Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks
Northwest A&F University · Henan University of Science and Technology
Abstract
Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four common types of apple leaf diseases. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This paper proposes an accurate identifying approach for apple leaf diseases based on deep convolutional neural networks. It includes generating sufficient pathological images and designing a novel architecture of a deep convolutional neural network based on AlexNet to detect apple leaf diseases. Using a dataset of 13,689 images of…
Citation impact
- FWCI
- 69.89
- Percentile
- 100%
- References
- 25
Authors
4Topics & keywords
- Convolutional neural network
- Computer science
- Artificial intelligence
- Deep learning
- Preprocessor
- Robustness (evolution)
- Pattern recognition (psychology)
- Identification (biology)
- Industry, innovation and infrastructure