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Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Foreign fiber cleaner was widely used in the field of cotton processing industry for its good cleaning efficiency. However, with the improvement of cotton processing level, how to improve the foreign fiber's cleaning efficiency and reduce the fiber's damage had become a growing concern, which was taken as the research object in the paper. This paper designed four-factor quadratic orthogonal rotation test. The four factors were the capacity of foreign fiber cleaner, the regain of seed cotton, the winding roller speed and the wind speed. The cleaning efficiency of foreign fiber and the damage of fiber length were objective functions. For the multi-factor test, orthogonal test design was an effective method of scientific experiments where part of the ones can reflect the comprehensive experiments. Through these factors quadratic orthogonal rotation test, the multi-objective optimal control model of foreign fiber cleaner was established. The results revealed that the remarkable factors which influenced the cleaning efficiency of linear foreign fiber were the winding roller speed, the regain of seed cotton and the wind speed, while the capacity of foreign fiber cleaner was not remarkable. The regain of seed cotton and winding roller speed had significantly interactive effect for the cleaning efficiency of linear foreign fiber. Higher winding roller speed, wind speed and lower regain of seed cotton would improve the cleaning efficiency. The effects on the cleaning efficiency of linear foreign fiber were in order of winding roller speed > wind speed > regain of seed cotton > capacity of foreign fiber cleaner. The effects on the cleaning efficiency of flake foreign fiber were in order of wind speed > winding roller speed > regain of seed cotton > capacity of foreign fiber cleaner. All of the factors were the remarkable factors. The effects on the damage of fiber length were in order of winding roller speed > regain of seed cotton > wind speed > capacity of foreign fiber cleaner. The remarkable factors which influenced the damage of fiber length were the winding roller speed, the regain of seed cotton and the wind speed. The model in this paper has been applied in the multi-objective optimization control of foreign fiber cleaner. Users could adjust the parameters of foreign fiber cleaning machine according to their demand in the actual production. In the conditions of this study, Compared with a fixed value control method, the cleaning efficiency of linear foreign fiber by using multi-objective adaptive control method was increased on average by about 9.4%, the cleaning efficiency of flake foreign fiber was increased on average by about 3.3%, and the damage of fiber length was reduced on average by about 0.051%. Cleaning effect was satisfactory. [ABSTRACT FROM AUTHOR]

Lotus seed has very high nutritional and medicinal value, which consists of lotus shell, lotus kernel and lotus core. Because of the bitter taste, the lotus cores need to be removed before eating. The existing equipment for removing core can remove the lotus core fast by operating the coring bit. Before drilling, the lotus kernel has to be centered in the centering working position. The centering device generally consists of two rotating rollers. Under the action of gravity and friction force, the lotus kernel can be centered in the rotating process. However, the position of the coring bit is fixed in the centering working position, and therefore the drilling deviation occurs due to the diameter variation of lotus kernels, and the lotus cores cannot be removed effectively, which lowers the quality of lotus kernels directly. To solve the above problems, the classification process is applied widely. However, it is ineffective owing to the inaccuracy of the classification process and the high requirement for staff and equipment. In order to avoid this situation, the shape parameters of lotus kernels are firstly measured and analyzed in this paper. The results show that the maximum cross section of the lotus kernel is almost round with a mean roundness deviation of 0.46 mm and the diameter ranges from 9 to 16 mm; the average chordwise curvature radius is 7.764 mm, and the average longitudinal curvature radius is 5.177 mm, therefore, the shape of a lotus kernel can approximate to an ellipsoid. Moreover, the centering deviation of the double roller rolling centering method presents a maximum value of 6.654 mm. Aiming at solving the problem of drilling deviation in the automated coring of lotus seeds, this paper proposes a self-adaptive centering method for lotus seeds based on a slider-crank mechanism and a parallel crank mechanism. By utilizing this method, the position of the coring bit can be automatically adjusted according to the lotus kernel size. A mathematical model of self-adaptive centering is established by the complex number vector method. Once the parameters of the linkages are selected, the centering deviation value is determined. For the self-adaptive centering mechanism, the optimization of the linkage parameters is complicated and difficult to meet the demand through traditional optimization methods. Therefore, in order to make the centering deviation minimum, by utilizing the stochastic optimization algorithm of Cuckoo Search, this paper optimizes the linkage parameters in MATLAB. The initial parameters are as follows: the nest number is 25, the probability