Abstract
In order to solve the problem of accuracy and speed of disease identification in real-time spraying operation in maize field, an improved ResNet50 maize disease identification model was proposed. Firstly, this paper uses the Adam algorithm to optimize the model, adjusts the learning strategy through the inclined triangle learning rate, increases L2 regularization to reduce over fitting, and adopts exit strategy and ReLU incentive function. Then, the first convolution kernel of the ResNet50 model is modified into three 3 x 3 small convolution kernels. Finally, the ratio of training set to verification set is 3 : 1. Through experimental comparison, the recognition accuracy of the maize disease recognition model proposed in this paper is higher than that of other models. The image recognition accuracy in the data set is 98.52%, the image recognition accuracy in the farmland is 97.826%, and the average recognition speed is 204 ms, which meets the accuracy and speed requirements of maize field spraying operation and provides technical support for the research of maize field spraying equipment.
Highlights
Maize is an important food crop, feed crop, and industrial raw material crop in China
In this paper, based on the ResNet50 model, the exponential decay method is used to adjust the learning rate, and L2 regular term is added to the cross-entropy function to punish the weight
In order to avoid over fitting in the training process, dropout strategy and ReLU incentive function are used between the network layers. e first layer of the ResNet50 model was changed into three 3 × 3 convolution layers to improve the recognition accuracy of small disease spots of maize diseases
Summary
Maize is an important food crop, feed crop, and industrial raw material crop in China. E above research of disease recognition methods based on traditional image processing technology has achieved some results, but the operation of these methods is too cumbersome, the robustness is poor, and the feature extraction method is not universal, which makes the generalization ability of the whole method poor. Mobile Information Systems and BP neural network to establish the maize leaf disease recognition model, with an accuracy of 93.4% [8]. Fan et al proposed an improved CNN model with the recognition accuracy of 97.10% [10]. Fan Xiangpeng et al proposed an improved faster R-CNN model, with an average accuracy of 97.23% and a single image taking 0.296 s [11]. The deep learning method has achieved good results in the research of maize disease identification, especially based on the ResNet model. In order to implement the spraying operation in the field in real time, in addition to the recognition accuracy, the recognition time of a single picture should be guaranteed. erefore, this study intends to improve the ResNet model, to improve the recognition accuracy and to improve the recognition speed
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