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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.)

Heterologous image fusion has been widely used to integrate multiple images into one. The fused images also present a higher definition, more significant edge intensity, and more information than the source image. There are different characteristics of image data collected by the various types of sensors. Among them, the depth sensor imaging used the Time of Flight (ToF) to realize the distance calculation using the ToF near-infrared light. The beneficial supplement has been commonly used for the visible light camera. The broad application can also be expected in the agriculture, medical treatment, quality inspection, and vision fields. However, the image acquisition of a single natural scene cannot fully meet the requirements for rapid and accurate identification of the fruits and positioning targets. The image fusion can be extended to the heterologous vision system using multi-objective optimization, particularly in the field of natural scenes. In this study, a multi-scale decomposition and dual optimization strategy was proposed to simplify the ToF and visible-light image fusion in an apple orchard using the Simplified Pulse Coupled Neural Network (SPCNN). A double strategy with parameter optimization was introduced into the SPCNN model for the fusion of Nonsubsampled Contourlet Transform (NSCT). The model included the registration module, coding area, multi-scale decomposition module, single target SPCNN fusion model, multi-target SPCNN fusion model, and decoding area. The heterologous vision system was also used to accurately register the ToF and visible light images. Four parameters of SPCNN model were encoded, including the link channel feedback term, link strength, dynamic threshold attenuation factor, and dynamic threshold amplification factor. The NSCT was used to decompose the image at multiple scales. The fusion rules in the SPCNN model were adopted with the improved artificial bee colony algorithm and double optimization, including the single- and multi-objective parameter optimization. Each binary vector was converted into the real parameters using the decoding area. The objective function of the double optimization was used as the iteration termination of the SPCNN model. Finally, the heterogeneous image fusion was implemented after the multi-scale inverse transformation. There was improved parameter optimization and iteration times of SPCNN model. The adaptive ignition times of the model were relatively low (about 3-7 times), indicating low ignition times, adaptive segmentation, and high efficiency. The success rate of ignition recognition reached 100.00%, and the minimum duration of ignition division reached 91.91 s at 15:00. Specifically, the success rate of fusion image recognition also reached 100.00% under different periods, including strong, medium, and weak light at 12:00, 15:00, 18:20, and 19:00, compared with the rest fusion models. The fusion time was much lower than that of SPCNN model, with a minimum of 92.68 s. The four fusion indexes were the largest in the weak light period of 18:20, including the average gradient, correlation coefficient, mutual information, entropy, and spatial frequency. The proposed model presented an excellent performance in accuracy, time-consuming, and model size. The finding can provide a supplement to the image hierarchical fusion. [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.)

Image registration has been a key preprocessing technique for heterogeneous images. Two kinds of image data were transformed into the same coordinate system by matching, combining spatial information and semantic information. Multi-source images can be gained to reduce the limitations of a single data source. This study aims to improve the matching accuracy between Time of Flight (ToF) and visible heterologous images collected by the binocular acquisition system in an orchard environment. A target salient region extraction was proposed using the local peak, pulse coupled neural network (pCNN) segmentation and the expectation-maximization for the ToF and visible image registration in an apple orchard. Firstly, the significant region in the visible image was calculated to preprocess the red and green components of the visible image, according to the Gaussian difference function. Taking the two-dimensional normal distribution of the local gray value in the image as the target component, the maximum interclass variance Otsu was used to extract the prospect with a fixed threshold as the local peak extraction strategy, then to preliminarily screen the characteristic areas of ToF and visible images, in order to improve the pCNN dynamic threshold using the maximum expectation. The link strength was calculated using the local image gradient. An Expectation-Maximization (EM) pCNN was proposed to refine the pre-selected region using the image region variance for a better termination condition. The invariant moment of the connected region was calculated to locate the same name point of the target center using the invariant principle, where the characteristic region was further screened. Finally, the same name points were purified by the Random Sample Consensus (RANSAC), where the purified coordinates of the same name points were substituted into the transformation model to calculate the model parameters for the registration. The experimental images were collected under three conditions, including a sunny day, shade, and weak light, in order to simulate different lighting conditions. The translation, spatial rotation, and scaling were performed on each group of ToF and visible images. The experimental results show that the EM-PCNN was better than the traditional segmentation for the image with less obvious color difference between fruit and growth background. Under normal conditions, the segmentation accuracy and the exposure were 96.62% and 73.84% lower, respectively. After that, the feature regions were screened to perform fine segmentation using EM-PCNN. There were smaller differences in regional features between the ToF and visible light, compared with the traditional Maximally Stable Extremal Regions (MSER), indicating a more accurate range. Harris algorithm was suitable for the image registration with the small scale change and rotation angle. The scale-invariant feature transform (Sift) algorithm presented the resistance to the translation, rotation, and scaling, where the complete registration was realized with the rotation at the spatial level. Consequently, the local peak extraction and EM-PCNN segmentation can be widely expected to register the ToF and visible images in the apple orchard, where the root mean square error was 3.05-4.75 under different lighting conditions, and the homonymous point was 3-5. The excellent registration was achieved for the ToF and visible heterologous images in an apple orchard under the binocular acquisition system, indicating better resistance to the image translation, rotation, and scaling. [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.)

