Browsing by Person "He, Xiongkui"

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Development of multifunctional unmanned aerial vehicles versus ground seeding and outplanting: What is more effective for improving the growth and quality of rice culture?
(2022) Qi, Peng; Wang, Zhichong; Wang, Changling; Xu, Lin; Jia, Xiaoming; Zhang, Yang; Wang, Shubo; Han, Leng; Li, Tian; Chen, Bo; Li, Chunyu; Mei, Changjun; Pan, Yayun; Zhang, Wei; Müller, Joachim; Liu, Yajia; He, Xiongkui
The agronomic processes are complex in rice production. The mechanization efficiency is low in seeding, fertilization, and pesticide application, which is labor-intensive and time-consuming. Currently, many kinds of research focus on the single operation of UAVs on rice, but there is a paucity of comprehensive applications for the whole process of seeding, fertilization, and pesticide application. Based on the previous research synthetically, a multifunctional unmanned aerial vehicle (mUAV) was designed for rice planting management based on the intelligent operation platform, which realized three functions of seeding, fertilizer spreading, and pesticide application on the same flight platform. Computational fluid dynamics (CFD) simulations were used for machine design. Field trials were used to measure operating parameters. Finally, a comparative experimental analysis of the whole process was conducted by comparing the cultivation patterns of mUAV seeding (T1) with mechanical rice direct seeder (T2), and mechanical rice transplanter (T3). The comprehensive benefit of different rice management processes was evaluated. The results showed that the downwash wind field of the mUAV fluctuated widely from 0 to 1.5 m, with the spreading height of 2.5 m, and the pesticide application height of 3 m, which meet the operational requirements. There was no significant difference in yield between T1, T2, and T3 test areas, while the differences in operational efficiency and input labor costs were large. In the sowing stage, T1 had obvious advantages since the working efficiency was 2.2 times higher than T2, and the labor cost was reduced by 68.5%. The advantages were more obvious compared to T3, the working efficiency was 4 times higher than in T3, and the labor cost was reduced by 82.5%. During the pesticide application, T1 still had an advantage, but it was not a significant increase in advantage relative to the seeding stage, in which operating efficiency increased by 1.3 times and labor costs were reduced by 25%. However, the fertilization of T1 was not advantageous due to load and other limitations. Compared to T2 and T3, operational efficiency was reduced by 80% and labor costs increased by 14.3%. It is hoped that this research will provide new equipment for rice cultivation patterns in different environments, while improving rice mechanization, reducing labor inputs, and lowering costs.
Effect of operational parameters of unmanned aerial vehicle (UAV) on droplet deposition in trellised pear orchard
(2023) Qi, Peng; Zhang, Lanting; Wang, Zhichong; Han, Hu; Müller, Joachim; Li, Tian; Wang, Changling; Huang, Zhan; He, Miao; Liu, Yajia; He, Xiongkui
Background: Unmanned Aerial Vehicles (UAVs) are increasingly being used commercially for crop protection in East Asia as a new type of equipment for pesticide applications, which is receiving more and more attention worldwide. A new model of pear cultivation called the ‘Double Primary Branches Along the Row Flat Type’ standard trellised pear orchards (FT orchard) is widely used in China, Japan, Korea, and other Asian countries because it saves manpower and is suitable for mechanization compared to traditional spindle and open-center cultivation. The disease and pest efficacy of the flat-type trellised canopy structure of this cultivation is a great challenge. Therefore, a UAV spraying trial was conducted in an FT orchard, and a four-factor (SV: Spray application volume rate, FS: Flight speed, FH: Flight height, FD: Flight direction) and 3-level orthogonal test were designed. Results: These data were used to analyze the effect, including spray coverage, deposit density, coefficient of variation, and penetration coefficient on the canopy, to determine the optimal operating parameters of the UAV for pest efficacy in FT orchards. The analysis of extremes of variance showed that factor FD had a significant effect on both spray coverage and deposition density. Followed by factor FS, which had a greater effect on spray coverage (p < 0.05), and factor SV, FH, which had a greater effect on deposition density (p < 0.05). The effects of different factors on spray coverage and deposit density were FD > FS > FH > SV, FD > FH > SV > FS, in that order. The SV3-FS1-FH1-FD3, which flight along the row with an application rate of 90 L/ha, a flight speed of 1.5 m/s, and a flight height of 4.5 m, was the optimal combination, which produced the highest deposit density and spray coverage. It was determined through univariate analysis of all experimental groups, using droplet density of 25/cm2 and spray coverage of 1%, and uniformity of 40% as the measurement index, that T4 and T8 performed the best and could meet the control requirements in different horizontal and vertical directions of the pear canopy. The parameters were as follows: flight along the tree rows, application rate not less than 75 L/ha, flight speed no more than 2 m/s, and flight height not higher than 5 m. Conclusion: This article provides ample data to promote innovation in the use of UAVs for crop protection programs in pergola/vertical trellis system orchards such as FT orchards. At the same time, this project provided a comprehensive analysis of canopy deposition methods and associated recommendations for UAV development and applications.
