Abstract
The prevention and control of navel orange pests and diseases is an important measure to ensure the yield of navel oranges. Aiming at the problems of slow speed, strong subjectivity, high requirements for professional knowledge required, and high identification costs in the identification methods of navel orange pests and diseases, this paper proposes a method based on DenseNet and attention. The power mechanism fusion (DCPSNET) identification method of navel orange diseases and pests improves the traditional deep dense network DenseNet model to realize accurate and efficient identification of navel orange diseases and pests. Due to the difficulty in collecting data of navel orange pests and diseases, this article uses image enhancement technology to expand. The experimental results show that, in the case of small samples, compared with the traditional model, the DCPSNET model can accurately identify different types of navel orange diseases and pests images and the accuracy of identifying six types of navel orange diseases and pests on the test set is as high as 96.90%. The method proposed in this paper has high recognition accuracy, realizes the intelligent recognition of navel orange diseases and pests, and also provides a way for high-precision recognition of small sample data sets.
Highlights
Data Acquisition and PreprocessingE images of diseases and pests of navel oranges are extracted and marked by consulting related literature and data combined with main expert knowledge, and preprocessing techniques such as filtering are performed on the images
A simple and effective image recognition network named Dense Channel And Position Self-Attention Fuse Network (DCPSNET) for navel orange diseases and pests is proposed. e design principle of the network focuses on improving the utilization rate of model parameters, and the self-attention mechanism module is added on the basis of the original DenseNet network, so that the network can better notice the diseases and pests in the training process
E data set includes 1157 images of navel orange leaves: 74 images of sun fruit disease, 225 images of canker leaf disease, 238 images of canker fruit, 88 images of gray mold, 283 images of leaf miner disease, and 69 images of anthracnose, as well as 180 healthy leaf images. e data distribution of the navel orange image data set is inconsistent, and the number of images of sun fruit disease, anthracnose, and gray mold is relatively less compared to other categories. erefore, in order to enrich the image and prevent overfitting, this article uses random angle rotation within the range, image translation within ±10%, flip scale transformation within the range, color jitter within the range, Gaussian noise added to the image, and so forth
Summary
E images of diseases and pests of navel oranges are extracted and marked by consulting related literature and data combined with main expert knowledge, and preprocessing techniques such as filtering are performed on the images. In order to diversify the image, a data enhancement scheme is used to enhance the deep convolutional generation adversarial network (DCGAN) [23], with traditional methods such as random vertical or horizontal flipping, random angle rotation, scale transformation, and color dithering to generate new synthesized images to expand the data set and reduce overfitting during network training. E data distribution of the navel orange image data set is inconsistent, and the number of images of sun fruit disease, anthracnose, and gray mold is relatively less compared to other categories. Rough data enhancement, the number of original image samples has been increased by 17 times, and each category has no less than 1000 samples. In addition to retaining some images to evaluate the effectiveness of the model, according to the method
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
