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Mechanical weeding can reduce the use of pesticides and is of great significance to ensure high yield of crops. Real-time and accurate identification of crops is a key technical problem needs to be solved in mechanical weeding equipment. Because of the subjectivity of feature extraction process in weed recognition, the accuracy of traditional methods in actual field environment is low. In recent years, the method of weed identification based on convolution neural network has been widely studied. Although the accuracy is obviously improved, there are still problems such as large parameters and poor real-time performance. In order to solve the above problems, a four-channel input image is constructed by collecting near infrared and visible images of sugar beet in the field, and a lightweight convolution neural network based on codec structure is proposed. In this paper, Sugarbeet and weed images collected from a farm in Bonn, Germany, in 2016 were used as data sets, which covered images of different growth stages of sugar beet, and 226 pictures of which were randomly selected as training sets, and the remaining 57 pictures were used as test sets. The experimental data set was composed of three channels of visible light image and one channel of near infrared image, which are merged into a four-channel image by pixel level superposition, and the depth-wise separable convolution was used in the deep model. Firstly, the input feature image was convoluted in 2 dimensions convolution kernel and the number of channels was expanded. Then, the 1×1 convolution kernel was used to make the 3 dimensions convolution which combined channel feature and compressed the channels to enhance the nonlinear mapping ability of the model. In order to avoid the problem of the gradient disappearing, the residual block was used to connect the input and output of the depth-wise separate convolution. Finally, the coding and decoder structure was designed and the shallow features were combined with deep features to refine the segmentation effect. Due to the imbalance of pixel proportions of soil, crops and weeds, the weighted loss function was used to optimize the model. The segmentation accuracy, parameters and operating efficiency of models at different input resolutions and different width factor were introduced to evalute the model. When the width factor was 1, the segmentation accuracy of the model increased with the increase of the input image resolution, the model accuracy of four channel input was higher than that of the model based on original visible image input, which showed that the near-infrared image features can compensate the defects of ordinary RGB images to some extent, and make the model more suitable for the dark environment. Under the same input image resolution, the model with a width factor of 2 or 4 performs better than the model with a width factor of 1. With the increases of width factor, the parameters of the model increase greatly. The amount of calculation is related to the size of the input image, so the frame rate gradually decreases with the increase of the size of input image. The experimental results show that the optimal model in this paper is a four channel input model with a width coefficient of 2, and the average intersection union ration is 87.58%, the average pixel accuracy is 99.19%, the parameters are 525 763 and the frame rate is 42.064 frames/s. The model has high segmentation and recognition accuracy and good real-time performance, and can provide theoretical basis for the development of intelligent mechanization weeding equipment. [ABSTRACT FROM AUTHOR]

Mechanical weeding can reduce the use of pesticides and is of great significance to ensure high yield of crops. Real-time and accurate identification of crops is a key technical problem needs to be solved in mechanical weeding equipment. Because of the subjectivity of feature extraction process in weed recognition, the accuracy of traditional methods in actual field environment is low. In recent years, the method of weed identification based on convolution neural network has been widely studied. Although the accuracy is obviously improved, there are still problems such as large parameters and poor real-time performance. In order to solve the above problems, a four-channel input image is constructed by collecting near infrared and visible images of sugar beet in the field, and a lightweight convolution neural network based on codec structure is proposed. In this paper, Sugarbeet and weed images collected from a farm in Bonn, Germany, in 2016 were used as data sets, which covered images of different growth stages of sugar beet, and 226 pictures of which were randomly selected as training sets, and the remaining 57 pictures were used as test sets. The experimental data set was composed of three channels of visible light image and one channel of near infrared image, which are merged into a four-channel image by pixel level superposition, and the depth-wise separable convolution was used in the deep model. Firstly, the input feature image was convoluted in 2 dimensions convolution kernel and the number of channels was expanded. Then, the 1×1 convolution kernel was used to make the 3 dimensions convolution which combined channel feature and compressed the channels to enhance the nonlinear mapping ability of the model. In order to avoid the