Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-02-17 Cooperative journals: 《智慧农业（中英文）》
Abstract： The detecting and grading of the internal quality of apples is an effective means to increase the added value of apples, protect the health of residents, meet consumer demand and improve market competitiveness. Therefore, an apple internal quality detecting module and a grading module were developed in this research to constitute a movable apple internal quality orchard origin grading system, which could realize the detection of apple sugar content and apple moldy core in orchard origin and grading according to the set grading standard. Based on this system, a multiplicative effect elimination (MEE) based spectral correction method was proposed to eliminate the multiplicative effect caused by the differences in physical properties of apples and improve the internal quality detection accuracy. The method assumed that the multiplication coefficient in the spectrum was closely related to the spectral data at a certain wavelength, and divided the original spectrum by the data at this wavelength point to achieve the elimination of the multiplicative scattering effect of the spectrum. It also combined the idea of least-squares loss function to set the loss function to solve for the optimal multiplication coefficient point. To verify the validity of the method, after pre-processing the apple spectra with multiple scattering correction (MSC), standard normal variate transform (SNV), and MEE algorithms, the partial least squares regression (PLSR) prediction models for apple sugar content and partial least squares-discriminant analysis (PLS-DA) models for apple moldy core were developed, respectively. The results showed that the MEE algorithm had the best results compared to the MSC and SNV algorithms. The correlation coefficient of correction set (Rc), root mean square error of correction set (RMSEC), the correlation coefficient of prediction set (Rp), and root mean square error of prediction set (RMSEP) for sugar content were 0.959, 0.430%, 0.929, and 0.592%, respectively; the sensitivity, specificity, and accuracy of correction set and prediction set for moldy core were 98.33%, 96.67%, 97.50%, 100.00%, 90.00%, and 95.00%, respectively. The best prediction model established was imported into the system for grading tests, and the results showed that the grading correct rate of the system was 90.00% and the grading speed was 3 pcs/s. In summary, the proposed spectral correction method is more suitable for apple transmission spectral correction. The mobile orchard local grading system of apple internal quality combined with the proposed spectral correction method can accurately detect apple sugar content and apple moldy core. The system meets the demand for internal quality detecting and grading of apples in orchard production areas.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-02-17 Cooperative journals: 《智慧农业（中英文）》
Abstract： To realize the autonomous navigation and automatic target spraying of intelligent plant protect machinery in orchard, in this study, an autonomous navigation and automatic target spraying robot for orchards was developed. Firstly, a single 3D light detection and ranging (LiDAR) was used to collect fruit trees and other information around the robot. The region of interest (ROI) was determined using information on the fruit trees in the orchard (plant spacing, plant height, and row spacing), as well as the fundamental LiDAR parameters. Additionally, it must be ensured that LiDAR was used to detect the canopy information of a whole fruit tree in the ROI. Secondly, the point clouds within the ROI was two-dimension processing to obtain the fruit tree center of mass coordinates. The coordinate was the location of the fruit trees. Based on the location of the fruit trees, the row lines of fruit tree were obtained by random sample consensus (RANSAC) algorithm. The center line (navigation line) of the fruit tree row within ROI was obtained through the fruit tree row lines. The robot was controlled to drive along the center line by the angular velocity signal transmitted from the computer. Next, the ATRS's body speed and position were determined by encoders and the inertial measurement unit (IMU). And the collected fruit tree zoned canopy information was corrected by IMU. The presence or absence of fruit tree zoned canopy was judged by the logical algorithm designed. Finally, the nozzles were controlled to spray or not according to the presence or absence of corresponding zoned canopy. The conclusions were obtained. The maximum lateral deviation of the robot during autonomous navigation was 21.8 cm, and the maximum course deviation angle was 4.02�. Compared with traditional spraying, the automatic target spraying designed in this study reduced pesticide volume, air drift and ground loss by 20.06%, 38.68% and 51.40%, respectively. There was no significant difference between the automatic target spraying and the traditional spraying in terms of the percentage of air drift. In terms of the percentage of ground loss, automatic target spraying had 43% at the bottom of the test fruit trees and 29% and 28% at the middle of the test fruit trees and the left and right neighboring fruit trees. But in traditional spraying, the percentage of ground loss was, in that sequence, 25%, 38%, and 37%. The robot developted can realize autonomous navigation while ensuring the spraying effect, reducing the pesticides volume and loss.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-02-17 Cooperative journals: 《智慧农业（中英文）》
Abstract： To solve the problems of low level of digitalization of orchard management and relatively single construction method, a three-dimensional virtual orchard construction method based on laser point cloud was proposed in this research. First, the hand-held 3D point cloud acquistion equipment (3D-BOX) combined with the lidar odometry and mapping (SLAM-LOAM) algorithm was used to complete the acquisition of the point cloud data set of orchard; then the outliers and noise points of the point cloud data were removed by using the statistical filtering algorithm, which was based on the K-neighbor distance statistical method. To achieve this, a distance threshold model for removing noise points was established. When a discrete point exceeded, it would be marked as an outlier, and the point was separated from the point cloud dataset to achieve the effect of discrete point filtering. The VoxelGrid filter was used for down sampling, the cloth simulation filtering (CSF) cloth simulation algorithm was used to calculate the distance between the cloth grid points and the corresponding laser point cloud, and the distinction between ground points and non-ground points was achieved by dividing the distance threshold, and when combined with the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm, ground removal and cluster segmentation of orchard were realized; finally, the Unity3D engine was used to build a virtual orchard roaming scene, and convert the real-time GPS data of the operating equipment from the WGS-84 coordinate system to the Gauss projection plane coordinate system through Gaussian projection forward calculation. The real-time trajectory of the equipment was displayed through the LineRenderer, which realized the visual display of the motion trajectory control and operation trajectory of the working machine. In order to verify the effectiveness of the virtual orchard construction method, the test of orchard construction method was carried out in the Begonia fruit and the mango orchard. The results showed that the proposed point cloud data processing method could achieve the accuracy of cluster segmentation of Begonia fruit trees and mango trees 95.3% and 98.2%, respectively. Compared with the row spacing and plant spacing of fruit trees in the actual mango orchard, the average inter-row error of the virtual mango orchard was about 3.5%, and the average inter-plant error was about 6.6%. And compared the virtual orchard constructed by Unity3D with the actual orchard, the proposed method can effectively reproduce the actual three-dimensional situation of the orchard, and obtain a better visualization effect, which provides a technical solution for the digital modeling and management of the orchard.
Subjects: Dynamic and Electric Engineering >> Engineering Thermophysics submitted time 2017-11-07 Cooperative journals: 《工程热物理学报》
Abstract：为了解干燥过程中玉米籽粒内部水分的扩散过程，优化干燥工艺参数，本文采用模拟研究和实验研究方法分析干燥过程中玉米籽粒内部水分分布随时间的变化规律。玉米籽粒由种皮、角质胚乳、粉质胚乳和胚四组分组成，四种组分水分扩散系数各不相同，本文分别假设玉米籽粒由单组分均质体和多组分非均质体组成，分别建立了玉米籽粒的干燥数学模型，利用COMSOL Multiphysics模块模拟研究了玉米籽粒内部的水分变化情况，并通过玉米籽粒的薄层干燥实验进行了实验验证。研究结果表明，建立的两个模型均可有效模拟玉米籽粒薄层干燥过程;80℃的模拟值与实验值的差异较70℃的小;玉米籽粒多组分模型精度优于单组分模型精度。
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