# Research Progress and Prospects of Agricultural Aero-Bionic Technology in China
## 4 Biological Intelligence and Agricultural Aviation
### Convolutional Neural Network  
An artificial neural network is a kind of simulated bionics of biological neural networks. It is a dynamic network system composed of a large number of nonlinear arrangements of neurons. A convolutional neural network is an artificial neural network that includes convolutional layers, and is usually used to identify two-dimensional images [73], has stronger learning and expression capabilities than traditional machine learning [74], and can simulate the human visual system to classify feature data by extracting target images to achieve target recognition [75]. At present, convolutional neural networks have achieved certain results in the fields of artificial handwriting recognition [76], face detection [77], crop recognition [78], terrain perception [79], and other fields, and they have attracted the attention of many agricultural researchers [80]. [PERSON] [81] proposed to use the Series Otsu method, Faster Regional Convolutional Neural Network, and YOLO-v3 methods to identify the flowering state of rice, and also use the open and inner glumes. Experimental results show that the combination of the Series Otsu method and the Faster Regional Convolutional Neural Network to identify the flowering state of rice has a high degree of consistency with manual observation, and it can be used instead of the artificial use of a drone for rice flowering detection. In order to improve the accuracy of UAV  
Figure 8: Schematic diagram of the mammalian brain navigation system.  
image recognition, [PERSON] [82] proposed the CNN-PNN (convolutional neural network-probabilistic neural network) model, which is a hybrid model combining the convolutional neural network and probabilistic neural network. The convolutional neural network is used to extract sample images. The feature vector is trained by a probabilistic neural network model and the output result. The simulation result shows that the model's recognition rate is as high as 96.3%. [PERSON] [83] proposed a method of a fully convolutional neural network to identify pest areas, using a multi-rotor UAV to acquire pest images from the air, marking the pixels of the pest areas, learning and training, and constructing a model to identify the pest areas. Experiments show that the method has high recognition accuracy, and the pixel accuracy rate is as high as 97.86%. In order to improve the accuracy of pest identification, [PERSON] [84] proposed a deep learning model of the convolutional neural network, which used image saliency analysis and the Grubcut algorithm to locate pests in complex backgrounds, and expressed and classified tea garden pests through the Alexnet convolution neural network architecture, so as to avoid human subjective factors affecting the classification results. [PERSON] [85] built a greenhouse cucumber disease recognition model based on the convolutional neural network through the combination of deep learning and image processing technology. According to the characteristics of mixed colors, the model was compared with traditional colors, and the model was trained through image data collection and calculation. The results show that the accuracy of the model is up to 95.7%.