problem of the gradient disappearing, the residual block was used to connect the input and output of the depth-wise separate convolution. Finally, the coding and decoder structure was designed and the shallow features were combined with deep features to refine the segmentation effect. Due to the imbalance of pixel proportions of soil, crops and weeds, the weighted loss function was used to optimize the model. The segmentation accuracy, parameters and operating efficiency of models at different input resolutions and different width factor were introduced to evalute the model. When the width factor was 1, the segmentation accuracy of the model increased with the increase of the input image resolution, the model accuracy of four channel input was higher than that of the model based on original visible image input, which showed that the near-infrared image features can compensate the defects of ordinary RGB images to some extent, and make the model more suitable for the dark environment. Under the same input image resolution, the model with a width factor of 2 or 4 performs better than the model with a width factor of 1. With the increases of width factor, the parameters of the model increase greatly. The amount of calculation is related to the size of the input image, so the frame rate gradually decreases with the increase of the size of input image. The experimental results show that the optimal model in this paper is a four channel input model with a width coefficient of 2, and the average intersection union ration is 87.58%, the average pixel accuracy is 99.19%, the parameters are 525 763 and the frame rate is 42.064 frames/s. The model has high segmentation and recognition accuracy and good real-time performance, and can provide theoretical basis for the development of intelligent mechanization weeding equipment. [ABSTRACT FROM AUTHOR]

With the advancement of remote sensing technology, the high spatial resolution remote sensing image contains rich special information with a great detail. At the same time, the complexity of high spatial resolution remote sensing images also requires higher the classification technology of remote sensing images. However, in the face of high spatial resolution remote sensing image more obvious geometrical structure and the more rich texture characteristics, how to design rational system of characteristics, select the appropriate sorting algorithms to accurately and quickly grasp the number of rural land of building and its distribution status, are of great significance to balance urban and rural areas, save land, and realize sustainable development. This will help in exploring the application of deep learning model in high spatial resolution remote sensing image building extraction, and have research significance for improving the classification accuracy of high resolution remote sensing image. In this paper, the semantic segmentation model (SegNet) was used for extracting buildings. SegNet is mainly composed of encoder network, decoder network and pixel-wise classification layer. The encoder network transforms high-dimensional vectors into low-dimensional vectors, enabling low-dimensional extraction of high-dimensional features. The decoder network maps low-resolution feature maps to high spatial resolution feature maps, realizing the reconstruction of low-dimensional vectors to high-dimensional vectors. The softmax classifier separately classifies each pixel, which outputs the probability that each pixel belongs to each class. In this paper, a 3000 pixel × 3000 pixel and two 2000 pixel × 2000 pixel slices were taken from the global remote sensing image of Bazhou City, Hebei Province as training samples, and a 3000 pixel × 3000 pixel slice was taken as the verification sample. In this paper, five comparative experiments were used to extract the buildings, including PSPNet, support vector machine, random forest, ISO clustering and maximum likelihood method. The confusion matrix of each classification method was obtained by calculating the difference between the classification results of the comparison experiment and the real value. From the traditional classification algorithm to the shallow learning algorithm to the deep learning algorithm, the Kappa coefficient and overall accuracy of classification kept constantly increasing, among which SegNet semantic segmentation algorithm based on the deep convolutional network performed better than the other five algorithms in extracting buildings from high spatial resolution remote sensing image. The Kappa coefficient and the overall accuracy of SegNet semantic segmentation algorithm were 0.90 and 96.61%, respectively, and the ground truth value was basically the same as the classification result. The F1Score of building extraction of SegNet semantic segmentation algorithm based on deep convolution network was 0.91, but the other five algorithms were below 0.87. SegNet had the lowest error rate of 9.71% for buildings, indicating that the ability to identify buildings of semantic segmentation algorithm from high spatial resolution remote sensing was superior to traditional classification algorithms, shallow layer learning algorithms based on machine learning, and PSPNet semantic segmentation algorithm based on deep convolution network. The Kappa coefficient and overall accuracy of the remaining five classification algorithms were respectively below 0.83 and 94.68%, and the