of detection is 0.25, and the maximum iteration number is 4×105. The optimized parameters of connecting rod are 88.51, 135.72, 0.12828, and 125.35 mm respectively, and the coaxial deviation between the coring bit axis and lotus kernel axis is lower than 0.05mm. Based on the above study, a self-adaptive machine for removing core of lotus seed was developed and a coring experiment with 400 samples of lotus seeds was carried out. The experimental parameters were as follows: the working cycle was 1.5 s, the roller's rotating speed was 238 r/min, the pressure of the pressing wheel was 25 N, the coring bit diameter was 2.5 mm and the drill speed was 2800 r/min. By measuring the circle run-out value at the maximum cross section of the lotus kernel, the drilling accuracy of lotus seeds were calculated. The results show that the drilling accuracy of a self-adaptive machine for removing core of lotus seed is 91.25%, much higher than that of the existing equipment, which is 60%-70%; moreover, this machine can handle different sizes of lotus kernels. The experimental results verify the effectiveness and practicability of this self-adaptive centering method. [ABSTRACT FROM AUTHOR]

In order to identify and avoid risks during the breeding process of laying hens, it is necessary to acquire real-time and continuous data of laying hens' physiology and production parameters. At present, data acquisition and processing is mainly dependent on the manual mode, which is a very costly and time consuming process. Meanwhile, the data obtained from human observation involves strong subjectivity, which could be disadvantageous for carrying out accurate, stable, and continuous recording. In addition, the manual mode of data measurement will affect the living environment and physiological state of laying hens, and even cause stress reactions. This will, in turn, affect the accuracy of data monitoring. Therefore, if there is an approach to collect all kinds of physiology and production parameters of laying hens in the natural state, the acquired data will be more accurate and closer to the actual situation of the testing objects. On the basis of previous studies, this paper proposes an automatic data monitoring system for laying hens' production parameters based on the sensor network technology. This system can collect 6 types of production parameters by using a multiple-sensor module, including food intake, water intake, excretion volume, egg production time, egg quantity, and egg quality. After an information integration process using a microprocessor, the acquired data will be transmitted back to the server end through the network. Thus, the remote data acquisition and the visualized data processing are realized under natural conditions. The author adopted the design method of man-machine engineering, and organically combined traditional cages with a sensor system. This new structure design ensures that each cage only holds one laying hen, and each hen has its own feed tank, water tank, egg collection plate, and excretion collection plate. The system therefore enables data acquisition of individual laying hens. The cages are built in modules, which is friendly for scale production, transportation, installation, and customization. In terms of data communication, this system applies the transmitter technology and expands the node access quantity of the RS 485 bus. The number of laying hens that can be simultaneously monitored on a single bus is up to 500. The data acquisition server reads the measurements of each sensor periodically by using the polling method. This paper discusses the factors influencing of the polling cycle, and provides a formula to determine its appropriate value. The paper also elaborates on the communication process between server and sensor, and describes the working principles of the sensors for food intake, water intake, excretion volume, egg production time, egg quantity, and egg quality respectively. Then data processing results are visualized in a variety of forms and tables. The results indicated that this system operates stably; the average relative error of food intake, water intake, excretion volume, and egg quality is below 0.2%. This study significantly improves the accuracy of data monitoring, and provides a new method and approach for the acquisition and analysis of scientific data. It also establishes meaningful references for the further research of automatic and interference-free data acquisition technology of laying hens during the breeding process. [ABSTRACT FROM AUTHOR]

Chrysanthemum is one of the top 10 traditional famous flowers in China, which has significant importance and great ornamental value and medicinal value. The chrysanthemum flowers are characterized by a large number of varieties and a wide range of petal shapes, which pose big challenges to their intelligent identification and efficient management. Currently, the identification and management of chrysanthemum mainly relies on the traditional manual way, and as a result, the efficiency is quite low. At the contemporary era, deep learning as a powerful technique in artificial intelligence field is becoming a prevalent way of identification and classification on text, image, video, and so on. Based on the end-to-end convolutional neural network deep neural network directly acting on the original chrysanthemum image dataset, this paper aims at obtaining the characteristic information of chrysanthemums through the multi-layer neural network. By this