Journal of Agricultural Science and Technology ›› 2022, Vol. 24 ›› Issue (5): 111-118.DOI: 10.13304/j.nykjdb.2021.0935
• INTELLIGENT AGRICULTURE & AGRICULTURAL MACHINERY • Previous Articles
Jianwei WU1,2,3(), Jie HUANG1, Xiaofei XIONG1,2,3, Han GAO3, Xiangyang QIN1()
Received:
2021-11-03
Accepted:
2022-01-18
Online:
2022-05-15
Published:
2022-06-06
Contact:
Xiangyang QIN
吴建伟1,2,3(), 黄杰1, 熊晓菲1,2,3, 高晗3, 秦向阳1()
通讯作者:
秦向阳
作者简介:
吴建伟 E-mail:wujw@nercita.org.cn;
基金资助:
CLC Number:
Jianwei WU, Jie HUANG, Xiaofei XIONG, Han GAO, Xiangyang QIN. Research and Application of Intelligent Recognition Method of Peach Tree Diseases Based on AI[J]. Journal of Agricultural Science and Technology, 2022, 24(5): 111-118.
吴建伟, 黄杰, 熊晓菲, 高晗, 秦向阳. 基于AI的桃树病害智能识别方法研究与应用[J]. 中国农业科技导报, 2022, 24(5): 111-118.
桃树病害类型 Type of peach tree disease | 样本数 Number of samples | 准确率 Accuracy rate/% |
---|---|---|
桃黑斑病 Peach black spot | 569 | 98.28 |
桃褐腐病 Peach brown rot | 336 | 94.12 |
桃黑星病 Peach scab | 280 | 92.86 |
桃炭疽病 Peach anthracnose | 282 | 96.55 |
桃缩叶病 Peach leaf curl | 268 | 92.31 |
桃灰霉病 Peach botrytis cinerea | 235 | 86.96 |
桃褐斑穿孔病 Peach brown spot perforation | 208 | 85.00 |
桃霉斑穿孔病 Peach mildew and perforation | 290 | 86.21 |
桃细菌性穿孔病 Peach bacterial perforation | 283 | 85.71 |
桃树流胶病 Peach gummosis | 268 | 92.31 |
桃树木腐病 Peach wood rot | 244 | 95.83 |
负样本 Negative sample | 1 000 | 98.00 |
总计 Total | 4 263 | 93.65 |
Tab.1 Test results of DenseNet-169 improved model for peach tree diseases image recognition
桃树病害类型 Type of peach tree disease | 样本数 Number of samples | 准确率 Accuracy rate/% |
---|---|---|
桃黑斑病 Peach black spot | 569 | 98.28 |
桃褐腐病 Peach brown rot | 336 | 94.12 |
桃黑星病 Peach scab | 280 | 92.86 |
桃炭疽病 Peach anthracnose | 282 | 96.55 |
桃缩叶病 Peach leaf curl | 268 | 92.31 |
桃灰霉病 Peach botrytis cinerea | 235 | 86.96 |
桃褐斑穿孔病 Peach brown spot perforation | 208 | 85.00 |
桃霉斑穿孔病 Peach mildew and perforation | 290 | 86.21 |
桃细菌性穿孔病 Peach bacterial perforation | 283 | 85.71 |
桃树流胶病 Peach gummosis | 268 | 92.31 |
桃树木腐病 Peach wood rot | 244 | 95.83 |
负样本 Negative sample | 1 000 | 98.00 |
总计 Total | 4 263 | 93.65 |
1 | 毕金峰,吕健,刘璇,等.国内外桃加工科技与产业现状及展望[J].食品科学技术学报, 2019,37(5):7-15. |
BI J F, LU J, LIU X, et al.. Research on techniques and industry situation and prospect for peach processing in domestic and aboard [J]. J. Food Sci. Technol., 2019,37(5):7-15. | |
2 | 朱更瑞.我国桃产业转型升级的思考[J].中国果树,2019(6):6-11. |
ZHU G R. Insights into transformation and upgrading of peach industry in China [J]. China Fruits, 2019 (6):6-11. | |
3 | 马革农,李建勋,张相斌,等.桃树病虫害防治中存在的问题及对策[J].现代农村科技, 2021(3):29-30. |
4 | 刘圣华.果树病虫害的识别方法及防治策略[J].农业与技术, 2017,37(19):54,70. |
LIU S H. Identification methods and control strategies of fruit tree diseases and pests [J]. Agric. Technol., 2017,37(19):54,70. | |
