Abstract
Apple leaf diseases seriously affect the sustainable production of apple fruit. Early infection monitoring of apple leaves and timely disease control measures are the key to ensuring the regular growth of apple fruits and achieving a high-efficiency economy. Consequently, disease detection schemes based on computer vision can compensate for the shortcomings of traditional disease detection methods that are inaccurate and time-consuming. Nowadays, to solve the limitations ranging from complex background environments to dense and small characteristics of apple leaf diseases, an improved Faster region-based convolutional neural network (Faster R-CNN) method was proposed. The advanced Res2Net and feature pyramid network architecture were introduced as the feature extraction network for extracting reliable and multi-dimensional features. Furthermore, RoIAlign was also employed to replace RoIPool so that accurate candidate regions will be produced to address the object location. Moreover, soft non-maximum suppression was applied for precise detection performance of apple leaf disease when making inferences to the images. The improved Faster R-CNN structure behaves effectively in the annotated apple leaf disease dataset with an accuracy of 63.1% average precision, which is higher than other object detection methods. The experiments proved that our improved Faster R-CNN method provides a highly precise apple leaf disease recognition method that could be used in real agricultural practice.
Funder
Youth Science and Technology Innovation Project of Shanxi Agricultural University Grant
Shanxi Provincial Key Research and Development Project
Subject
Plant Science,Agronomy and Crop Science,Food Science
Reference36 articles.
1. (2023, January 01). National Bureau of Statistics of China, Agricultural Data, Available online: https://data.stats.gov.cn/easyquery.htm?cn=C01.
2. Research on deep learning in apple leaf disease recognition;Zhong;Comput. Electron. Agric.,2020
3. Bansal, P., Kumar, R., and Kumar, S. (2021). Disease detection in apple leaves using deep convolutional neural network. Agriculture, 11.
4. Hlaing, C.S., and Maung Zaw, S.M. (2018, January 6–8). Tomato plant diseases classification using statistical texture feature and color feature. Proceedings of the 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS), Singapore.
5. Rice plant disease classification using color features: A machine learning paradigm;Shrivastava;J. Plant Pathol.,2021
Cited by
18 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献