means, the problem of extracting the features manually is avoided, and then optimization target function is applied to achieve a better image recognition accuracy. Based on this, the system of chrysanthemum flower pattern intelligent recognition and breed classification is researched and implemented. In view of the subtle differences among the flower patterns of chrysanthemums, on the one hand, in order to preserve as much information as possible for the data, tensor is employed to represent the image data; on the other hand, the pairwise confusion loss function based on pair similarity is used to distinguish pattern differences and similarities. By this means, the objective function of distinguishing the different flower patterns is realized on the fine grain size. The system not only can identify the chrysanthemum pattern, but also can give the probability value of the top 3 results. In addition to this, the variety information covered by the flower pattern is also provided. The operation of the system can be divided into 2 stages: the off-line training and the online classification. Off-line models can be hosted in the cloud environment such as Amazon AWS for the easy usage on the mobile platform. Moreover, the model can be replanted and updated with little hindrance. In order to train the network model, we collected a large amount of data of real chrysanthemum image, and manually marked the relevant pattern and category information of chrysanthemum. Based on the datasets, we conducted extensive experiments with our system and made comparisons with 3 existing systems, and experimental results show that: The identification accuracy of the system has been significantly improved compared with the existing systems for chrysanthemum flower pattern. Beyond that, the system can provide more detailed chrysanthemum species information at the same time. The average recognition rate can reach about 0.95, and even surpass the rate of 0.98 for some chrysanthemum patterns. The system provides a powerful means for the automatic management of chrysanthemum and fills the gaps in chrysanthemum pattern recognition and classification. In this paper, the research on the intelligent identification and effective management of chrysanthemum flower pattern has great significance in theory and practice. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Since the numbers and capacities of dairy cattle farms are increasing with every passing day, computer aided studies for the management of these farms become more important and are widely used in daily life. In this study, we assessed the potential of Xtion as a point cloud data acquisition system for the body measurements of Holstein cows in the context of dairy cow conformation. For this purpose, we selected Xtion for point cloud data acquisition and chose a dairy cow model and a dairy cow as the experimental subjects. In an indoor environment, three dimensional laser scanners with high precision were employed to get point cloud data of the cow model as comparison data, and Xtion was used for acquiring point cloud data at different distances away from the cow model as test data. After aligning the comparison data and test data by using commercial point cloud processing software, statistical errors between comparison data and test data including positive maximum distance, negative maximum distance, positive average distance, negative average distance, standard deviation, and RMS error were calculated by the professional point cloud software in order to quantitatively analyze the precision and density of point cloud data from Xtion at different distances. The test results indicate that the errors almost increased linearly with increasing of the distance between Xtion and subject. The average errors were less than ±5mm on the condition that the distance from Xtion to the subject were between 0.6m and 1.2m. The average errors were less than ±10mm on the condition that the distance between Xtion and the subject were between 1.2m and 1.8m. Maximum errors were always less than ±20mm. The density of point clouds decreased exponentially with increasing of the distance from Xtion to the subject to such an extent that body measurements were impossible to get on account of missing the mark points on the body. These results imply that the appropriate distance between Xtion and a cow is between 0.6m and 1.2m. Under the dairy cow breeding environment, body measurements including chest width, thurl width, rear leg side view, front nipple length, rear udder width, and rear udder height of cows were first determined manually, by direct measurement. Then we used Xtion to acquire point cloud data at a distance of less than 1.2 meters from the cow. Visualization of the point cloud data and interactive measurement of the cow body based on point cloud data from Xtion were performed by Meshlab so as to qualitatively analyze the sunlight and surface materials’ influence on the quality of point cloud data acquired from Xtion under dairy farm conditions. The results indicate that Xtion failed to give normal operation under the sunlight due to the presence of the IR rays in the sunlight. Point cloud acquisition from Xtion may have some small holes due to the material and appearance of a live cow body. The relative errors of comparing the body measurements obtained by Meshlab with the manual measurements were less than 10%, which indicates that Xtion is appropriate for body measurements of Holstein cows. [ABSTRACT FROM AUTHOR]