Hot peppers are well known for being spicy and also have a high nutrient content. Human resources have formerly been used to harvest hot peppers; however, a high level of musculoskeletal risk to the human workforce has been reported. Therefore, to reduce the risk to farmers and replace the human workforce, the mechanical harvesting of hot pepper and steps to improve the harvesting efficiency of farmers were conducted. To achieve this, the effect of planting distance on the mechanical harvesting of hot peppers was analyzed at three planting distances (30, 40, and 50 cm) with several cultivars. Subsequently, machine-harvested hot peppers were classified into five groups (marketable, damaged, lost, unharvested, and twigged hot pepper), depending on their postharvest status. The average weight ratio of each group was then calculated, after which statistical analyses were conducted. The effect of planting distance on mechanical harvesting was then analyzed by comparing the differences between each group’s average weight ratio and the total weight of hot pepper, which was simultaneously harvested mechanically at each planting distance. Results showed that the average weight ratio of marketable, unharvested, and twigged hot pepper improved as the planting distance increased. However, no effect on the average weight ratio of damaged and lost hot pepper was observed. The highest yield of marketable hot pepper was found at a planting distance of 40 cm, and the average weight ratio to the whole was lower than at 50 cm of planting distance. Thus, the most suitable planting distance for mechanical harvesting was 40 cm.

Overturning accidents in agriculture offroad vehicles have been reported annually around the world. Safety structures, such as rollover prevention, have been adopted to mitigate these accidents. Despite this, accidents remain persistent but less fatal. This study investigated the safe driving boundary for a multi-purpose platform with attachments (cargo, pepper harvester). Mathematical formulations of the roll and pitch motions for multi-purpose platforms were created. The critical overturning angle, at five load levels for each machine (0, 50, 100, 150, 200 kg), was determined using Recurdyn, a dynamic analysis software. Regression analysis of each coordinate of the center of gravity was conducted to verify the most critical coordinate against gaining loads. The critical overturning angle for the X and Y directions was found. The cargo and pepper harvester had 28.64° and 21.04° of critical overturning angles in the Y direction, respectively, with a full loading state while climbing a steep hill. The regression model of the X, Y, and Z coordinates of CoG suggested that the most critical coordinate of the CoG against the loads was in the Y coordinate for the pepper harvester and the X direction for cargo. This study could be applied to develop an awareness safety system that warns the operator of the risk of a fatal accident.

The decreasing rural population and migration to urban areas for high-tech opportunities have weakened the agricultural labor force. While data technology has been adopted in protected agriculture, numerous challenges remain in field agriculture. In this study, we focus on one of the fundamental steps of field operations, i.e., ridge forming, specifically in unmanned agriculture. We compared the performance of a conventional tractor with an implement to that of a ridge-forming robot. The operation data were collected using an acquisition system, and a comparison between the results of both methods was conducted. Additionally, we analyzed the linearity of autonomous driving and the expenses associated with the selected operation. Our findings indicate that the developed robot for ridge forming caused less torque damage and achieved a more accurate target soil depth, with a linearity performance showing a distance error of only 0.267 m. Furthermore, it eliminated the need for hiring an operator and significantly reduced fuel consumption, which accounts for 50.81% of the operational expenses. These results suggest that field operations can be effectively replaced by autonomous systems, and further research on unmanned agriculture is warranted.

In this study, the physical properties of garlic just before collection were investigated, and garlic-collecting conditions are presented as basic data of design factors that can be utilized during the development of garlic collectors. The physical properties were subdivided into geometrical properties and mechanical properties, and the ecotype of garlic was divided into CSG (cold-season garlic) and WSG (warm-season garlic). Geometrical properties were compared between CSG and WSG by measuring the average weight, length, width, number of kernels, volume, and density of each cultivar, and WSG showed higher geometrical properties in all geometrical properties. In addition, there was garlic on the incomplete side of WSG, and shape non-uniformity was higher than that of CSG. There was no significant difference in the average Poisson’s ratio of mechanical properties. In the case of the modulus of elasticity, Namdo cultivars showed the lowest value at 15.52 MPa. The average allowable compressive stress measured using Hooke’s law was the lowest in the Uiseong cultivar (204.33 kPa) and the highest in the Danyang cultivar (376.42 kPa). The static friction coefficients among the four different materials by cultivar exhibited a commonality of being highest in aluminum and generally lowest in plastic. It is expected that the design of the garlic collector will be carried out in consideration of garlic when developing it using up-to-date physical properties of garlic just before collection.

A prototype for the remover of cherry tomato calyxes was designed and manufactured. The tap remover was designed and manufactured considering the conveyor transport speed, brush length and clearance, and diameter. These were adjusted in three levels to determine the optimal design factor. Performance tests were conducted using Icon 513, a circular-shaped cherry tomato variety, and Minimaru, a jujube-shaped cherry tomato variety. Conveyor transport speeds were set at 210, 280, and 350 mm/s; brush lengths at 70, 80, and 90 mm; brush clearances at 20, 22, and 24 mm; and brush diameters at 0.8, 1.0, and 1.2 mm. The two varieties showed a similar damage rate during calyx removal. However, Minimaru showed a higher calyx removal rate than Icon 513, indicating that it is most suitable for the calyx removal mechanization process.

Manure distribution in soil creates a ground environment that is conducive to crop cultivation. However, the lumping and concentration of manure in the field can occur, hindering the fertilization of the soil for plant growth, and the randomization of nutrients under different soil depths accelerates it. To overcome the challenges associated with agricultural testing, such as high cost, inclement weather, and other constraints, computational analysis is often used. In this study, rotary operations are performed using the discrete element method (DEM) to ensure the uniform distribution of manure and four soil layers. DEM analysis was conducted with three experimental factors, and simulation sets were designed using the Box-Behnken central combination method. The DEM results were evaluated using the uniformity index (UI), and the field test of the rotary operation was performed with the set showing the most uniform distribution among the results. Due to undistinguishable particles in reality, the uniformity was validated by a comparison of the chemical characteristics of the L₁ and L₅ in terms of before and after the rotary operation. The DEM parameter of the soil was determined by performing field measurements at different soil depths (0–20 cm), and this parameter was calibrated by conducting a penetration test. The Box–Behnken central combination method was implemented using the following factors: tillage depth (X₁), PTO revolution speed (X₂), and forward machine velocity (X₃). These factors were obtained using the UI regression model and the response surface method. In the results, it was indicated that the UI was affected by the factors in the following order: X₁ > X₂ > X₃. The optimized factor values were X₁ = 25 cm, X₂ = 800 RPM, and X₃ = 1.8 km/h, leading to a UI of 6.07, which was consistent with the analysis results. The operating parameters were maintained throughout the field test, and the acquired data were input into the measurement system. The lowest UI value of 6.07 had the strongest effect on decreasing the disparity between L₁ and L₅, especially in terms of pH, organic matter, P, Ca, and Mg. In summary, the results indicated that soil distribution can be controlled by adjusting mechanical parameters to ensure uniform chemical characteristics across various soil depths.