The accurate spraying of herbicides and intelligent mechanical weeding operations are the main ways to reduce the use of chemical pesticides in fields and achieve sustainable agricultural development, and an important prerequisite for achieving these is to identify field crops and weeds accurately and quickly. To this end, a semantic segmentation model based on an improved U-Net is proposed in this paper to address the issue of efficient and accurate identification of vegetable crops and weeds. First, the simplified visual group geometry 16 (VGG16) network is used as the coding network of the improved model, and then, the input images are continuously and naturally down-sampled using the average pooling layer to create feature maps of various sizes, and these feature maps are laterally integrated from the network into the coding network of the improved model. Then, the number of convolutional layers of the decoding network of the model is cut and the efficient channel attention (ECA) is introduced before the feature fusion of the decoding network, so that the feature maps from the jump connection in the encoding network and the up-sampled feature maps in the decoding network pass through the ECA module together before feature fusion. Finally, the study uses the obtained Chinese cabbage and weed images as a dataset to compare the improved model with the original U-Net model and the current commonly used semantic segmentation models PSPNet and DeepLab V3+. The results show that the mean intersection over union and mean pixel accuracy of the improved model increased in comparison to the original U-Net model by 1.41 and 0.72 percentage points, respectively, to 88.96% and 93.05%, and the processing time of a single image increased by 9.36 percentage points to 64.85 ms. In addition, the improved model in this paper has a more accurate segmentation effect on weeds that are close to and overlap with crops compared to the other three comparison models, which is a necessary condition for accurate spraying and accurate weeding. As a result, the improved model in this paper can offer strong technical support for the development of intelligent spraying robots and intelligent weeding robots.

Crop-related object recognition is of great importance in realizing intelligent agricultural machinery. Maize (Zea mays. L.) ear recognition could be a representative of crop-related object recognition, which is a critical technological premise for realizing automatic maize ear picking and maize yield prediction. In order to recognize maize ears in dough stage, this study combined deep learning and image processing, which have advantages of feature extraction and hardware flexibility, respectively. LabelImage was applied to mark and label maize plants, based on the deep learning framework TensorFlow, and this study developed multiscale hierarchical feature extraction together with quadruple-expanded convolutional kernels. To recognize the whole maize plant, 1250 images were acquired for training the recognition model, and its performance in a test set showed that the recognition accuracy was 99.47%. Subsequently, multifeatures of maize ear were determined, and the optimum binary threshold was obtained by fitting Gaussian distribution in the subblock image. Consequently, the maize ear was recognized by morphological process which was conducted by Python and OpenCV. Experiment was conducted in August 2018, and 10800 images were acquired for testing this algorithm. Experimental results showed that the average recognition accuracy was 97.02% and time consumption was 0.39 s for each image, which could meet a forward speed of 4.61 km/h for combine harvesters.