Le Zi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Le Zi has a total of 44 co-authors. Here are the top co-authors by number of shared publications:
- Jie He
College of Engineering, South China Agricultural University, Guangzhou 510642, China
2 shared publications - Kang Hou
South China Agr Univ, Coll Engn, Guangzhou 510642, Peoples R China
2 shared publications - Lian Hu
Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510640, China
2 shared publications - Mengdong Yue
Guangdong Provincial Laboratory of Lingnan Modern Agricultural Science and Technology, South China Agricultural University, Guangzhou 510642, China
2 shared publications - Mingjin Li
South China Agr Univ, Coll Engn, Guangzhou 510642, Peoples R China
2 shared publications - Xingmou Qin
College of Engineering, South China Agricultural University, Guangzhou 510642, China
2 shared publications
Latest Publications
- 14 July 2024
- Published by Elsevier in Computers and Electronics in Agriculture
- Vol. 224
Multi-constraint improved RS path planning method for unmanned rice direct seeding machine
- 14 July 2024
- Published by Elsevier in Computers and Electronics in Agriculture
- Vol. 224
Multi-constraint improved RS path planning method for unmanned rice direct seeding machine
- Open Access
- 29 April 2024
- Published by MDPI AG in Agriculture
- Vol. 14 (5) , 702
Worldwide Examination of Magnetic Responses to Heavy Metal Pollution in Agricultural Soils
Over the last decade, a large number of studies have been conducted on heavy metals and magnetic susceptibility () measurement in soils. Yet, a global understanding of soil contamination and magnetic responses remains elusive due to the limited scope or sampling sites of these studies. Hence, we attempted to explore a pollution proxy on a global scale. Through a meta-analysis of data from 102 published studies, our research aimed to provide a worldwide overview of heavy metal pollution and magnetic responses in agriculture soils. We mapped the geographic distribution of nine heavy metals (Cr, Cu, Zn, Pb, Ni, As, Cd, Mn, and Fe) in agricultural soils and explored their pollution sources and contributions. Since 2011, The accumulation of heavy metals has escalated, with industrial activities (31.5%) being the largest contributor, followed by agricultural inputs (27.1%), atmospheric deposition (22.66%), and natural sources (18.74%). The study reports ranging from 6.45 × 10−8 m3/kg to 319.23 × 10−8 m3/kg and from 0.59% and 12.85%, with the majority of the samples being below 6%, indicating heavy metal influence mainly from human activities. Pearson’s correlation and redundancy analysis show significant positive correlations of Pb, Zn, and Cu with (r = 0.51–0.53) and Mn and Fe with (r = 0.50–0.53), while Pb, Zn, Cu, and As metals were shown to be key factors of variation in magnetic response. The average heavy metal pollution load index of 2.03 suggests moderate global agricultural soil pollution, with higher heavy metal contamination in areas of high . Regression analysis confirms soil is considered to be non-polluted below of and polluted above this threshold, with all contamination factors of metals showing a linear correlation with (R = 0.72), indicating that a significant relationship between and the geochemical properties of soils continues to exist on a global scale. This study provides new insights for large-scale agricultural soil quality assessment and magnetic response.- 29 April 2024
- Published by MDPI AG in Agriculture
- Vol. 14 (5) , 702
Open AccessWorldwide Examination of Magnetic Responses to Heavy Metal Pollution in Agricultural Soils
Over the last decade, a large number of studies have been conducted on heavy metals and magnetic susceptibility () measurement in soils. Yet, a global understanding of soil contamination and magnetic responses remains elusive due to the limited scope or sampling sites of these studies. Hence, we attempted to explore a pollution proxy on a global scale. Through a meta-analysis of data from 102 published studies, our research aimed to provide a worldwide overview of heavy metal pollution and magnetic responses in agriculture soils. We mapped the geographic distribution of nine heavy metals (Cr, Cu, Zn, Pb, Ni, As, Cd, Mn, and Fe) in agricultural soils and explored their pollution sources and contributions. Since 2011, The accumulation of heavy metals has escalated, with industrial activities (31.5%) being the largest contributor, followed by agricultural inputs (27.1%), atmospheric deposition (22.66%), and natural sources (18.74%). The study reports ranging from 6.45 × 10−8 m3/kg to 319.23 × 10−8 m3/kg and from 0.59% and 12.85%, with the majority of the samples being below 6%, indicating heavy metal influence mainly from human activities. Pearson’s correlation and redundancy analysis show significant positive correlations of Pb, Zn, and Cu with (r = 0.51–0.53) and Mn and Fe with (r = 0.50–0.53), while Pb, Zn, Cu, and As metals were shown to be key factors of variation in magnetic response. The average heavy metal pollution load index of 2.03 suggests moderate global agricultural soil pollution, with higher heavy metal contamination in areas of high . Regression analysis confirms soil is considered to be non-polluted below of and polluted above this threshold, with all contamination factors of metals showing a linear correlation with (R = 0.72), indicating that a significant relationship between and the geochemical properties of soils continues to exist on a global scale. This study provides new insights for large-scale agricultural soil quality assessment and magnetic response. - Open Access
- 18 February 2024
- Published by MDPI AG in Agriculture
- Vol. 14 (2) , 324
Vibrational Dynamics of Rice Precision Hole Seeders and Their Impact on Seed Dispensation Efficacy
This investigation considered the effects of both internal and external excitation vibrations on the efficacy of the seed dispenser in a rice precision hole seeder. Through comprehensive field tests, we analyzed vibrational characteristics during direct seeder operations and established a vibration seeding test bed for systematic examination of these effects. Time-domain analysis of the vibration data revealed a predominantly vertical vibration direction, with notably higher levels in sandy loam soil compared to clay loam. A correlation was observed between increased engine size and rotary ploughing speeds, as well as forward speed and elevated vibration amplitudes. Frequency domain analysis pinpointed the primary vibration frequency of the machinery within the 0–170 Hz range, remaining consistent across different operating conditions. Crucially, bench test results indicated that seeding accuracy and dispersion were significantly influenced by vibration frequencies, particularly within the 70–130 Hz range, where a decrease in accuracy and increase in dispersion were noted. A regression model suggested a complex, non-linear relationship between seeding performance and vibration frequency. These insights highlight the necessity for a robust mechanism to effectively address these vibrational impacts. This study paves the way for enhancing the operational efficiency of the rice precision hole seeder, aiming to achieve the design goals of minimized vibrations in the paddy power chassis.