Effects of different ground segmentation methods on the accuracy of UAV-based canopy volume measurements
(2024) Han, Leng; Wang, Zhichong; He, Miao; He, Xiongkui; Han, Leng; College of Science, China Agricultural University, Beijing, China; Wang, Zhichong; Tropics and Subtropics Group, Institute of Agricultural Engineering, University of Hohenheim, Stuttgart, Germany; He, Miao; College of Science, China Agricultural University, Beijing, China; He, Xiongkui; College of Science, China Agricultural University, Beijing, China
The nonuniform distribution of fruit tree canopies in space poses a challenge for precision management. In recent years, with the development of Structure from Motion (SFM) technology, unmanned aerial vehicle (UAV) remote sensing has been widely used to measure canopy features in orchards to balance efficiency and accuracy. A pipeline of canopy volume measurement based on UAV remote sensing was developed, in which RGB and digital surface model (DSM) orthophotos were constructed from captured RGB images, and then the canopy was segmented using U-Net, OTSU, and RANSAC methods, and the volume was calculated. The accuracy of the segmentation and the canopy volume measurement were compared. The results show that the U-Net trained with RGB and DSM achieves the best accuracy in the segmentation task, with mean intersection of concatenation (MIoU) of 84.75% and mean pixel accuracy (MPA) of 92.58%. However, in the canopy volume estimation task, the U-Net trained with DSM only achieved the best accuracy with Root mean square error (RMSE) of 0.410 m 3 , relative root mean square error (rRMSE) of 6.40%, and mean absolute percentage error (MAPE) of 4.74%. The deep learning-based segmentation method achieved higher accuracy in both the segmentation task and the canopy volume measurement task. For canopy volumes up to 7.50 m 3 , OTSU and RANSAC achieve an RMSE of 0.521 m 3 and 0.580 m 3 , respectively. Therefore, in the case of manually labeled datasets, the use of U-Net to segment the canopy region can achieve higher accuracy of canopy volume measurement. If it is difficult to cover the cost of data labeling, ground segmentation using partitioned OTSU can yield more accurate canopy volumes than RANSAC.
Evaluation of the effects of airflow distribution patterns on deposit coverage and spray penetration in multi-unit air-assisted sprayer
(2022) Li, Tian; Qi, Peng; Wang, Zhichong; Xu, Shaoqing; Huang, Zhan; Han, Leng; He, Xiongkui
Efficient utilization is a pre-requisite for pesticide reduction, and appropriate airflow distribution pattern plays a key role in enhancing the effectiveness of pesticide application by air-assisted orchard sprayers, yet the mechanism of this is unclear. In order to clarify the specific effects of airflow velocity and direction on spraying efficacy, a series of spray tests on pear and cherry and airflow distribution tests in open areas were conducted by a multi-unit air-assisted sprayer on ten different fan settings. Several deposit indicators were analyzed and contrasted with the air distribution. The results showed that an increase in airflow velocity inside the canopy improved the abaxial side deposit coverage of both pear (from 3.33% to 11.80% in the Top canopy and from 6.26% to 11.00% in the Upper canopy) and cherry leaves (from 3.61% to 10.87% in the Top canopy, from 1.36% to 9.04% in the Middle canopy, and from 3.40% to 9.04% in the Bottom canopy), but had no significant effect on the spray penetration. The correlation between deposit indicators and airflow velocities/directions was evaluated, and the results indicated that the enhanced airflow velocities, both in the forward and horizontal direction, improved the abaxial side deposit coverage (CAB) on the outside of pear canopy (p < 0.001), but for cherry, none of the airflow indicators had a significant impact on the CAB independently. On the other hand, the increased airflow direction angle in the cross-row plane for pear, as well as the increased airflow velocities in forward and vertical direction for cherry, both showed negative effects on the adaxial side deposit coverage (p < 0.01). The findings in this study might be helpful to improve the performance of pesticide application in orchards, especially for abaxial side deposition, and could provide a reference for the further investigations about the effect of airflow on spray canopy deposition.
