land, associated with the scarcity of irrigation water and rapid population growth. Hydroponic farming, seawater desalination and magnetic treatment are among the practical solutions for sustaining rapid population growth. In this regard, the main objective of the present research study was to design and construct a hierarchical engineering unit as a hydroponic farming system (soilless) to produce an iceberg lettuce crop using magnetically treated saline water. The treatments included four types of irrigation water: common irrigation water (IW₁) with an electrical conductivity (EC) of 0.96 dS/m as a control treatment, magnetically treated common irrigation water (IW₂) with an EC of 0.96 dS/m, saline water (IW₃) with an EC of 4.56 dS/m and magnetically treated saline water (IW₄) with an EC of 4.56 dS/m; three depletion ratios (DR) of field capacity (DR₀ = 50%, DR₁ = 60% and DR₂ = 70%) and three slopes of hydroponic pipes (S₁ = 0.0%, S₂ = 0.025% and S₃ = 0.075%). The results revealed that seawater contributed 7.15% to produce iceberg lettuce in the hydroponic system. The geometric parameter, the slope of the pipes, influenced the obtained luminous intensity by an average increase of 21% and 71% for S₂ and S₃, respectively, compared with the zero slope (horizontal pipes). Magnetization of irrigation water increased the total soluble solids (TSS) and enhanced the fresh weight and water productivity of both iceberg lettuce varieties used. The maximum percentages of TSS were 5.20% and 5.10% for lemur and iceberg 077, respectively, for the combination IW₄DR₂S₂. The highest values of fresh weight and water productivity of 3.10 kg/m and 39.15 kg/m³ were recorded with the combinations IW₃DR₂S₃ and IW₄DR₁S₃, respectively, for lemur and iceberg lettuce. The percentages of these increases were 109.46% and 97.78%, respectively, when compared with the combination IW₁DR₀S₁. The highest values of iceberg lettuce 077 fresh weight and water productivity were 2.93 kg/m and 36.15 kg/m³, respectively, which were recorded with the combination IW₄DR₁S₃. The percentages of these increases were 112.32% and 120.56%, respectively, when compared with IW₁DR₀S₁ (the control treatment).