difference between the ground truth value and the classification result was relatively large. SegNet can not only make use of spectral information but also make full use of abundant spatial information. During SegNet training, more essential features can be learned, and more ideal features suitable for pattern classification were finally formed, which can enhance the ability of convergence and generalization of the model and improve the classification accuracy. Traditional classification algorithms, such as ISO clustering and maximum likelihood method, failed to make use of the rich spatial information of the high-resolution remote sensing image, so the accuracy was relatively low. Due to limited computing units and large amount of high spatial resolution remote sensing image data, shallow layer learning algorithms based on machine learning such as support vector machines and random forest cannot effectively express complex features of ground objects, so their advantages are not obvious in building extraction from the high spatial resolution remote sensing images. The experimental results showed that the SegNet based on deep learning has the best performance, and it has important theoretical significance to explore the application of deep learning model to remote sensing image classification methods. At the same time, the research results also provide a reference for improving the classification accuracy of high resolution remote sensing images. [ABSTRACT FROM AUTHOR]

In this paper, a soil pore segmentation method based on fully convolutional network (FCN) is proposed to improve the accuracy of pore segmentation in soil image and provide technical support for the research of soil science. Taking the soil of typical black soil as the research object, the soil computed tomography image were obtained by scanning and cutting. Based on the FCN network, the soil image and the calibrated image of pore structure were input for convoluting, pooling and deconvoluting operations, and the error between the prediction image and the calibration image was used as feedback to complete the forward inference operation. Then, the weight value was updated by the back propagation algorithm to establish the soil pore segmentation model. Fully considering the pore geometry and spatial distribution characteristics, the pore model can accurately output the soil pore binary image. Meanwhile, the commonly used segmentation methods in the literature, such as Otsu method, watershed method, regional growth method and Fuzzy C-means method (FCM) were adopted for the comparative experiments on soil computed tomography images with low pore density (0-0.03), medium pore density (0.03-0.1) and high pore density (0.1-1) which were defined by porosity of soil. The experimental results showed that the watershed method and the regional growth method overestimate the pore structure of different geometries, including cracks between the pores, whereas the Otsu method and FCM method tended to overestimate the macropores and underestimate the micropores. Compared the five methods, the FCN method can accurately extract the pore structures with vary topologies from the complex soil computed tomography images with low, medium and high pore density. Moreover, the segmentation accuracy rate, over-segmentation rate, and under-segmentation rate were used to evaluate the soil pore segmentation performance of five methods. Based on 1487 soil computed tomography images, the average segmentation accuracy of FCN pore segmentation method was 98.1%, which was 25.6%, 48.3%, 55.7% and 9.5% higher than that of Otsu method, watershed method, regional growth method and FCM method. The average over-segmentation rate of the FCN pore segmentation method was 2.2%, which was only 33.8% of the suboptimal method (FCM method), respectively. And the average under-segmentation rate of the FCN pore segmentation method was 1.3%, which was only 23.6% of the suboptimal method (FCM method). In total, the FCN method can accurately extract the pore topology, restore the spatial distribution of pores and its application can make up for the shortcoming that the traditional segmentation method only uses the low-level features (gray and edge) when extracting the pore structure. Owing to the multiple convolution layers in the network, the FCN method can obtain the vary features of pore structure, so it has strong generalization ability and robustness of pore segmentation for different types of soil images. This paper will has a good reference for the microscopic process simulation , 3D reconstruction and soil structure analysis on the pore scale, and can provide a more intelligent technical method for soil science. [ABSTRACT FROM AUTHOR]

