Lodging is a major obstacle to autonomous harvesting. As a first step toward lodging-aware harvesting, this study presents a method based on unmanned aerial vehicles (UAVs) for detecting rice lodging directions. UAV orthomosaics are divided into tiles and classified into eight directions using pretrained convolutional neural network classifiers. On a mixed-field dataset, the method achieved an overall accuracy of approximately 0.85 and a soft accuracy (allowing for classification into adjacent classes) of over 0.97. On separate-field datasets, overall accuracy decreased to approximately 0.78–0.82 due to spatial domain differences, whereas soft accuracy remained above 0.95. Most misclassifications occurred in adjacent direction classes, indicating practical reliability. The resulting lodging direction maps can be used to plan efficient harvesting paths in lodged fields.

This study aimed to determine the optimal temperature and salt concentration for producing salted eggs with uniform salt content. Medium-sized eggs were soaked in brine solutions with three different salt concentrations and four temperature levels for three days. Subsequently, eggs with three different diameters mean geometric and three shell thicknesses were soaked in a 25 % salt solution at 60 °C for three days. The results showed that the salt content in eggs increased linearly with salt concentration. The temperature that produced the highest salt content was 64 °C. The rate of salt diffusion followed a hyperbolic relationship with shell thickness and an exponential relationship with egg diameter.

This study evaluated the influence of rice hull biochar (RHB) and cedar bark media (CBM) as sustainable substrates for hydroponic strawberry cultivation. The total yield from virgin CBM was 413 g/plant and that from virgin RHB was 354 g/plant. Used CBM increased the yield by 4 % (429 g/plant) while used RHB increased yield by 18 % (419 g/plant) compared to the virgin, with no significant substrate differences. Fruit yields and physical properties were strongly correlated, particularly with particle density (R² = 0.573). After 7 years of cultivation, plant residues had accumulated in both substrates. Uncarbonized carbohydrates in RHB indicated 25.71 % and 0.40 kg/kg of carbonized carbon retained, which was 98.4 % compared to the virgin RHB. CBM decomposed overtime, limiting its long-term stability.

Predicting sugar quality in sugarcane stalks using in-field measurements is difficult because the outer layer is covered with wax, water, and soil. This layer contains fibers and acts as a strong protective barrier with high thermal conductivity, hindering light transmission and masking signals related to the internal sugar content, increasing difficulties in measurement due to the difference in outer layers between sugarcane cultivars. Our research presents a solution to address the varying outer layer conditions of sugarcane cultivars by peeling away the outer layers to achieve uniformity. The findings indicate that this peeling method enhances the quality of the absorbance spectra and that focusing on the significant and reliable wavelength range of 800–950 nm further improves the model prediction accuracy.

An intelligent monitoring model based on YOLOv5 and an attention mechanism is proposed to improve automatic identification of abnormal pig behaviors in farming environments. A dataset containing pig fighting, abnormal feeding, and reduced activity behaviors was constructed and enhanced through data augmentation. By integrating a channel attention mechanism into YOLOv5s, the model improves feature extraction and detection performance for small and occluded targets. Experimental results show that the improved model achieves 95.24 % detection accuracy and 91 FPS, outperforming Faster R-CNN, YOLOv3, and the original YOLOv5s. The model demonstrates strong robustness, stability, and computational efficiency under complex conditions, providing effective technical support for intelligent livestock health management and precision farming applications.

Sweetness and texture are key factors in determining sweet potato quality, but traditional evaluation methods often permanently destroy the product. In this study, we utilized a cost-effective handheld near-infrared (NIR) spectrometer (operating at 900–1,700 nm) to assess Brix and moisture content of yellow-fleshed Japanese sweet potato cultivars grown in Memuro, Hokkaido. Measurements were taken across the growth and storage stages. Spectral data showed that the tip of the sweet potato root showed consistent patterns, making it effective for Brix prediction, with model ratio of prediction to deviation of 2.5.