In order to ensure safe storage of highly moist grain after harvest and alleviate grain loss, we took a dry circular steel grain storage silo with a vertical ventilation tube in its center as an example and simulated airflow in the silo filled with highly moist wheat grain after harvest, using CFD. The effect of the wheat grain on airflow was approximated by a porous medium model, and the static and dynamic pressure in the silo was analyzed. The results showed that the static and dynamic pressure and the flow rate decrease exponentially with distance from the ventilation tube. Horizontally, the radial flow rate and the flow velocity (flow rate through per unit areas) both decreased with the distance from the ventilation tube in a power law. Vertically, both the flow rate and velocity decreased exponentially with the height of the grain. The ventilation in the grain stack area was more uniform in the vertical (longitudinal) direction than in the transverse (radial) direction, and the air velocity in the upper and lower part of the grain in the silo was higher than that in region from the vertical ventilation wall to 0.9m away from the central ventilation tube. It was found that the annular baffle with radius of 0.5 m and 0.9m could cover the upper surface and bottom of the silo respectively to improve uniformity of the ventilation. The airflow in the bottom of the silo was highest, followed by the upper surface. The minimum flow rate in the silo wall was only 24.6%, meaning that 1/4 of the flow was discharged from the silo wall and 3/4 was from the upper and low sides. The volume of the exhaust of the silo wall was on the low side. Wind speed test results were consistent with the simulations, with an average difference of 16.35%. When pressurized air was flow into the ventilation, air flowed radially and the air velocity in the central silo was higher than that in region proximal to the silo wall. It was suggested that periodically ventilating the silo by blowing pressurized air followed by suction with vacuumed air can improved evenness of the air in the silo. The wind speed measurements in the silo were in good agreement with the simulations, proving that combing CFD with the porous media model was accurate to simulate airflow in the silo. The simulation results provide guidance for improving and optimizing silo management. [ABSTRACT FROM AUTHOR]

The quality of seedbed after seeding such as soil surface leveling, soil-breaking rate and seed furrow preparing and so on, are the main factors affecting the seedling rate of rapeseed. A good seedbed is a necessary condition for uniform seedling emergence, increasing the rate of subsequent seedlings and promoting the homogeneity of plants after sowing. In view of the fact that the measurement and data processing methods of rapeseed seedbed surface roughness under mechanized direct seeding cannot fully reflect the real soil surface condition, a method of measuring and quantifying roughness surface area by using laser radar scanning technology is proposed in this paper. The main components of the measuring device are the lidar, linear guide, stepper motor, portable computer and other components. The device can quickly acquire soil surface data in three dimensions through the whole working width of direct seeding machine. The surface roughness data of rapeseed combined direct seeder and no-tillage direct seeder after operation were used to detect the effect of two kinds of seedbed operation on seedbed surface. The characteristics of rapeseed direct seeding process were analyzed, three-dimensional digital modeling and detrend processing of ground data after two kinds of direct seeders were carried out by using MATLAB. The least square method was utilized to eliminate the inclined tendency of seedbed surface, the fourth order polynomial was applied to remove the slope trend in the width direction of the soil surface of rapeseed combined direct seeder. The root mean square (RMS) height and the correlation length were involved as evaluating parameters to analyze the data. Statistical results of roughness parameters for different spatial sampling intervals and different angles cross-section data showed that the average RMS height and RMS height mean error of 170 mm sampling interval were all higher than that of 5 mm sampling interval, the average RMS height of the 170 mm sampling interval was 2.2 mm higher than that of the 5 mm sampling interval, the corresponding correlation length was around 1.8 times the latter; and the RMS height and the correlation length error of the 170 mm sampling interval were higher than that of the 5 mm sampling interval. The maximum difference of RMS height and correlation length of surface elevation data were 7.69 and 25.14 mm respectively in the three directions of 0, 45 and 90 degrees in the forward direction of vertical unit, and there was obvious anisotropy on the surface of seedbed with structural characteristics such as seed trench. Statistical results of local roughness quantification using sliding sampling windows in different size regions showed that when the window width is 0.5 times the compartment width, the standard deviation of RMS height can be stabilized within 0.27 mm if the length of the window is not less than 1.2 m, and the influence of local surface tilt on the roughness calculation can be eliminated by the trend treatment of the tilt of each sampling window. The results can provide reference for the measurement and quantitative analysis of surface roughness in rape direct seeding operation. [ABSTRACT FROM AUTHOR]