5 | 朱伊平,管孝锋,黄海龙,等.农业病虫害远程诊断平台[J].浙江农业科学, 2020,61(09):1819-1820, 1832. |
ZHU Y P, GUAN X F, HUANG H L, et al. Research on remote diagnosis platform of agricultural diseases and insect pests [J]. J. Zhejiang Agric. Sci., 2020,61(9):1819-1820, 1832. | |
6 | 张嫚嫚,张武,金秀,等.农作物病虫害专家系统中的知识表示方法[J].江汉大学学报(自然科学版), 2019,47(4):378-384. |
ZHANG M M, ZHANG W, JIN X, et al. Knowledge representation methods of expert systems for crop pests and diseases [J]. J. Jianghan Univ. (Nat. Sci.), 2019,47(4):378-384. | |
7 | 赵春江.对发展农业智能科技的思考[J].机器人产业, 2020,(4):36-40. |
ZHAO C J. Thinking on the development of agricultural intelligent science and technology [J]. Robot Ind., 2020,(4):36-40. | |
8 | 张宇,王翠宁,秦妮妮.优化识别植物病虫害的方法[J].种子科技, 2020,38(7):77-78. |
ZHANG Y, WANG C N, QIN N N. Methods for optimizing identification of plant diseases and insect pests [J]. Seed Sci. Technol., 2020,38(7):77-78. | |
9 | 康飞龙,李佳,刘涛,等.多类农作物病虫害的图像识别应用技术研究综述[J].江苏农业科学, 2020,48(22):22-27. |
KANG F L, LI J, LIU T, et al.. Application technology of image recognition for various crop diseases and insect pests: a review [J]. Jiangsu Agric. Sci., 2020,48(22):22-27. | |
10 | 饶晓燕,吴建伟,李春朋,等.智慧苹果园“空-天-地”一体化监控系统设计与研究[J].中国农业科技导报, 2021,23(6):59-66. |
RAO X Y, WU J W, LI C P, et al. Design and research on “space-air-ground” integrated monitoring system for intelligent orchard [J]. J. Agric. Sci. Technol., 2021,23(6):59-66. | |
11 | 李素,郭兆春,王聪,等.信息技术在农作物病虫害监测预警中的应用综述[J].江苏农业科学, 2018,46(22):1-6. |
LI S, GUO Z C, WANG C, et al. Application of information technology in monitoring and early warning of crop diseases and insect pests [J]. Jiangsu Agric. Sci., 2018,46(22):1-6. | |
12 | 李禾.AI变身农业“医生”问诊作物病虫害[J].当代农机, 2018(12):20-21. |
LI H. AI turns into an agricultural “doctor” to inquire about crop diseases and pests [J]. Contemporary Farm Machinery, 2018(12):20-21. | |
13 | 王彦翔,张艳,杨成娅,等.基于深度学习的农作物病害图像识别技术进展[J].浙江农业学报, 2019,31(4):669-676. |
WANG Y X, ZHANG Y, YANG C Y, et al. Advances in new nondestructive detection and identification techniques of diseases based on deep learning [J]. Acta Agric. Zhejiangensis, 2019,31(4):669-676. | |
14 | 康飞龙,李佳,刘涛,等.多类农作物病虫害的图像识别应用技术研究综述[J].江苏农业科学, 2020,48(22):22-27. |
KANG F L, LI J, LIU T, et al. Application technology of image recognition for various crop diseases and insect pests: a review [J]. Jiangsu Agric. Sci., 2020,48(22):22-27. | |
15 | 边柯橙,杨海军,路永华.深度学习在农业病虫害检测识别中的应用综述[J].软件导刊, 2021,20(3):26-33. |
BIAN K C, YANG H J, LU Y H. Application review of deep learning in detection and identification of agricultural pests and diseases [J]. Software Guide, 2021,20(3):26-33. | |
16 | 周惠汝,吴波明.深度学习在作物病害图像识别方面应用的研究进展[J].中国农业科技导报, 2021,23(5):61-68. |
ZHOU H R, WU B M. Advances in research on deep learning for crop disease image recognition [J]. J. Agric. Sci. Technol., 2021,23(5):61-68. | |
