Effects of planting distance and depth on PTO load spectrum of a small riding-type transplanter

Abstract

The aim of this study was to analyze the effects of the planting distance and depth on the power takeoff (PTO) load spectrum of a small riding-type transplanter for the optimal design of the transplanter. To measure load data during actual planting operation, a load measurement system was developed using a torque sensor, a data acquisition system, and an inverter. Field experiments were conducted at four planting distances (26 cm, 35 cm, 43 cm, and 80 cm) and three planting depths (85 mm, 105 mm, and 136 mm) in a field with similar soil conditions. The measured load data were inverted into a load spectrum using rain-flow counting and Smith-Watson-Topper (SWT) methods. The safety factor of a transplanter according to the planting conditions was analyzed using the converted load spectrum and commercial software. The load spectrum for all planting conditions showed torque ratios similar within a high cycle region of 10 8 to 10 9. The torque ratio increased when the planting depth increased and planting distance decreased in the low cycle region under less than 10 8 cycles. The safety factors of the PTO driving gear and the driven gear increased as the planting distance increased at all planting depths. When the planting depth decreased at the same planting distance, the safety factor of the PTO gears increased. The results of this study might provide useful information for a transplanter PTO design considering the working load according to the various planting conditions.

Environment Modeling Method for Intelligent Robots Based on Binocular Vision

“…In 2017, Prof. Kendall and his team [4] learned the image features captured by binocular cameras using a 3D convolutional structure while adding differentiable flexible functions in the parallax regression stage to obtain higher accuracy subpixel-level parallax values; in 2018, Chang et al [5] proposed to improve the semantic information utilization and optimize the feature point matching results in the feature extraction stage PSMNet (Pyramid Stereo Matching Network) method with the addition of spatial pyramidal pooling network structure, but the disadvantage is low computational efficiency; in 2019, Gao Kun et al [6] proposed an improved method such as using cross windows to construct auto-correlation weights with constraint functions to eliminate matching cost outliers to solve the problem of poor matching results due to the lack of texture in the matching process of binocular vision systems; in 2021, Wan-Soo Kim [7] applied binocular stereo vision system to the measurement of field crop height by developing configuration and stereo matching of binocular stereo cameras to transform the obtained parallax map into the distance depth map between binocular cameras and crops; in 2022, Wang et al used binocular vision system to measure the broken wave surface in wave flume and accurately evaluated the source of error in binocular stereo vision technique and magnitude. Nowadays, binocular stereo vision technology will be targeted to improve the matching accuracy and computing speed for specific usage scenarios according to different production and life needs, and build up an efficient, stable and perfect binocular stereo vision research application field [8].…”

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Stress Simulation on Four-Bar Link-Type Transplanting Device of Semiautomatic Vegetable Transplanter

“…However, to ensure the seamless functionality and safe operation of vegetable transplanters, a comprehensive load and safety analysis is imperative. Such analyses are equally essential for understanding the behavior of transplanting devices under different operating conditions and for identifying potential design improvements [9,10]. Sri et al [11] conducted a study on four-bar link vegetable transplanters, analyzing loads and safety.…”

Experimental Safety Analysis for Transplanting Device of the 4-Bar Link Type Semi-Automatic Vegetable Transplanter

“…Kim et al [29] analyzed loads of a transplanter PTO shaft during transplanting operations concerning planting distance. Kim et al [30] constructed the PTO load spectrum from the measured load data using the rain-flow counting method and Smith-Watson-Topper (SWT) methods and analyzed the fatigue life of a small riding-type transplanter according to the planting distance and depth. Siddique et al [31] analyzed the axle load of a rice transplanter when planting rice seedlings at different working load conditions to select a suitable gear stage and a constant planting depth for rice seedlings.…”