Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks

Jiang, Peng; Chen, Yuehan; Liu, Bin; He, Dongjian; Liang, Chunquan

doi:10.1109/access.2019.2914929

articleIEEE AccessJan 1, 2019GOLD OA

Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks

PJPeng Jiang YCYuehan Chen BLBin Liu DHDongjian He CLChunquan Liang

Northwest A&F University · Ministry of Agriculture and Rural Affairs

Indexed incrossrefdoaj

Abstract

Alternaria leaf spot, Brown spot, Mosaic, Grey spot, and Rust are five common types of apple leaf diseases that severely affect apple yield. However, the existing research lacks an accurate and fast detector of apple diseases for ensuring the healthy development of the apple industry. This paper proposes a deep learning approach that is based on improved convolutional neural networks (CNNs) for the real-time detection of apple leaf diseases. In this paper, the apple leaf disease dataset (ALDD), which is composed of laboratory images and complex images under real field conditions, is first constructed via data augmentation and image annotation technologies. Based on this, a new apple leaf disease detection…

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854

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FWCI: 96.78
Percentile: 100%
References: 52

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Authors

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Topics & keywords

Topics

Keywords

Convolutional neural network
Computer science
Deep learning
Concatenation (mathematics)
Artificial intelligence
Leaf spot
Rust (programming language)
Pattern recognition (psychology)

UN Sustainable Development Goals

Industry, innovation and infrastructure

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