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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]

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]

Accurate and timely winter wheat yield estimation has significant effect to grain markets and policy. Most crop estimation methods can be divided into two categories, one is based on the crop model and the other is the statistical learning method. For statistical learning methods with recent advances in deep learning, convolutional neural network (CNN) have become state-of-the-art algorithms. can extract the depth-dependent features of crop growth. However, the pivotal challenge is to combine remote sensing images with CNN. In this paper, we employ the method of histogram dimensionality reduction and time series fusion to generate the input layer. The experiment firstly performed projection transformation, splicing, mask, fusion, and clipping for 6 different MODIS images in the research area from 2006 to 2016, and then generated 21 600 fusion images of 12 bands (surface reflectance data of 7 different wavelengths in MOD09A1, surface temperature of day and night in MYD11A2, NDVI and EVI in MOD13A1, and FPAR in MOD15A2H). Then, the sensitivity range of winter wheat growth in each band is divided into 36 sections, and the histogram statistics are used to reduce the dimension to generate a vector of length 36, so the remote sensing image generates a matrix of 36×36×12 in the 228-day growing season. The corresponding time and regional statistics are applied as the output layer to construct a complete sample. The yield estimation sample database of 12 indices in the winter wheat region of north China (60 prefecture-level cities) from 2006 to 2016 was constructed, and the training set and verification set were divided into 10:1 for the training and evaluation of yield estimation model. Finally, the neural network structure is designed according to the sample, which consists of the input layer, 7 convolution layers (c1-c7), 7 activation layers, 7 batch normalization layers, 3 dropout layers, 2 full connection layers, and output layer. The number of c1-c7 convolution kernels is 64, 64, 128, 128, 256, 256, 256, the convolution kernel size is 3×3 dpi, and the sliding step length is 2, 1, 2, 2, 2, 1 and 2 respectively, 1 zero paddings per convolutional layer. At the same time, batch normalization and Relu function activation are performed on each convolutional layer, and the Dropout layer is used in the fully connected layer. The results show that: 1) The root-mean-square error (RMSE) and coefficient of determination (R²) of the convolutional neural network model on the training set are 183.82 kg/hm² and 0.98 respectively. In the validation set, RMSE and R² are 689.72 kg/hm² and 0.71. 2) With the same neural network structure, the average RMSE of the estimated samples from 2006 to 2016 trained as validation sets for 11 models was 772.03 kg/hm². The error of the yield estimation model was the largest in 2007 and the smallest in 2012, and the RMSE was 920.45 kg/hm² and 632.08 kg/hm² respectively. Crop estimation algorithm based on CNN has high robustness and precision; 3) The accuracy analysis of prediction yield at the municipal level of different provinces in three temporal points of 2007, 2012 and 2016 indicates that the model has higher accuracy in most areas of the northern winter wheat region, especially, RMSE of Hebei and Shandong provinces is approximately 500 kg/hm². The result shows that CNN is well applied to the estimation of winter wheat production. This is a great thought of remote sensing combined with the deep learning algorithm. This method can be used to estimate yield by remote sensing in different scales and regions. Compared with the traditional method, this “start-to-end” learning method has the advantage of synergy and can obtain the optimal estimation model relative to the whole area. Meanwhile, As data accumulates, the estimation accuracy will be continuously improved, and it has a good application prospect in the national agricultural production forecast. [ABSTRACT FROM AUTHOR]

Accurate and timely winter wheat yield estimation has significant effect to grain markets and policy. Most crop estimation methods can be divided into two categories, one is based on the crop model and the other is the statistical learning method. For statistical learning methods with recent advances in deep learning, convolutional neural network (CNN) have become state-of-the-art algorithms. CNN can extract the depth-dependent features of crop growth. However, the pivotal challenge is to combine remote sensing images with CNN. In this paper, we employ the method of histogram dimensionality reduction and time series fusion to generate the input layer. The experiment firstly performed projection transformation, splicing, mask, fusion, and clipping for 6 different MODIS images in the research area from 2006 to 2016, and then generated 21 600 fusion images of 12 bands (surface reflectance data of 7 different wavelengths in MOD09A1, surface temperature of day and night in MYD11A2, NDVI and EVI in MOD13A1, and FPAR in MOD15A2H). Then, the sensitivity range of winter wheat growth in each band is divided into 36 sections, and the histogram statistics are used to reduce the dimension to generate a vector of length 36, so the remote sensing image generates a Matrix of 36×36×12 in the 228-day growing season. The corresponding time and regional statistics are applied as the output layer to construct a complete sample. The yield estimation sample database of 12 indices in the winter wheat region of north China (60 prefecture-level cities) from 2006 to 2016 was constructed, and the training set and verification set were divided into 10:1 for the training and evaluation of yield estimation model. Finally, the neural network structure is designed according to the sample, which consists of the input layer, 7 convolution layers (c1-c7), 7 activation layers, 7 batch normalization layers, 3 dropout layers, 2 full connection layers, and output layer. The number of c1-c7 convolution kernels is 64, 64, 128, 128, 256, 256, 256, the convolution kernel size is 3×3 dpi, and the sliding step length is 2, 1, 2, 2, 2, 1 and 2 respectively, 1 zero paddings per convolutional layer. At the same time, batch normalization and Relu function activation are performed on each convolutional layer, and the Dropout layer is used in the fully connected layer. The results show that: 1) The root-mean-square error (RMSE) and coefficient of determination (R2) of the convolutional neural network model on the training set are 183.82 kg/hm2 and 0.98 respectively. In the validation set, RMSE and R2 are 689.72 kg/hm2 and 0.71. 2) With the same neural network structure, the average RMSE of the estimated samples from 2006 to 2016 trained as validation sets for 11 models was 772.03 kg/hm2. The error of the yield estimation model was the largest in 2007 and the smallest in 2012, and the RMSE was 920.45 kg/hm2 and 632.08 kg/hm2 respectively. Crop estimation algorithm based on CNN has high robustness and precision; 3) The accuracy analysis of prediction yield at the municipal level of different provinces in three temporal points of 2007, 2012 and 2016 indicates that the model has higher accuracy in most areas of the northern winter wheat region, especially, RMSE of Hebei and Shandong provinces is approximately 500 kg/hm2. The result shows that CNN is well applied to the estimation of winter wheat production. This is a great thought of remote sensing combined with the deep learning algorithm. This method can be used to estimate yield by remote sensing in different scales and regions. Compared with the traditional method, this “start-to-end” learning method has the advantage of synergy and can obtain the optimal estimation model relative to the whole area. Meanwhile, As data accumulates, the estimation accuracy will be continuously improved, and it has a good application prospect in the national agricultural production forecast. [ABSTRACT FROM AUTHOR]