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： In order to solve the problems of high labor intensity, low efficiency of manual operation and lack of supporting machinery in the fruit harvesting of modern orchards, a self-propelled orchard multi-station harvesting equipment was designed in combination with the fruit tree dwarf anvil wide-row dense planting mode and agronomic planting requirements. The whole machine structure and working principle of the self-propelled orchard multi-station harvesting equipment were expounded. According to the environmental conditions of mountainous orchards, the crawler chassis structure was designed, and the working speed was 0~2 km/h. The operating platform including left extension platform and right extension platform was designed according to the difference of fruit tree row spacing, and the working width of the operating platform was 1500~2700 mm. In order to improve the working efficiency and ensure the same picking speed of upper and lower operators, the picking operation mode of "two sides, two heights and six stations" was proposed by comparing the difference in the working flexibility between the operator on the platform and the operator on the ground during the operation of the machine, and the in-and-out channels of fruit boxes and the automatic collection and packing device were designed. The front and rear unobstructed fruit box access system was composed of the front loading and unloading mechanism, the rear loading and unloading mechanism and the fruit box slide rail, which was convenient for the empty fruit box to enter the fruit loading station of the working platform from the front and unloading from the rear after the fruit was filled. Six sub-conveyor belts were designed to handle apples harvested by six non interacting operators at the same time. The prototype was test in the field, and the packing uniform distribution coefficient calculation method was proposed to evaluate the uniformity of fruit packing, and the performance of the prototype was comprehensively evaluated in combination with the fruit damage rate and packing speed. The results showed that, the designed self-propelled orchard multi-station harvesting equipment could synchronize with the six stations manual harvesting speed. At the same time, with the help of the expansion platform, the apple picking range covered the entire canopy of the fruit tree. The prototype worked smoothly, and the speed of each conveyor belt was in good coordination with manual picking, and there was no apple congestion occurred. The apple harvest damage rate was 4.67%, the packing uniform distribution coefficient was 1.475, and the packing speed was 72.9 apples per minute, which could meet the requirements of orchard harvest operation.
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： Protein, fat and somatic cells are three important reference indicators in milk purchase, which determine the quality and price of milk. The traditional chemical analysis methods of these indexes are time-consuming and pollute the environment, while the mid-infrared spectrum has the advantages of fast, non-destructive and simple operation. In order to realize the rapid classification of milk quality and improve the production efficiency of dairy enterprises, 3216 Holstein milk samples were chosen as the research objects and mid-infrared spectroscopy technology was applied to realize the detection and classification of 4 different quality milks during the purchase process. The spectrum was preprocessed by using the first derivative and the first difference, and combined with the algorithm competitive adaptive reweighted sampling (CARS) and the shuffled frog leaping algorithm (SFLA), the effective characteristic variables that could represent different milks were selected, and the SVM model was established. Among them, the penalty parameter c and the kernel function parameter g which were the key parameters of the SVM model were optimized by using the grid search method (GS), genetic algorithm (GA) and particle swarm algorithm (PSO). The training time of GS, GA and PSO algorithms were compared, the results showed that the training time of GS was much longer than that of GA and PSO algorithms.The SFLA algorithm was generally better than the CARS algorithm, and the PSO optimized the SVM model the best. After the first-order difference preprocessing, the PSO-SVM established by using the SFLA algorithm to filter the characteristic variables, the accuracy of the training set, the accuracy of the test set and the AUC were 97.8%, 95.6% and 0.96489, respectively. This model has a high accuracy rate and has practical application value in the milk industry.
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： Automatic grading method of pomelo fruit according to the shape and size is urgently needed in the industry since the work mainly depends on artificial judgment currently. In this research, a method, which detected the vertical and horizontal size of pomelo by using contour coordinate transformation fitting, fruit shape feature extraction and direction angle compensation algorithm, while it determined the shape defects based on fruit shape index, was proposed. The image acquisition system was selfdesigned and built up with a CMOS camera, a dot matrix LED light source, a plane mirror, the computer, a box and brackets. The image data containing whole surface information of Shatian pomelo samples with different sizes and shapes were collected by this system. The G-B component grayscale image was chosen for denoising and segmentation. The Laplacian edge detection algorithm was implemented to extract the edge pixels of the fruit. The polynomial fitting method was applied to converse the rectangular coordinates to polar coordinates so that the fruit shape description was simplified. The characteristic point polar angle value was used to compensate the random direction of the vertical and horizontal diameters of the sample. Then the vertical and horizontal diameters of fruit were calculated after classifying the sample shapes into the spherical and the pear-like categories. For the involved 168 pomelo samples, the average error, maximum absolute error and average relative error of the vertical diameters were 2.23 mm, 7.39 mm and 1.6% respectively, while these parameters of the horizontal diameters were 2.21 mm, 7.66 mm and 1.4% respectively. The fruit shape discriminant model was established by using BP neural network algorithm based on the seven features extracted from the fitting function and verified by independent validation set including 3 peak heights, 3 peak widths and 1 trough value difference. The total recognition rate of shape identification was 83.7%. The results illustrated that the method had the potential to measuring the pomelo size and shape for grading fast and non-destructively.
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