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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]

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Plant leaf diseases are a serious problem in agricultural production. To solve this problem and prevent diseases deterioration, accurate identification of diseases types is of great significance. In this paper, we proposed a recognition model of plant leaf diseases based on convolutional neural network (CNN), which combines the batch normalization and global pooling methods. The parameters of the traditional CNN model are large and have difficulty to converge. The proposed model was modified in the traditional structure of the CNN, which could optimize the training time and achieve the higher accuracy, and also reduce the size of model. In order to speed up the training convergence, we used the batch normalization layers. We put the input of every convolutional layer in batch, calculated the mean and variance of the batch, and then normalized this batch. We reduced some feature maps of some layers and removed the last full connect layer, with the global pooling layer instead. The proposed model has 5 convolutional layers and 4 pooling layers. In the last pooling layer pool5, the same kernel size of convolutional layer Conv5 was used to take advantage of the information of Conv5's feature map comprehensively. For the image preprocessing, we had zoomed, flipped and rotated the original pictures of dataset randomly to get the augmented dataset, and used the 80% of pictures as the train dataset and the rest as the test dataset. These pictures were quantized to 256×256 dpi for CNN training, and the original dataset and augmented dataset were used to train models. To look for the best size of the first layer kernel, in the first convolutional layer, different kernel sizes i.e. 11×11, 9×9 and 7×7 dpi were used respectively. Furthermore, we chose the type of global pooling layer, like max pooling and average pooling. Then we designed 8 models with different Conv1 kernel sizes or global pooling types. To further improve the efficiency of this model, besides using the Gaussian initialization, we used the other common type of convolutional initialization such as Xavier initialization, and also used the PRelu activation function for each convolution layer. So the optimal model could be selected to recognize the 26 kinds of leaf diseases which involved 14 kinds of plants, and then we analyzed the model's convergence rate, memory usage and robustness. After the experiment, we compared the test accuracy between the traditional model and the proposed model based on original dataset and augmented dataset. The proposed model could accelerate the training convergence, and the test accuracy could achieve about 90% while the traditional model was only about 77% after 3 training epochs. Different kernel sizes of Conv1 had little impact on the accuracy but small kernel was proved to be more beneficial to the recognition of plant diseases, which could get more texture features than the big kernel size filter, and average pooling also made better results than max pooling. We got the best performance model which used the 9×9 dpi kernel size and global average pooling layer. To show the proposed model's performance, we tested the accuracy on each class, and the mean accuracy of augmented test dataset was 99.56%, and the weighted average score of recall and precision rate achieved 99.41%. The proposed model had the size of only 2.6 MB. In addition, compared with the traditional methods, the change of the spatial position of the pictures had little effect on the performance of the improved model, and the proposed model could identify different diseases of various plant leaves. The results show that the model has higher recognition accuracy and stronger robustness, and can be used for the identification of plant leaf diseases. [ABSTRACT FROM AUTHOR]