Effective recognition method of crop and weed is the basis for promoting the development of intelligent mechanization weeding pattern. Summarizing the previous research, we found that the accuracy and stability of the recognition model could be improved by natural and diversified feature presentation, but there are still 2 main problems. On the one hand, feature presentation of the natural property of target was difficult to be obtained by the hand-engineered feature extractor. The spatial consistency of the obtained features was bad, and the real-time performance of recognition system was reduced for the complex feature extraction algorithm. On the other hand, the effect of image preprocessing has important influence on recognition results, especially the overlapping segmentation of crop and weed. For overlapped objects, it is usually difficult to segment them without affecting their respective feature presentations, resulting in low recognition accuracy and stability. In order to solve the main problems in the current research, we explored the way to improve the recognition accuracy, stability and real-time performance, and a recognition method of crop and weed based on multiscale hierarchical feature learning combined with superpixels segmentation was proposed. The main research contents of this paper were as follows: 1) Excellent internal features of image are hierarchical. In this research, the multiscale hierarchical feature is a scene level feature with invariance and consistency in scale space. Multiscale convolutional neural network was built to extract multiscale hierarchical feature. Multiscale convolutional neural network contains multiple copies of a single CNN (convolutional neural network) that are applied to multi-scale Gaussian pyramid of the input image. The CNN model as feature extractor in this paper includes 3 stages. In the first 2 stages, it contains a bank of filters (convolution kernels) to produce dense feature maps, a point to point nonlinear mapping activation function, and a spatial pooling module for sub-sampling of each feature map, While the last stage only contains a bank of filters. Each filter is applied to the input feature maps through 2-dimensional convolution operation, in which local feature presentations are detected at all pixel locations on the input image. For each pixel, the CNN model is used to collectively encode the internal information in a large sense region around the location of given pixel. The CNN is fed with raw pixels and trained with back propagation method. With complete training, this CNN model can automatically extract hierarchical feature representations from the input image, thereby decreasing the need for hand-engineered features extracting. A series of feature maps for multiscale regions centered on each pixel of the image are produced by the multiscale convolutional neural network; these representations contain shape, texture and sense information. Therefore, the multiscale hierarchical feature is learned to allow the recognition of the class of all pixels in the image. The average pixel recognition rate is 93.41%. 2) In this research, multiscale hierarchical feature is used to produce the class distribution for every pixel through a 2-layer MLP (multi-layer perceptron). But recognizing the class of pixel independently from its surrounding regions could produce some interference at the boundary of targets. Accurate boundary segmentation for each target in the image is not provided. In this paper, the superpixel method is used for generating an over segmentation of the image, where each segmentation component (superpixel) is an irregular pixel block composed of similar pixels in texture, color and brightness characteristics. Object in the image is composed of these superpixels, and has the exact original boundary. The superpixels segmentation is computed following the simple linear iterative clustering method described in this paper. 3) We proposed the recognition strategy of multiscale hierarchical feature learning combined with the superpixels segmentation. Firstly, an over segmentation of the original image was produced through the superpixels segmentation. At the same time, each pixel location of the image was classified densely based on the multiscale hierarchical features. These predictions of pixels in each superpixel were aggregated to produce the class prediction of superpixel, through computing the average class distribution within the superpixel. Adjacent superpixels with the same class were merged to obtain the final target class prediction and image segmentation. The accurate image segmentation was achieved while recognizing the target in the image by this recognition method, which effectively avoided the problems caused by targets overlapping, and the recognition results were more stable and accurate. Maize seedlings at 2-5 leaves stage and weed during the same stage were used as research object, and the recognition method of multiscale hierarchical feature learning combined with superpixels segmentation was tested. The results showed that the average target recognition rate with this method was 98.92%, and the standard deviation was 0.55%. The average target recognition rate with the method in previous research was 98.36%, and the standard deviation was 1.05%. So, the accuracy and stability of recognition results by this method in this paper were improved with different degrees. In aspect of real-time performance, the average time to recognize a single image was 1.68 s with this method, and compared with the method proposed in previous research, it was decreased by 1.58 s. The real-time performance of this method can be further improved by GPU (graphics processing unit) hardware acceleration, and the average time to recognize a single image was only 0.72 s. Therefore, the recognition method based on multiscale hierarchical feature learning combined with superpixels segmentation can effectively achieve accurate, stable and efficient recognition of maize and weed. The research results provide reference for the development of precision weeding. [ABSTRACT FROM AUTHOR]