Scientia Agricultura Sinica ›› 2020, Vol. 53 ›› Issue (16): 3257-3268.doi: 10.3864/j.issn.0578-1752.2020.16.005

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Research and Development of the Intelligent Identification System of Agricultural Pests for Mobile Terminals

SHAO ZeZhong1(),YAO Qing1(),TANG Jian2(),LI HanQiong3,YANG BaoJun2,LÜ Jun1,CHEN Yi4   

  1. 1School of Information and Technology, Zhejiang Sci-Tech University, Hangzhou 310018
    2State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006
    3Jia Shan County Agricultural and Rural Bureau, Jiaxing 314100, Zhejiang
    4Tongxiang Agricultural Technology Extension Service Center, Tongxiang 314500, Zhejiang
  • Received:2019-10-16 Accepted:2019-12-02 Online:2020-08-16 Published:2020-08-27
  • Contact: Qing YAO,Jian TANG E-mail:812193586@qq.com;q-yao@126.com;tangjian@caas.cn

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Abstract:

【Objective】There are various species of pests in crop fields, and interspecific similarity and intraspecific difference are common in agricultural pests, which are easy to be confused. In this study, an intelligent system based on mobile terminals was developed to identify agricultural field pests. This system is an easy and intelligent tool of pest identification for peasants and pest forecasting technicians. 【Method】The intelligent identification system of agricultural field pests consists of a mobile client with a system APP, a server and a pest identification model based on deep learning. The application (APP) can be installed in mobile devices with Android system and includes user registration, pest information inquiry, pest automatic identification, pest location information and remote expert identification. The UI interface in this APP uses the style of bottom navigation bar, the information exchange between mobile client and server adopts HTTP protocol and the SDK of Baidu Android map is used to mark the geographic information of pests. The information of users and pests is saved in MySQL database. In the same training and testing sets, different convolutional neural network models were developed to identify agricultural pests. The results showed that the DenseNet121 model achieved the highest precision and lowest false alarm rate. The pest identification model based on DenseNet121 was installed in Alibaba Cloud remote server. When the server received the images from the mobile clients, the identification model was performed. The identification results were feed back to clients from the server. All images and results were saved in database for being traced back in future.【Result】When the users met unidentified pests in crop fields, the users could collect pest images and upload them to the server by the APP installed in mobile clients, such as mobile phone or PAD. The identification results and pest control information would be fed back to the mobile clients in 1-2 seconds. If the results were unsatisfied, the user could ask the expert to remotely identify pests. This system could identify 66 species of pests, and the average precision was 93.9% and false alarm rate was 8.2%. 【Conclusion】The intelligent identification system of agricultural pests could automatically identify the 66 species of agricultural pests. The system could inquire pest information, show the pest geographic information, and ask expert to remotely identify pests. This system is a tool for peasants and pest forecasting technicians to easily and accurately identify agricultural pests in crop fields. It can provide users the one-to-one pest control information and experts needn't go to crop fields for guiding peasants to manually identify pests. This system can save money and time.

Key words: agricultural pests, mobile clients, cloud server, convolutional neural networks, image intelligent identification

Fig. 1

Frame map of the intelligent identification system of agricultural pests for mobile terminals"

Fig. 2

Images of 66 agricultural pests"

Table 1

66 species of common agricultural pests and image number"

