The agricultural sector emphasizes sustainable development and energy efficiency, particularly in optimizing water pumping systems for irrigation. Brushless DC (BLDC) motors are the preferred prime mover over induction motors due to their high efficiency in such applications. This article details the rotor design and analysis of an energy-efficient BLDC motor with specifications of 1 hp, 3000 rpm, and 48 V, specifically tailored for a centrifugal monoset pump for irrigation. The focus lies in achieving optimal energy efficiency through grey wolf optimization (GWO) algorithm in the rotor design to determine optimal dimensions of the Neodymium Iron Boron (NdFeB) magnet as well as its grade. The finite element method analysis software, MagNet, is used to model and analyze the BLDC motor. The motor parameters, such as speed, torque, flux functions, temperature, and efficiency, are analyzed. For performance comparison, the same model with different magnet models is also analyzed. Validation via 3D finite element analysis highlights improvements in magnet flux linkage, stator tooth flux density, and rotor inertia with increased magnet thickness. Simulation results affirm the consistent performance of the designed BLDC motor, preferably when efficiency is increased. This efficiency and the constant speed lead to an improvement in the overall conversion efficiency of 7% within its operating range, affirming that the motor pump system is energy-efficient.

With the rampant growth of the global population and the increasing need for agricultural products, farmers confront a critical hurdle of ensuring an adequate supply of water resources for irrigation to meet these escalating demands. Amid limited water resources, farmers must embrace innovative solutions and efficient water management practices in the era of Agriculture 4.0 turning this concept into a tangible reality to meet the growing demand for efficient farming practices. By integrating Artificial Intelligence (AI) and the Internet of Things (IoT) to gather and analyze agricultural data, we now have the capability to make more accurate and informed decisions in agriculture. In this, an innovative and adaptable irrigation system is introduced which prioritizes efficiency and cost-effectiveness by minimizing resource consumption. This strategy leverages the capabilities of Machine Learning (ML) algorithms to elevate the level of agricultural intelligence. By employing a suite of sensors, including those for soil moisture, temperature, and rainfall, within an environment conducive to prolonged plant growth, the collected data to construct SVM models findings indicate that SVM outperforms other models (LR, NN, NB) with an impressive recognition rate of 98.5% and a minimal root mean square error (RMSE) of 0.10. Through this system, crop irrigation is achieved with a high level of precision, entirely automated, and without the need for human intervention. As a result, this technology leads to a substantial reduction in water consumption, up to 50%.

The cascaded H-bridge (CHB) inverter offers a significant advantage in its straightforward modular design. In a recent study, a unified multilevel inverter incorporating a unique H-bridge component was proposed. This innovative topology has been further refined to include switching devices and DC-link voltage inputs, enabling the generation of a multitude of voltage steps. This advancement is specifically aimed at enhancing control over both active and reactive power for individual DC voltage sources within a photovoltaic module, ultimately improving power quality and system reliability. The study also presents an original control strategy and proportional pulse width modulation techniques tailored for CHB multilevel inverters in grid-connected systems, where each H-bridge module can deliver variable power levels.

This paper presents an adaptive reinforcement learning (RL)-based control strategy for a wireless power transfer (WPT)-fed brushless DC (BLDC) motor drive, aimed at enhancing efficiency in industrial applications. Conventional control methods for BLDC motors often result in higher energy consumption and increased torque ripple under dynamic load and voltage variations. To address this, an adaptive RL framework is implemented with pulse density modulation (PDM), enabling the controller to augment motor speed, torque, and input power in real time. The system is modeled and tested for a 48 V, 1 HP BLDC motor, powered through a 1.1 kW WPT system. Training is carried out across 10 learning episodes with varying load torque and speed demands, allowing the RL agent to adaptively minimize losses while maintaining performance. Results indicate a significant reduction in torque ripple to a minimum of 0.20 Nm, stable speed regulation within ±30 rpm, and improved power utilization compared to existing controllers. The integration of RL with WPT provides a robust, contactless, and energy-efficient solution that is suitable for sustainable industrial motor-pump applications.

Every economy depends on agriculture, and research in this area is primarily focused on boosting food supply and quality while minimizing costs and maximizing profit. Numerous factors can have an impact on tomato pricing, which serve as a crucial indicator of market trends for vegetables. Making educated decisions could be facilitated by predicting tomato average prices. To forecast the typical prices of tomatoes, we used the Support Vector Machine (SVM) model together with polynomial feature modification in this study. For our projections, we used the ‘Minimum’ and ‘Maximum’ pricing attributes. To guarantee consistency and improved performance, the dataset was standardized. With crossvalidation, we used a Grid Search technique to refine the parameters of our SVM model. The Mean Squared Error (MSE) and R2 score metrics were used to assess the model's performance. Our findings were shown graphically by scatter plots that contrasted real and forecasted prices and a histogram that showed the prediction errors. The SVM model has the potential to forecast average tomato prices when used in conjunction with polynomial feature transformation and parameter optimization. The agricultural industry's stakeholders may use this technique to gather knowledge of price trends and guide their decisions. More research may be done to increase prediction accuracy by incorporating more features and using other algorithm.