Tractor accidents caused by lateral overturning and backward rollover during agricultural activities and general driving are common. In this study, various research cases were analyzed to identify the factors influencing the lateral overturning and backward rollover of tractors and to examine their static and dynamic stability. Studies on the analysis of the major causes of these incidents and evaluation of tractor safety were compiled. Test methods, including actual tests and simulations, were categorized, and the characteristics of lateral overturning and backward rollover safety of tractors in different studies were examined. Additionally, safety improvement measures were proposed by identifying and summarizing the causes of accidents involving agricultural machinery. Tractor safety was evaluated primarily by conducting actual tractor and simulation tests. These tests were classified into field tests, tests on scale models, spreadsheet programs, and 3D simulation programs. The primary causes of lateral overturning and backward rollover were unstable center of gravity, extremely high driving speed, and ground conditions. Given the considerable number of studies dedicated to evaluating tractor safety, various technologies aimed at preventing lateral overturning and backward rollover incidents are expected to be applied to tractors in the future. The production and testing of safe agricultural machinery are expected to contribute to a reduction in accident rates.

In this study, we developed a monitoring system to accurately track the seeding rate and to identify the locations where the mechanical pot-seeding machine failed to sow seeds correctly. The monitoring system employs diverse image processing techniques, including the Hough transform, hue–saturation–value color space conversion, image morphology techniques, and Gaussian blur, to accurately pinpoint the seeding rate and the locations where seeds are missing. To determine the optimal operating conditions for the seeding rate monitoring system, a factorial experiment was conducted by varying the brightness and saturation values of the image data. When the derived optimal operating conditions were applied, the system consistently achieved a 100% seed recognition rate across various seeding conditions. The monitoring system developed in this study has the potential to significantly reduce the labor required for supplementary planting by enabling the real-time identification of locations where seeds were not sown during pot-seeding operations.

In this study, to evaluate the safety of the fastening device, which is a vulnerable part of the agricultural by-product collector, the stress in fastening devices was measured, and the operational and driving safety were analyzed by deriving the static safety factor and fatigue life. The position with the maximum stress in fastening devices was identified through structural analysis simulation, and a stress measurement system was constructed using strain gauges. Test conditions for stress measurement were classified into three operating conditions (collection operation, driving with the loading part lifted to the highest point, and driving with the loading part lifted to the lowest point) and three soil conditions (even pavement, sloped pavement, and farmland). A process for deriving the fatigue life based on the measured stress was constructed by applying the rain-flow counting method, Goodman equation, and Palmgren–Miner’s rule via commercial software. From the stress measurement results, the collection operation exhibited the highest maximum stress, followed by driving with the loading part lifted to the highest point and driving with the loading part lifted to the lowest point. Under all conditions, the static safety factor of the fastening devices was found to be higher than 1.0 (1.16–1.33). The fatigue life of the fastening devices was also found to be longer than the service life of Korean agricultural machinery under all operating conditions. Therefore, the fastening devices are expected to operate safely under generated static and dynamic loads. The agricultural by-product collector can perform agricultural work and drive stably and is expected to contribute to reducing unnecessary labor force for Korean farms.

Tractor fuel consumption has typically been predicted using indoor test results under specific conditions. This study analyzes the factors affecting fuel consumption during rotary tillage in actual fields and develops a prediction model. The test field was divided into sections using a grid method, and rotary tillage operations were performed to measure various parameters, including soil strength, tractor’s transmission and PTO gear stages, tillage pitch, travel speed, engine and PTO shaft torque and speed, and fuel consumption. Pearson correlation identified variables affecting specific fuel consumption, and regression analysis was used to develop a prediction model. The model’s accuracy was analyzed using the coefficient of determination (R²) and root mean square error (RMSE), and it was compared with the ASABE’s fuel consumption prediction model. The test results showed that higher transmission and PTO gear stages, and tillage pitch decreased specific fuel consumption, while soil strength had no significant effect. Thus, operating at higher gear and PTO stages within suitable conditions enhances energy efficiency in rotary tillage. Statistical analysis showed that specific fuel consumption significantly correlated with tractor travel speed, PTO shaft power, and PTO shaft speed. The prediction model, including these variables, had the highest accuracy with R² of 0.91 and RMSE of 0.011 L/kW·h. The developed prediction model showed significantly improved accuracy compared to the ASABE model, indicating that it can predict specific fuel consumption based on key operational variables in actual rotary tillage operations.

Toxicogenomics, the subdiscipline that merges genomics with toxicology, hold the promise to contributing toward the goal of elucidating mechanism by studying genomic profiling related with various drugs. The application of gene expression profiling technology to examine multiple genes and signaling pathways promises a significant advance in understanding the toxic mechanisms of various drugs and prediction of new drug candidate. Toxicogenomics is emerging field combining genomics and bioinformatics to identify and characterize mechanisms of toxicity of drug and various compounds. The principal hypothesis underlying on this field is that chemical-specific pattern of altered gene expression is related with each chemicals properties, especially toxicological property, and it will be revealed using high-density microarray analysis of sample from exposed organisms. So, in this study we compare the gene expression pattern of two anticancer drugs paclitaxel and orally absorbable paclitaxel, using the cDNA microarray. And from the result of this study, it is possible to provide the new possibility for genome-wide insight into mechanism of their anticancer activity and toxicological phenotype.