Abstract
Citrus is one of the most widely cultivated fruit in the world. However, citrus diseases are becoming more and more serious, which has caused substantial economic losses to citrus growers. With the rapid developments of mobile device, mobile services computing play an increasingly important role in our daily lives. How to develop an intelligent diagnosis system for citrus diseases based on mobile services computing and bridge the gap between citrus growers and plant diagnostic experts is worth studying. In this paper, we build an image dataset of six kinds of citrus diseases with the help of experts and realize an intelligent diagnosis system for citrus diseases by constructing the simplified densely connected convolutional networks (DenseNet). The system is realized using the WeChat applet in the mobile device, with which users can upload images and receive diagnostic results and comments. The experimental results show that the recognition accuracy of citrus diseases exceeds 88% and the predict time consumption has also been reduced by simplifying the structure of the DenseNet.
Highlights
The development of mobile devices and wireless technology further pushed the advance of other fields [1]
The idea of this paper is to combine mobile computing with deep learning, so we proposed an intelligent mobile diagnosis system for citrus diseases based on DenseNet and mobile service computing to break the barrier between citrus growers and experts
In order to avoid repeated development work, deep learning has a large number of frameworks
Summary
The development of mobile devices and wireless technology further pushed the advance of other fields [1]. Mobile service computing enables us to provide and access services anytime and anywhere through mobile devices, such as mobile phones. This paper aims to use mobile services computing to provide services to citrus growers through mobile phones. Due to the high performance of convolutional neural networks (CNNs), it becomes a general trend to apply them to practical application scenarios. It is hampered by their large number of computational costs and a lack of datasets, that makes it becomes a hot topic for researchers
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