文献类型： 外文期刊

作者： Zhu, DingJu ¹ ; Xie, LianZi ¹ ; Chen, BingXu ⁴ ; Tan, JianBin ¹ ; Deng, RenFeng ² ; Zheng, Yongzhi ¹ ; Hu, Qi ¹ ; Mustafa, Rashed ⁵ ; Chen, Wanshan ⁶ ; Yi, Shuai ¹ ; Yung, KaiLeung ⁷ ; Andrew, W. H. Ip ⁸ ;

作者机构： 1.South China Normal Univ, Sch Comp Sci, Guangzhou 510631, Guangdong, Peoples R China

2.South China Normal Univ, Sch Software, Guangzhou 510631, Guangdong, Peoples R China

3.Guangdong Prov Ind Coll Artificial Intelligence &, Guangzhou 510631, Guangdong, Peoples R China

4.Guangdong Acad Agr Sci, Inst Plant Protect, Guangzhou 510640, Guangdong, Peoples R China

5.Univ Chittagong, Comp Sci & Engn, Chittagong 4331, Bangladesh

6.Natl S&T Innovat Ctr Modern Agr Ind Guangzhou, Guangzhou 510520, Guangdong, Peoples R China

7.Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong 999077, Peoples R China

8.Univ Saskatchewan, Dept Mech Engn, Saskatoon, SK M4Y 1M7, Canada

关键词： Pests detection and identification; Knowledge graph; Raspberry PI; Image classification

期刊名称：INTERNET OF THINGS （ 影响因子：5.9； 五年影响因子：6.4 ）

ISSN： 2543-1536

年卷期： 2023 年 21 卷

页码：

收录情况： SCI

摘要： Fruit usually plays a vital role in people's daily life. Many kinds of fruits are rich in vitamins and trace elements, which have high edible value. Pests and diseases are a considerable problem in the process of fruit planting. The quality and quantity of fruit can be effectively improved by the detection and preventing pests and diseases. However, suppose in the process of fruit growth, it is always necessary to manually identify and detect pests and diseases. In that case, it will inevitably consume a lot of workforce and material resources. Therefore, it is advisable to have an auto-mated system to save unnecessary time and effort. This article introduces the detection and identification system of pests and diseases based on Raspberry Pi to identify and detect the pests and diseases of fruit such as Longan and lychee. Firstly, we constructed a knowledge graph of pests and diseases related to lychee and longan. Then, we used the Raspberry Pi to control the camera to capture the pests and diseases images. Next, the system processed and recognized the images captured by the camera. Finally, the Bluetooth speaker broadcasted the results in real-time. We constructed the knowledge graph through data collection, information extraction, knowledge fusion and storage. We trained the vgg-16 model, which achieves 94.9% accuracy in the pests identification task, and we deployed it on a Raspberry Pi.