Theoretical Analysis of Power Requirement of a Four-Row Tractor-Mounted Chinese Cabbage Collector

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.

“…The SRR is calculated as shown in Equation (2). The SRR is 100% when the monitoring system recognizes all the seeds seeded in the pot tray.…”

Section: Factorial Experiments Results Of the Seeding Rate Monitoring...mentioning

confidence: 99%

“…Numerous countries worldwide, including developing countries, are encountering aging farming populations and insufficient labor forces owing to industrialization and urbanization [1,2]. The development and utilization of various agricultural machines are required to address these issues [3,4].…”

Section: Introductionmentioning

confidence: 99%

Development of Seeding Rate Monitoring System Applicable to a Mechanical Pot-Seeding Machine

Kim,

Lee,

Hwang

et al. 2023

“…In order to ensure safe farming, it is important to consider the optimal design of the frame. Fatigue damage is an important issue in mechanical design, and components must be designed to withstand cyclic loads [11]. Agricultural machinery experiences frequent failures due to the heavy loads applied.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Safety Factors and Predicting Fatigue Life of Weak Points in an Electrically Driven, Multi-Purpose Cultivation Tractor

Hwang,

Kim,

et al. 2024

The advancement of agriculture and a shortage of labor have led to an increased use of agricultural machinery. However, the resulting environmental issues have prompted a shift from internal combustion engines to electric drivetrains. The electric drivetrain includes the installation of batteries, which can lead to decreased energy efficiency and significant loads on the vehicle due to their heavy weight. Consequently, the importance of ensuring the safety of agricultural machinery is being increasingly emphasized. The load on the frame of agricultural machinery is not consistent during off-road driving, and the accumulation of load cycles can lead to the destruction and failure of components. Therefore, it is necessary to ensure a level of safety and to predict the fatigue life. In this study, we estimate the safety factor and predict the fatigue life of weak points in an electrically driven, multi-purpose cultivation tractor based on working conditions (width, soil, and drive). Strain gauges were attached to these weak points to measure the strain, which was then converted to von Mises stress. Fatigue life was predicted using the rainflow counting method and the Palmgren–Miner rule. The results showed that the safety factor measured under various working conditions was greater than 1. The estimated minimum fatigue life was 124,176 years. Considering that the cultivator is used for 29.7 h annually and has a durability lifespan of 5 years, it is expected to be safely usable throughout its service life.

“…The initial step in the load and safety analysis of a machine component is stress determinative [15,16]. The prevalent technique for measuring stress in machine parts involves using sensors, such as strain gauges and load cells in field tests [17][18][19]. The sensor can measure stress exclusively at a specific location.…”

Section: Introductionmentioning

confidence: 99%

Stress Simulation on Cam-Type Transplanting Device of Semiautomatic Vegetable Transplanter

Markumningsih,

Hwang,

Kim

et al. 2023

Stress measurements play a crucial role in safety analyses of transplanting devices. Strain gauges for stress measurements during field tests can be expensive and time-consuming. The aim of this study was to investigate the stress on the transplanting device of a cam-type semiautomatic vegetable transplanter using a simulation method. A three-dimensional simulation model was established, considering the dimensions and material properties of the transplanting device. The stress distribution and maximum stress values were obtained through simulations. The maximum stress values at 15 points within the transplanting device determined via the simulation were compared with the experimental stress data to verify the stress simulation model. The results show that the maximum stress obtained from the simulation correlated with that of the measured results, although differences were observed at different locations, particularly at strain gauge positions 11 and 13. Based on the simulation results, the maximum stress occurs at the upper link of the cam-type transplanting device, reaching a magnitude of 201.21 MPa, and the static safety factor is 1.04.