Rice lodging, a common physiological issue that occurs during rice growth and development, is a major factor contributing to a decline in rice production. Current techniques for the extraction of rice lodging are subjective and require considerable time and labor. In this paper, we propose a fully automated method in an end-to-end format to objectively calculate the rice-lodging rate based on remote sensing data captured by a drone under field conditions. An image post-processing method was applied to enhance the semantic-segmentation results of an operable lightweight model on an embedded board. The area of interest within the parcel was preserved based on these results, and the lodging occurrence rate was calculated in a fully automated manner without external intervention. Five models were compared based on the U-Net and lite-reduced atrous spatial pyramid pooling (LR-ASPP) models with MobileNet versions 1–3 as the backbones. The final model, MobileNetV1_U-Net, performed the best with an RMSE of 11.75 and R² of 0.875, and MobileNetV3_LR-ASPP (small) achieved the shortest processing time of 4.9844 s. This study provides an effective method for monitoring large-scale rice lodging, accurate extraction of areas of interest, and calculating lodging occurrence rates.

Hydrogen water, characterized by a high concentration of unionized hydrogen molecules, is being presented as a new alternative in agriculture. This study focused on the application of hydrogen water to leaves and its effects on crop yield and quality, especially on leaf lettuce, through foliar fertilization (twice a week at 25 L per 330 m²) experiments with hydrogen water with dissolved hydrogen of more than 300 ppb and control water with dissolved hydrogen close to 0 ppb. The experimental group that received foliar fertilization showed significant advantages over the control group in leaf thickness and stem thickness characteristics that affected post-harvest distribution quality. Area growth rate analysis revealed a consistently higher average area growth rate (up to 0.86%) in the group treated with hydrogen water compared to the control group (0.1%). The results of an independent-sample t-test of spectral indices showed that the experimental group showed a significance level above the critical value (p < 0.05) compared to the control group in the analysis results of NDVI in soil cultivation and NDRE in hydroponic cultivation. Although other spectral indices did not show differences between the experimental and control groups at the significance level, the average of all samples was higher in the experimental group than in the control group. Overall, the findings suggest that foliar application of hydrogen water positively influences the growth of lettuce crops, as evidenced by comprehensive trait, area growth rate, and spectral index analyses. These results underscore the novelty of hydrogen water as an efficient foliar treatment that enhances crop performance with minimal system changes. Its potential to improve yield quality while reducing chemical input demands suggests clear economic and environmental benefits.

This paper proposes an analytical voltage error compensation algorithm to improve flux-based torque control performance in interior permanent magnet synchronous motor (IPMSM) drive systems. Although flux-based torque control is inherently robust against battery voltage variations, voltage errors caused by stator resistance and inverter dead-time lead to inaccurate flux estimation, which degrades torque control performance. In flux-based torque control, a flux-current map is generated by applying stator currents with predefined magnitudes and phase angles near the rated speed, measuring the corresponding dq-axis voltages and torque, and deriving the flux linkage. Based on the obtained flux-current characteristics, optimal operating conditions such as maximum torque per ampere (MTPA), minimum flux per torque (MFPT), and constant torque operation are determined. The flux-current map is then used to calculate dq-axis current references corresponding to the flux and torque commands derived from the inverter input voltage and motor speed. However, when voltage errors are included during the flux-current map generation process or the flux reference calculation process, inaccurate maps and flux references are produced, resulting in degraded torque control performance. In practical drive systems, the measured voltage corresponds to the output voltage of the dq-axis current controller and inherently includes voltage drops caused by stator resistance and voltage distortion induced by inverter dead-time. To address this issue, this paper proposes an equation-based voltage error compensation method derived from the IPMSM dq-axis voltage equations. The proposed method compensates both resistive and dead-time induced voltage errors during the flux-current map generation and flux reference generation processes. The effectiveness of the proposed method is verified through MATLAB/Simulink simulations and experimental results, demonstrating improved torque control performance, voltage utilization, and efficiency.