- 18 February 2024
- Published by MDPI AG in Agriculture
- Vol. 14 (2) , 324
Open AccessVibrational Dynamics of Rice Precision Hole Seeders and Their Impact on Seed Dispensation Efficacy
This investigation considered the effects of both internal and external excitation vibrations on the efficacy of the seed dispenser in a rice precision hole seeder. Through comprehensive field tests, we analyzed vibrational characteristics during direct seeder operations and established a vibration seeding test bed for systematic examination of these effects. Time-domain analysis of the vibration data revealed a predominantly vertical vibration direction, with notably higher levels in sandy loam soil compared to clay loam. A correlation was observed between increased engine size and rotary ploughing speeds, as well as forward speed and elevated vibration amplitudes. Frequency domain analysis pinpointed the primary vibration frequency of the machinery within the 0–170 Hz range, remaining consistent across different operating conditions. Crucially, bench test results indicated that seeding accuracy and dispersion were significantly influenced by vibration frequencies, particularly within the 70–130 Hz range, where a decrease in accuracy and increase in dispersion were noted. A regression model suggested a complex, non-linear relationship between seeding performance and vibration frequency. These insights highlight the necessity for a robust mechanism to effectively address these vibrational impacts. This study paves the way for enhancing the operational efficiency of the rice precision hole seeder, aiming to achieve the design goals of minimized vibrations in the paddy power chassis.
- Open Access
Method and Experiment for Quantifying Local Features of Hard Bottom Contours When Driving Intelligent Farm Machinery in Paddy Fields
The hard bottom layer of a paddy field has a great influence on the driving stability and the operation quality and efficiency of intelligent farm machinery. For paddy field machinery, continuous improvements in the accuracy and operation efficiency of unmanned precision operations are needed to realize unmanned rice farming. In the context of unmanned farm machinery operation, the complicated hard bottom layer situation makes it difficult to quantify the local characteristics of paddy fields. In this paper, an unmanned direct rice seeding machine chassis is used to maneuver the operation field and collect the hard bottom layer information simultaneously. This information is used to design a data processing method that automatically calibrates the sensor installation error and performs abnormal value rejection and 3D sample curve denoising of the contour trajectory. A hard bottom layer surface profile evaluation method based on the local sliding surface roughness is also proposed. The local characteristics of the hard bottom layer were quantified, and the results from the test plots showed that the mean value of the local roughness was 0.0065, where 68.27% of the plots were distributed in the variation range of 0.0042~0.0087 and 99.73% were distributed in the variation range of 0~0.0133. Using the test field data, the surface roughness features were verified to describe the variability in representative working conditions, such as the transport, downfield, operation, and trapping of unmanned intelligent farm machinery. When driving intelligent farm machinery, the proposed method for quantifying local features of the hard bottom layer can provide feedback on the local environmental features at any given position of the machinery. The method also provides a reference for the design optimization of unmanned systems, which can help to realize speed adaption and improve the local path tracking control accuracy of smart farming machines.
Open AccessMethod and Experiment for Quantifying Local Features of Hard Bottom Contours When Driving Intelligent Farm Machinery in Paddy Fields
The hard bottom layer of a paddy field has a great influence on the driving stability and the operation quality and efficiency of intelligent farm machinery. For paddy field machinery, continuous improvements in the accuracy and operation efficiency of unmanned precision operations are needed to realize unmanned rice farming. In the context of unmanned farm machinery operation, the complicated hard bottom layer situation makes it difficult to quantify the local characteristics of paddy fields. In this paper, an unmanned direct rice seeding machine chassis is used to maneuver the operation field and collect the hard bottom layer information simultaneously. This information is used to design a data processing method that automatically calibrates the sensor installation error and performs abnormal value rejection and 3D sample curve denoising of the contour trajectory. A hard bottom layer surface profile evaluation method based on the local sliding surface roughness is also proposed. The local characteristics of the hard bottom layer were quantified, and the results from the test plots showed that the mean value of the local roughness was 0.0065, where 68.27% of the plots were distributed in the variation range of 0.0042~0.0087 and 99.73% were distributed in the variation range of 0~0.0133. Using the test field data, the surface roughness features were verified to describe the variability in representative working conditions, such as the transport, downfield, operation, and trapping of unmanned intelligent farm machinery. When driving intelligent farm machinery, the proposed method for quantifying local features of the hard bottom layer can provide feedback on the local environmental features at any given position of the machinery. The method also provides a reference for the design optimization of unmanned systems, which can help to realize speed adaption and improve the local path tracking control accuracy of smart farming machines.