序号
ID		 害虫种类
Pest name		 样本总量
Sample size		 序号
ID		 害虫种类
Pest name		 样本总量
Sample size
1 瘤缘蝽 Acanthocoris scaber 49 34 褐边绿刺蛾 Latoia consocia 122
2 鬼脸天蛾 Acherontia lachesis 200 35 大稻缘蝽 Leptocorisa acuta 36
3 苎麻珍蝶 Acraea issoria 278 36 劳氏粘虫 Leucanialoryi 70
4 绿尾大蚕蛾 Actias ningpoana 190 37 东亚飞蝗 Locusta migratoria manilensis 202
5 小地老虎 Agrotis ypsilon 46 38 草地螟 Loxostege stieticatis 65
6 黄地老虎 Agrotis segetum 77 39 甘蓝夜蛾 Mamestra brassicae Linnaeus 115
7 铜绿丽金龟 Anomala corpulenta 115 40 樟叶蜂 Mesonura rufonota 64
8 苎麻夜蛾 Arcte coerula 112 41 草蝉 Mogannia hebes 65
9 绿灰蝶 Artipe 220 42 稻眼蝶 Mycalesis gotama 155
10 二点委夜蛾 Athetis lepigone 129 43 鹰粘夜蛾 Mythimna impura 77
11 枯球箩纹蛾 Brahmaea wallichii 140 44 黑尾叶蝉 Nephotettix bipunctatus 215
12 稻赤斑沫蝉 Callitetix versicolor 60 45 稻绿蝽 Nezara viridula 119
13 桃小食心虫 Carposina sasakii 37 46 褐飞虱 Nilaparvata lugens 236
14 红褐斑腿蝗 Catantops pinguis 150 47 黑翅土白蚁 Odontotermes formosanus 94
15 咖啡透翅天蛾 Cephonodes hylas 223 48 中华稻蝗 Oxya chinensis 112
16 曲纹紫灰蝶 Chilades pandava 260 49 玉带凤蝶 Papilio polytes 212
17 二化螟 Chilo suppressalis 310 50 直纹稻弄蝶 Parnara guttata 170
18 稻蓟马 Chloethrips oryzae 76 51 隐纹谷弄蝶 Pelopidas mathias 120
19 豆天蛾 Clanis bilineata 193 52 苹掌舟蛾 Phalera flavescens 112
20 稻棘缘蝽 Cletus punctiger 60 53 扶桑绵粉蚧 Phenacoccus solenopsis 81
21 长肩棘缘蝽 Cletus trigonus 92 54 菜粉蝶 Pieris rapae 470
22 稻纵卷叶螟 Cnaphalocrocis medinalis 312 55 小菜蛾 Plutella xylostella 80
23 中华草螽 Conocephalus chinensis 99 56 白星花金龟 Protaetia brevitarsis 190
24 稻象甲 Echinocnemus squameus 76 57 紫茎甲 Sagra femorata purpurea 91
25 豆芫菁 Epicauta gorhami 62 58 大螟 Sesamia inferens 144
26 麻皮蝽 Erthesina fullo 74 59 黑额光叶甲 Smaragdina nigrifrons 65
27 宽边黄粉蝶 Eurema hecabe 280 60 白背飞虱 Sogatella furcifera 150
28 硕蝽 Eurostus validus 86 61 甜菜夜蛾 Spodoptera exigua 150
29 菜蝽 Eurydema dominulus 99 62 草地贪夜蛾 Spodoptera frugiperda 66
30 梨小食心虫 Grapholita molesta 56 63 斜纹夜蛾 Spodoptera litura 150
31 蟋蟀 Gryllidae 130 64 红脊长蝽 Tropidothorax elegans 62
32 蝼蛄 Gryllotalpa spps 132 65 大红蛱蝶 Vanessa indica 130
33 棉铃虫 Helicoverpa armigera 108 66 短角外斑腿蝗 Xenocatantops brachycerus 112

Fig. 3

Network architecture of DenseNet"

Fig. 4

Nonlinear transformation structure in Dense Block"

Fig. 5

Loss curves of five CNN models"

Fig. 6

Visualization of pest feature map (a) raw image; (b) Image features after the first convolution; (c) Image features of the first pooling"

Table 2

Identification results of 66 species of agricultural pests by different CNN models"

评价方法Evaluation measures CaffeNet GoogleNet VGG19 ResNet101 DenseNet121
平均识别率Average identification rate (%) 86.2 87.4 88.3 91.7 93.9
平均虚警率Average false alarm rate (%) 16.5 17.1 16.4 11.9 8.2

Fig. 7

Densenet121 accuracy curve"

Fig. 8

Some images from misidentified pests"

Fig. 9

APP interface of agricultural pest identification based on Android mobile phone"

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