Maize characteristics estimation and classification by spectral data under two soil phosphorus levels
(2022) Qiao, Baiyu; He, Xiongkui; Liu, Yajia; Zhang, Hao; Zhang, Lanting; Liu, Limin; Reineke, Alice-Jacqueline; Liu, Wenxin; Müller, Joachim
As an essential element, the effect of Phosphorus (P) on plant growth is very significant. In the early growth stage of maize, it has a high sensitivity to the deficiency of phosphorus. The main purpose of this paper is to monitor the maize status under two phosphorus levels in soil by a nondestructive testing method and identify different phosphorus treatments by spectral data. Here, the Analytical Spectral Devices (ASD) spectrometer was used to obtain canopy spectral data of 30 maize inbred lines in two P-level fields, whose reflectance differences were compared and the sensitive bands of P were discovered. Leaf Area Index (LAI) and yield under two P levels were quantitatively analyzed, and the responses of different varieties to P content in soil were observed. In addition, the correlations between 13 vegetation indexes and eight phenotypic parameters were compared under two P levels so as to find out the best vegetation index for maize characteristics estimation. A Back Propagation (BP) neural network was used to evaluate leaf area index and yield, and the corresponding prediction model was established. In order to classify different P levels of soil, the method of support vector machine (SVM) was applied. The results showed that the sensitive bands of P for maize canopy included 763 nm, 815 nm, and 900–1000 nm. P-stress had a significant effect on LAI and yield of most varieties, whose reduction rate reached 41% as a whole. In addition, it was found that the correlations between vegetation indexes and phenotypic parameters were weakened under low-P level. The regression coefficients of 0.75 and 0.5 for the prediction models of LAI and yield were found by combining the spectral data under two P levels. For the P-level identification in soil, the classification accuracy could reach above 86%. These abilities potentially allow for phenotypic parameters prediction of maize plants by spectral data and different phosphorus contents identification with unknown phosphorus fertilizer status.
Method of 3D voxel prescription map construction in digital orchard management based on LiDAR-RTK boarded on a UGV
(2023) Han, Leng; Wang, Shubo; Wang, Zhichong; Jin, Liujian; He, Xiongkui
Precision application of pesticides based on tree canopy characteristics such as tree height is more environmentally friendly and healthier for humans. Offline prescription maps can be used to achieve precise pesticide application at low cost. To obtain a complete point cloud with detailed tree canopy information in orchards, a LiDAR-RTK fusion information acquisition system was developed on an all-terrain vehicle (ATV) with an autonomous driving system. The point cloud was transformed into a geographic coordinate system for registration, and the Random sample consensus (RANSAC) was used to segment it into ground and canopy. A 3D voxel prescription map with a unit size of 0.25 m was constructed from the tree canopy point cloud. The height of 20 trees was geometrically measured to evaluate the accuracy of the voxel prescription map. The results showed that the RMSE between tree height calculated from the LiDAR obtained point cloud and the actual measured tree height was 0.42 m, the relative RMSE (rRMSE) was 10.86%, and the mean of absolute percentage error (MAPE) was 8.16%. The developed LiDAR-RTK fusion acquisition system can generate 3D prescription maps that meet the requirements of precision pesticide application. The information acquisition system of developed LiDAR-RTK fusion could construct 3D prescription maps autonomously that match the application requirements in digital orchard management.
Remote sensing of maize plant height at different growth stages using UAV-based digital surface models (DSM)
(2022) Oehme, Leon Hinrich; Reineke, Alice-Jacqueline; Weiß, Thea Mi; Würschum, Tobias; He, Xiongkui; Müller, Joachim
Plant height of maize is related to lodging resistance and yield and is highly heritable but also polygenic, and thus is an important trait in maize breeding. Various manual methods exist to determine the plant height of maize, yet they are labor-intensive and time consuming. Therefore, we established digital surface models (DSM) based on RGB-images captured by an unmanned aerial vehicle (UAV) at five different dates throughout the growth period to rapidly estimate plant height of 400 maize genotypes. The UAV-based estimation of plant height (PHUAV) was compared to the manual measurement from the ground to the highest leaf (PHL), to the tip of the manually straightened highest leaf (PHS) and, on the final date, to the top of the tassel (PHT). The best results were obtained for estimating both PHL (0.44 ≤ R2 ≤ 0.51) and PHS (0.50 ≤ R2 ≤ 0.61) from 39 to 68 days after sowing (DAS). After calibration the mean absolute percentage error (MAPE) between PHUAV and PHS was in a range from 12.07% to 19.62%. It is recommended to apply UAV-based maize height estimation from 0.2 m average plant height to maturity before the plants start to senesce and change the leaf color.
Visualization of lidar-based 3D droplet distribution detection for air-assisted spraying
(2023) Wang, Zhichong; Zhang, Yang; Li, Tian; Müller, Joachim; He, Xiongkui
Air-assisted spraying is a commonly used spraying method for orchard plant protection operations. However, its spraying parameters have complex effects on droplet distribution. The lack of large-scale 3D droplet density distribution measurement methods of equipment has limited the optimization of spraying parameters. Therefore, there is a need to develop a method that can quickly obtain 3D droplet distribution. In this study, a 2D LiDAR was used to quickly scan moving droplets in the air, and a test method that can obtain the visualization of 3D droplet distribution was constructed by using the traveling mode of the machine perpendicular to the scanning plane. The 3D droplet distribution at different positions of the nozzle installed in the air-assisted system was tested at different fan rotation speeds, and the methods for signal processing, point cloud noise reduction, and point cloud division for 2D LiDAR were developed. The results showed that the LiDAR-based method for detecting 3D droplet distribution is feasible, fast, and environmentally friendly.