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Background: Following the popularization of high-yielding rice in China, fast and efficient mechanized harvesting proved challenging. In addition, the physical characteristics of rice grains and stems are substantially affected during harvest by the field environment and harvest time. However, the combine harvester driver is focused on maximizing the outputs and does not consider the adverse effects of these factors during the rice harvest. Methods: We investigated the effects of the harvest time, spatial position, and temperature on the static friction coefficient of rice grains and stems of high-yielding rice using a field experiment. Results: The result difference in the static friction coefficient between the parallel and perpendicular placements of the rice stem on the steel plate was 9%, indicating that the contact configuration had a significant impact. The region, harvest time, and temperature significantly affected the static friction characteristics of the rice grains and stems. The most significant differences were observed in the X-direction. Conclusions: The optimum harvest time was 10:11 a.m.–3:30 p.m. and the optimum temperature was above 16.5 °C. A quantitative analysis of the effects of the harvest time and temperature on the static friction characteristics of rice provides reliable data for machine design optimization and standardization of harvests operations.

Kernel direct harvesting is the mainstream technology of maize harvesting in the world today, it has significant impact on the maize kernel subsequent food processing. Direct harvest technology in china is not well developed due to the influence of growth environment, agronomy, etc., which leads kernel damage. The kernel damage is necessary studied in maize direct harvesting technology. Therefore, "Zheng Dan 958" was selected, the entrance clearance, export clearance, and cylinder speed as variables to carry out the kernel damage experiment. Processed the image of threshed maize kernel, extracted the crack and boundary characteristics of kernel damage, and established the BP neural network model to study the direct harvesting damage and optimize parameters. The results indicated that kernel damage increased with decreasing threshing clearance and increasing threshing intensity. In a certain threshing clearance, cylinder speed was the key factor affecting kernel damage. The R of model was above 0.95, the accuracy of damage quantitative identification was above 85%. When inlet clearance was 35 mm, outlet clearance was 15 mm, and cylinder speed was 300 rpm, kernel damage was small. Our findings will provide reference for kernel direct harvesting technology and improve harvest quality to meet food processing industry demands.