The uniformity of the travel speed of electric reel sprinkling irrigation machines is a key factor affecting irrigation quality. However, conventional PID control is susceptible to sudden disturbances under complex farmland conditions, leading to reduced speed uniformity. To enhance the robustness of the control system, it is necessary to investigate new disturbance rejection control algorithms and their effects. Therefore, a kinematic model of the reel sprinkling irrigation machine and a brushless DC (BLDC) motor model were established, and a linear active disturbance rejection control (LADRC) strategy based on improved particle swarm optimization (IPSO) was proposed. The simulation results show that under variable speed conditions, the system exhibits no overshoot, with an adjustment time of 0.064 s; under variable load conditions, the speed vibration amplitude is less than 0.3%. The field test results indicate that at travel speeds of 10 m/h and 30 m/h, the maximum absolute deviation rate under IPSO-LADRC control is reduced by 27.07% and 13.98%, respectively, compared to PID control. The control strategy based on IPSO-LADRC effectively improves the control accuracy and robustness under complex farmland conditions, providing a reference for enhancing the control performance of other electric agricultural machinery.

Various rapid propagation strategies have been discovered, which has facilitated large-scale plant reproduction and cultivar development. These methods, in many plant species, are used to rapidly generate large quantities (900 mini-tubers/m²) of high-quality propagule (free from contamination) at a relatively low cost in a small space. They are also used for plant preservation. This review article aims to provide potential applications for regeneration and clonal propagation. Plant propagation using advanced agrotechnology, such as aeroponics, is becoming increasingly popular among academics and industrialists. The advancement of asexual aeroponic propagation has been achieved through advancements in monitoring and control systems using IoT and smart sensor technology. New sensor technology systems have gained substantial interest in agriculture in recent years. It is used in agriculture to precisely arrange various operations and objectives while harnessing limited resources with minimal human intervention. Modern intelligent technologies and control systems simplify sensor data collection, making it more efficient than manual data collection, which can be slow and prone to errors. Specific ambient variables like temperature, humidity, light intensity, stock solution concentrations (nutrient water), EC (electrical conductivity), pH values, CO₂ content, and atomization parameters (frequency and interval) are collected more effectively through these systems. The use of intelligent technologies provides complete control over the system. When combined with IoT, it aids in boosting crop quality and yield while also lowering production costs and providing data directly to tablets and smartphones in aeroponic propagation systems. It can potentially increase the system’s productivity and usefulness compared to the older manual monitoring and operating methods.

Leaf water content is a critical metric during the growth and development of winter wheat. Rapid and efficient monitoring of leaf water content in winter wheat is essential for achieving precision irrigation and assessing crop quality. Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing technology has enormous application potential in the field of crop monitoring. In this study, UAV was used as the platform to conduct six canopy hyperspectral data samplings and field-measured leaf water content (LWC) across four growth stages of winter wheat. Then, six spectral transformations were performed on the original spectral data and combined with the correlation analysis with wheat leaf water content (LWC). Multiple scattering correction (MSC), standard normal variate (SNV), and first derivative (FD) were selected as the subsequent transformation methods. Additionally, competitive adaptive reweighted sampling (CARS) and the Hilbert–Schmidt independence criterion lasso (HSICLasso) were employed for feature selection to eliminate redundant information from the spectral data. Finally, three machine learning algorithms—partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF)—were combined with different data preprocessing methods, and 50 random partition datasets and model evaluation experiments were conducted to compare the accuracy of different combination models in assessing wheat LWC. The results showed that there are significant differences in the predictive performance of different combination models. By comparing the prediction accuracy on the test set, the optimal combinations of the three models are MSC + CARS + SVR (R² = 0.713, RMSE = 0.793, RPD = 2.097), SNV + CARS + PLSR (R² = 0.692, RMSE = 0.866, RPD = 2.053), and FD + CARS + RF (R² = 0.689, RMSE = 0.848, RPD = 2.002). All three models can accurately and stably predict winter wheat LWC, and the CARS feature extraction method can improve the prediction accuracy and enhance the stability of the model, among which the SVR algorithm has better robustness and generalization ability.

Crop yields remain significantly low in underdeveloped countries, such as Pakistan, primarily due to the prevalent use of traditional farming methods by most farmers. Resource-saving strategies, like ridge and raised flat bed systems, could boost water use efficiency and production per acre. However, further research is needed to investigate the effectiveness of these methods, along with different levels of soil moisture depletion (SMD), on wheat development in the climatic conditions of Sindh Province. Thus, field trials were undertaken at Sindh Agriculture University’s Tandojam for two years (2022 and 2023) during the wheat growing seasons. The experiment included six treatments, viz., T1 (raised flat bed method under 40% depletion), T2 (ridge method under 40% depletion), T3 (raised flat bed method under 50% depletion), T4 (ridge method under 50% depletion), T5 (raised flat bed method under 60% depletion), and T6 (ridge method under 60% depletion). The bed planter was employed to make raised flat beds, and the ridges were prepared using a ridge planter. Wheat variety (TJ-83) was sown. Under all treatments, the plant population, plant height, and seed index were statistically significant (p < 0.05), although spike length, grain spikes⁻¹, and grain weight spikes⁻¹ were non-significant at different SMD levels. To compute water saving through the ridge and raised flat methods, the recommended delta value for wheat crops under the traditional method was used as a benchmark. Both irrigation technologies conserve water compared to traditional irrigation methods. The T5 treatment conserved the most water, followed by T6. Under a 60% depletion level, the ridge approach produced the highest yield of 2175 kg ha⁻¹, compared to 601 kg ha⁻¹ with the raised flat bed method. The best crop water productivity (CWP) of 1.34 kg m⁻³ was achieved under T6, whereas raised flat beds attained the lowest CWP of 0.27 kg m⁻³ under T1. In terms of wheat grain production and CWP, the ridge furrow method outperformed the raised flat bed furrow method. Based on the results, it is recommended that farmers should use the ridge furrow method and allow a 60% depletion of soil moisture to obtain a high yield of wheat crops.

Double-volute is an effective technique to reduce radial hydraulic force on the centrifugal pump and thereby mitigate the pump-casing vibration induced by unsteady flow characteristics. The mechanism of the double-volute structure balancing radial force on the impeller and volute was investigated on the basis of volute cross-sections by using Computational Fluid Dynamics (CFD) method. The tested performances and simulated inner-flow characteristics of two pumps with single-volute and double-volute were compared in this paper. The performance-test results verify the veracity of CFD method and illustrate that double-volute pump has some losses in terms of pump head and operation efficiency. The numerical simulations reveal that double-volute pump has smaller radial-force magnitude than single-volute pump on the abnormal conditions. Steady pressure field and transient pressure variations of pumps were explored to account for radial-force characteristics of double-volute pump. Compared with the single-volute structure, obvious pressure increases were found in the upper chamber (single part) of the double-volute, while the static pressure decreased in the lower chamber (double chambers). This situation reduces the pressure difference between two volute cross-sections in the collinear radial direction, resulting in smaller radial hydraulic force. Moreover, the transient simulations present the same phenomenon. The radial-forces distribute more uniformly in the double-volute pump, which can alleviate some vibrations.