To achieve fully autonomous harvesting by robot combines, recognizing the external environments in rice fields is paramount. Two deep neural networks were used to detect the rice lodging, FCNResNet50 and FCNVGG16. In this study, a fisheye-lens camera was deployed to obtain a wider range of images. However, fisheye-lens cameras have a problem over the imaging frame, distortion. To resolve it, we propose to train models by adding cropped images from the specific area to the primitive dataset. By applying designated cropped images, these models achieved improved performance in testing original scenes. For FCNResNet50, the performance was 1–5 % better, while 10 % on average on FCNVGG16. Still, mean intersection over union (IoU), pixel accuracy and class accuracy also outperformed.

Assessing the maturity and size of crops with below-ground edible parts, such as potatoes (Solanum tuberosum), traditionally requires labour-intensive digging and excavation. This study evaluates the use of Electrical Impedance Tomography (EIT) as a non-destructive tool for monitoring potato growth under varying soil moisture levels and tuber sizes in pot conditions. Our results show that both tuber size and soil moisture content significantly impact the quality of potato reconstructions using EIT. At low soil moisture levels (around 10 %), tubers are not detectable due to high impedance, but their visibility improves as moisture increases to 28–35 %. Larger tubers yield more accurate reconstructions across all frequencies, while smaller tubers perform better at lower frequencies (below 5 kHz).

The effects of regrowth on the internal structure of green pea sprouts. Using X-ray micro-CT, void distribution was successfully visualized and characterized in sprouts before and after regrowth. In the CT images, a cavity appeared at the center of the stem after regrowth. Individual void analysis using Avizo software for three-dimensional void structures showed a shift in the void volume distribution after regrowth with the emergence of irregularly shaped voids. However, textural analysis indicated a significant decrease in crispness after regrowth. This study suggested that the decrease in crispness correlated with void distribution modifications. In addition, metabolome analysis revealed the downregulation of mannitol and other carbohydrates, indicating reduced photosynthetic activity and sustained aerobic respiration in regrown sprouts.

The environmental and economic viability of recycling brown grease (BG) in a Japanese food processing factory was assessed via oil and fat flow analysis, life cycle inventory analysis of oil-fat recovery equipment (OFRE), and economic loss evaluation. After installation in a food processing factory that discharges wastewater (oil and fat concentration: 2,100 mg/L; wastewater volume: 21.1 m³/d), the OFRE enabled biomass utilization of BG and increased the oil and fat resource rate from 0 % to 63.9 %. Additionally, using refined oil and fat from BG as an alternative to heavy oil A reduced CO_2eq emissions by 11.1 t-CO_2eq/year. Labor and odor control costs also decreased, resulting in a reduction of 51,387 JPY/(t year) in economic loss.

Japan’s agricultural sector faces serious challenges due to a declining and aging workforce. Greenhouse cultivation requires intensive labor in confined spaces, making efficiency improvement and reduced physical burden urgent. This study proposes a solution that converts a commercially available harvesting trolley into an electric multifunctional platform capable of both hands free assistance and autonomous operation. Key technological contributions include a travel compensation control system that enhances operability and positional accuracy and a torque transmission mechanism that adapts to multiple tasks. The evaluation experiments showed reduced positional errors during movement in narrow greenhouse aisles. In addition, the practical utility of the two modes, human following and autonomous movement, was confirmed, indicating potential for agricultural support.

An image-processing algorithm for identifying individual crops is developed for labor-savings and time-series biological information collection. Information including the leaf development frequency are diagnostic indicators of strawberry growth. The algorithm is designed for drones in greenhouses that cannot acquire location information using the global navigation satellite system (GNSS). Drones fly over crop rows and sequentially assign identification numbers (IDs) to crops. Object-detection artificial intelligence (AI) is used to estimate the crop zone, and the ID is based on the crops number difference between frames. The previous misdetection rate was 1.06 %, failing to identify crops, which decreases to 0.31 % using the proposed algorithm. Furthermore, because there are no failures in consecutive frames, IDs are assigned to all crops correctly.

This study utilized unmanned aerial vehicle (UAV)-based multispectral remote sensing to estimate the leaf area index (LAI) of cotton under Verticillium wilt stress. Key spectral bands (B12, B9, B8) and vegetation indices—transformed vegetation index (TVI), difference vegetation index (DVI), and enhanced vegetation index (EVI)—were identified as strongly correlated with LAI. A support vector regression (SVR) model utilizing these features achieved the best estimation performance (validation: R² = 0.877, RMSE = 0.284). Furthermore, a radial basis function kernel support vector machine (SVM-RBF) classifier attained the highest accuracy in mapping canopy parameters (overall accuracy = 94.05 %, Kappa = 0.916). The proposed framework offers a viable technical solution for large-scale, real-time monitoring of cotton Verticillium wilt.

This study investigates the effects of latency in a real-time controlled networked robotic system by isolating the system into four main processes using high-speed camera and timestamp methods. The overall time delay was 1,057 to 2,479 ms, of which the time from when the network camera captures an image to when the host computer receives it accounted for 88.6 %. It was made clear that this time is the delay that should be given the most attention when building a remote monitoring and control system for small agricultural robots. It was also considered that this time delay would not be a fatal problem for tasks that do not require centimeter-order control, such as with weed mowing robots.