Using computer vision technology to monitor cotton growing state is the development trend of cotton production informatization, and is also the widely used automation technology. Cotton field canopy image segmentation under natural scenes is an important part for monitoring growth status of cotton. Since the existing methods are not good in the image segmentation of cotton fields in the environment of light and background, and the adaptive ability is not strong, an image segmentation method based on the fully convolution network and conditional random field was proposed. Firstly, fully convolutional network was used to extract the image features, and the fully convolutional network was trained by back propagation algorithm and stochastic gradient descent(SGD)to obtain the initial segmentation results; This network model is an improvement on the traditional network model convolutional neural network (CNN), which was widely used in image classification. The full connect layer is transformed into a convolution layer, so that the fully convolutional network’s output image as input image size, so it can be applied in the field of image segmentation. For achieve the FCN segmentation model structure, VGG16 was chosen as a basic network structure. And for improve the accuracy of segmentation results, we design the skip structure which can combine pool3 pool4 pool5 feature pictures to combine the FCN-8s. Secondly, the initial segmentation results were input into the conditional random field, used the classification vector of image pixels and the relative relationship between the pixels to construct the energy function and train the conditional random field, obtained the best performance conditional random field; conditional random field is an undirected graph model, this model builds a graph on the relationship between image pixels and image pixels. Each pixel has different label, conditional random field improve the quality of the segmentation results and optimal segmentation results by minimizing the graph energy function. Thirdly, we through the verification process, the structure of the full convolutional network and the conditional random field model is optimized. Finally, used the trained fully convolutional network and conditional random field make inferences about input image to get the final segmentation result.In order to verify the effect of the method proposed in this paper, 625 test cotton canopy images were captured from the cotton producing areas in Xinjiang, China from April to July 2017. These images were collected by the Canon EOS6D digital camera with 1360×800, and zoomed into 850×500 pixels. The 375 images in these images are used as the training set image, and 125 images are used as the validation set images, and the 125 images are used as the test set. This method programming development environment is Python2.7, the operating system is Ubuntu 14.04 LTS, the deep learning development framework is Chainer 1.24 on an NVIDIA Quadro k5000 GPU(graphics processing unit). The experimental result show that proposed method's mean pixel accuracy is 83.24%, mean IOU is 71.02%, Compared with the traditional fully convolutional network, the performance of the proposed method is improved by 16.22 and 12.1 percentages respectively, proposed method has obvious improvement effect. Compared with the Zoom-out and conditional random field as recurrent neural networks(CRFasRNN) segmentation methods, the average pixel accuracy of fully convolutional network is improved by 4.56 and 1.69 percentages, and the average crossing ratio is improved by 7.23 and 0.83 percentages respectively. Compared with the logistic regression method and SVM method, the average pixel accuracy of fully convolutional network is improved by 3.29 and 4.01 percentages, and the average crossing ratio is improved by 2.69 and 3.55 percentages respectively. In order to verify the robustness of the method proposed in this paper, the segmentation of cotton canopy images under complex environment(plastic film), complex canopy area(leaf cover) and complex illumination condition(poor contrast in the canopy area) were carried out by different methods. Because of the GPU parallel computation technology, the proposed segmentation method is very efficient, the average processing time is 0.33s. The segmentation result show that the proposal method is less affected by environment and has better robustness. This study can provide reference for automatic monitoring of cotton growth status. [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.)

