Optimized design and experiment of spiral-type intra-row weeding actuator for maize (Zea mays L.) planting

Hu, Jia; Gu, Binglong; Ma, Zhongyang; Liu, Huili; Wang, Gang; Li, Mingwei; Hewen, Tan

doi:10.25165/j.ijabe.20211406.6542

Cited by 9 publications

(8 citation statements)

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“…After the weeding test was completed, the numbers of weeds removed and not removed were both counted. Each group of data was collected three times, and an average was taken ( Jia et al., 2021 ).…”

Section: Resultsmentioning

confidence: 99%

Optimization and validation of blade parameters for inter-row weeding wheel in paddy fields

Zhang

Tian²,

Cao³

et al. 2022

Front. Plant Sci.

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The performance of existing rice-paddy weeding machines is not optimal. In this study, the influence of the installation angle of the weeding-wheel blade on cutting resistance and soil-slippage ability was analyzed. The optimal blade angle of the weeding wheel (i.e., the angle at which the resistance to the weeding wheel is minimal and the disturbance speed of the soil maximal) was shown to be< 20°; numerical simulation showed the actual optimal value to be 0°. Different weeding depths (30, 40, and 50 mm), rotation speed of weeding wheel (120, 180, and 240 r/min), and weeder forward speeds (0.3, 0.6, and 0.9 m/s) were used as test factors, and the rates of seedling injury and weeding were used as performance-evaluation criteria to optimize the machine in a secondary orthogonal-rotation combination test. Field experiments showed that the weeding wheel can exhibit optimal working performance under the operating conditions of weeding depth of 39 mm, rotation speed of 175 r/min, and forward speed of 0.6 m/s. The seedling injury and weeding rates were 4.4% and 88.2%, respectively, which were consistent with the numerically predicted results and met the agronomic requirements. This study provides a technical reference for the improvement of paddy-field weeding components.

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Section: Resultsmentioning

confidence: 99%

Optimization and validation of blade parameters for inter-row weeding wheel in paddy fields

Zhang

Tian²,

Cao³

et al. 2022

Front. Plant Sci.

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“…There are two types of proximity sensors which are physical contact sensors and non-contact sensors. As for physical contact sensors, they use a certain part to detect the crop position with the promise of no harm to crops [Jia et al (2018)]. For non-contact sensors, they can detect crop positions without direct mechanical contact.…”

Section: Detection Technology Based On Proximity Sensorsmentioning

confidence: 99%

Crop detection technologies, mechanical weeding executive parts and working performance of intelligent mechanical weeding: a review

Xiang,

Qu,

Wang

et al. 2024

Front. Plant Sci.

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Weeding is a key link in agricultural production. Intelligent mechanical weeding is recognized as environmentally friendly, and it profoundly alleviates labor intensity compared with manual hand weeding. While intelligent mechanical weeding can be implemented only when a large number of disciplines are intersected and integrated. This article reviewed two important aspects of intelligent mechanical weeding. The first one was detection technology for crops and weeds. The contact sensors, non-contact sensors and machine vision play pivotal roles in supporting crop detection, which are used for guiding the movements of mechanical weeding executive parts. The second one was mechanical weeding executive part, which include hoes, spring teeth, fingers, brushes, swing and rotational executive parts, these parts were created to adapt to different soil conditions and crop agronomy. It is a fact that intelligent mechanical weeding is not widely applied yet, this review also analyzed the related reasons. We found that compared with the biochemical sprayer, intelligent mechanical weeding has two inevitable limitations: The higher technology cost and lower working efficiency. And some conclusions were commented objectively in the end.

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“…The level of each factor is listed in Table 3. Considering the first-order interaction between each factor, an L 27 (3 13 ) orthogonal table is selected for the orthogonal test [25][26][27] . Each test is repeated three times, and the test results are averaged.…”

Section: Test Scheme Designmentioning

confidence: 99%

Experiment and parameter optimization of an automatic row following system for the traction beet combine harvester

Wang

You

Peng

et al. 2023

International Journal of Agricultural and Biological Engineering

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To improve the automation level and operation quality of China's beet harvester and reduce the loss due to damaged and missed excavation, this study used a self-developed sugar beet combine harvester and field simulation experiment platform, based on the single-factor bench test of the automatic row following system in the early stage, taking hydraulic flow A, spring preload B, and forward speed C which have significant influence on performance indices as test factors, and taking the missed excavation rate, breakage rate and reaction time as performance indices, the orthogonal experimental study on the parameter optimization of the three-factor and three-level automatic row following system with the first-order interaction of various factors was carried out. The results of the orthogonal experiments were analyzed using range analysis and variance analysis. The results showed that there were differences in the influence degree, factor priority order and first-order interaction, and the optimal parameter combination on each performance index. A weighted comprehensive scoring method was used to optimize and analyze each index. The optimal parameter combination of the overall operating performance of the automatic row following system was A 2 B 2 C 1 , that is, the hydraulic flow was 25 L/min, the forward speed was 0.8 m/s, and the spring preload was 198 N. Under this combination, the response time was 0.496 s, the missed excavation rate was 2.35%, the breakage rate was 3.65%, and the operation quality was relatively good, which can meet the harvest requirements. The comprehensive optimization results were verified by field experiments with different ridge shapes and different planting patterns. The results showed that the mean values of the missed excavation rate of different planting patterns of conventional straight ridges and extremely large "S" ridges were 2.23% and 2.69%, respectively, and the maximum values were 2.39% and 2.98%, respectively; the average damage rates were 3.38% and 4.14%, and the maximum values were 3.58% and 4.48%, which meet the industry standards of sugar beet harvester operation quality. The overall adaptability of the automatic row following system is good. This study can provide a reference for research on automatic row following harvesting systems of sugar beets and other subsoil crop harvesters.

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Optimized design and experiment of spiral-type intra-row weeding actuator for maize (Zea mays L.) planting

Cited by 9 publications

References 0 publications

Optimization and validation of blade parameters for inter-row weeding wheel in paddy fields

Optimization and validation of blade parameters for inter-row weeding wheel in paddy fields

Crop detection technologies, mechanical weeding executive parts and working performance of intelligent mechanical weeding: a review

Experiment and parameter optimization of an automatic row following system for the traction beet combine harvester

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