Cotton canopy coverage is an important index for monitoring cotton growing in field. It is easy but not accurate to calculate, because it is difficult to accurately segment the cotton canopy in the complex environment image of cotton field. This paper presents an adaptive threshold segmentation approach of cotton canopy image based on logistic algorithm in order to improve the segmentation precision and robustness for cotton canopy image. Firstly, the cotton canopy image is transformed into HSV (hue, saturation, value) color space. This color space is designed by human color description. In this color space, the color feature of the pixel can be expressed by 3 independent components i.e. H, S and V. In this paper, the logistic regression algorithm is used to compute threshold used in image segmentation. The logistic regression algorithm is often used in 2 kinds of classification problem, so our method need an artificially defined variable. This variable and a single color feature variable can form a dataset as the input of logical regression algorithm to calculate the segmentation threshold. In our paper, the proposed artificially defined variable is set to a specific value that is 1, the effect of which is to reduce the impact for computed segmentation threshold. The cotton canopy image’s pixel is divided into 2 classes: target and background. The H channel feature of 2 classes can be extracted in HSV color space, and the green ratio (G/(R+G+B)) of 2 classes can be extracted in RGB (red, green, blue) color space. Those features’ thresholds are computed by logistic regression algorithm. H channel thresholds are used to achieve the first segmentation. Secondly, the first segmentation result is divided to highlight pixels and low pixels. The highlight pixels mainly include light canopy and light soil, and the low pixels mainly include shadow canopy and shadow soil. However, it is difficult to segment cotton canopy in the low pixels. In order to solve this problem, extra-green (Exg) color feature is used as segmentation feature to get cotton canopy in the low pixels. Thirdly, the highlight pixels in the first segmentation result and the low pixels segmented by Exg threshold are segmented by green ratio threshold. This segmentation is called the second segmentation. At last, the segmentation result of cotton canopy is acquired by morphology repair operation, and it ensures the integrity of the canopy region and the independent noise removal. In order to verify the effect of the method proposed in this paper, 320 test images were captured from the cotton producing areas in Xinjiang, China from April to July 2016. The acquisition was often on sunny day, aiming at obtaining images under different lighting conditions, different positions in cotton field, and different cotton growth periods. These images were collected by the Canon EOS5D digital camera with 6 912×3 416 pixels, and zoomed into 1 728×1 152 pixels to improve segmentation effect. This algorithm programming development environment is Python 2.7, and OpenCV 2.4.9. The experimental results show that the average relative object area error (RAE) by our method is only 5.46%, the Exg feature OTSU method 11.78%, the four-component segmentation method 24.11%, and the saliency segmentation method 36.92%. The overall average matching rate by our method is 93.07%, the Exg feature OTSU method 76.43%, the four-component segmentation method 71.67%, and the saliency segmentation method 66.92%. The average processing time of this paper proposed method was 4.63 s, which was much more time-consuming than the super-green characteristic OTSU method (3.84 s) and the four-component segmentation method (2.56 s), but this time less than that of the segmentation method (6.25 s). Therefore the proposed method in our paper has better performance than other methods in cotton canopy segmentation task, and is effective to segment the cotton canopy in the complicated background and different cotton growth periods. The proposed method can provide certain basis for implementation of cotton growth condition automatic monitoring. [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.)