17 | 盖荣丽,蔡建荣,王诗宇,等.卷积神经网络在图像识别中的应用研究综述[J].小型微型计算机系统, 2021,42(9):1980-1984. |
GAI L R, CAI J R, WANG S Y, et al.. Research review on image recognition based on deep learning [J]. J. Chin. Computer Sys., 2021,42(9):1980-1984. | |
18 | TOO C N, LI Y J, NJUKI S, et al.. A comparative study of fine-tuning deep learning models for plant disease identification [J]. Computers Electron. Agric., 2019,161:272-279. |
19 | 黄双萍,孙超,齐龙,等.基于深度卷积神经网络的水稻穗瘟病检测方法[J]. 农业工程学报, 2017,33(20):169-176. |
HUANG S P, SUN C, QI L, et al.. Rice panicle blast identification method based on deep convolution neural network [J]. Trans. Chin. Soc. Agric. Eng., 2017,33(20):169-176. | |
20 | 刘阗宇,冯全,杨森.基于卷积神经网络的葡萄叶片病害检测方法[J].东北农业大学学报, 2018,49(3):73-83. |
LIU T Y, FENG Q, YANG S. Detecting grape diseases based on convolutional neural network [J]. J. Northeast Agric.Univ., 2018,49(3):73-83. | |
21 | 郭小清,范涛杰,舒欣. 基于改进Multi-Scale AlexNet的番茄叶部病害图像识别[J].农业工程学报, 2019,35(13):162-169. |
GUO X Q, FAN T J, SHU X. Tomato leaf diseases recognition based on improved multi-scale Alexnet [J]. Trans. Chin. Soc. Agric. Eng., 2019,35(13):162-169. | |
22 | 张敏,刘杰,蔡高勇.基于卷积神经网络的柑橘溃疡病识别方法[J].计算机应用, 2018,38(S1):48-52, 76. |
ZHANG M, LIU J, CAI G Y. Recognition method of citrus canker disease based on convolution neural network [J]. J. Computer Appl., 2018,38(S1):48-52, 76. | |
23 | 金瑛,叶飒,李洪磊.基于ResNet-50深度卷积网络的果树病害智能诊断模型研究[J].农业图书情报学报, 2021,33(4):58-67. |
JIN Y, YE S, LI H L. The intelligent diagnosis model of fruit tree disease based on ResNet-50 [J]. J. Library Inform. Sci. Agric., 2021,33(4):58-67. | |
24 | 宋晨勇,白皓然,孙伟浩,等.基于GoogLeNet改进模型的苹果叶病诊断系统设计[J].中国农机化学报, 2021,42(7):148-155. |
SONG C Y, BAI H R, SUN W H, et al.. Design of apple leaf disease diagnosis system based on GoogLeNet improved model [J]. J. Chin. Agric. Mechanization, 2021,42(7):148-155. | |
25 | YAO N, NI F, WANG Z, et al.. L2 MXception: an improved Xception network for classification of peach diseases [J/OL]. Plant Methods, 2021,17: 36 [2022-04-12]. . |
26 | HUANG G, LIU Z, LAURENS V D M, et al. Densely connected convolutional networks [C]// IEEE. Proceedings of the IEEE Conference on Computer Vision and Pattern Recongnition. Honolulu, USA, 2016:2261-2269. |
27 | 任胜男,孙钰,张海燕,等.基于one-shot学习的小样本植物病害识别[J].江苏农业学报, 2019,35(5):1061-1067. |
REN S N, SUN Y, ZHANG H Y, et al.. Plant disease identification for small sample based on one-shot learning [J]. Jiangsu J. Agric. Sci., 2019,35(5):1061-1067. | |
28 | 项小东,翟蔚,黄言态,等.基于Xception-CEMs神经网络的植物病害识别[J].中国农机化学报, 2021,42(8):177-186. |
XIANG X D, ZHAI W, HUANG Y T, et al.. Plant disease recognition based on Xception-CEMs neural network [J]. J. Chin. Agric. Mechanization, 2021,42(8):177-186. | |
29 | 宋余庆,谢熹,刘哲,等.基于多层EESP深度学习模型的农作物病虫害识别方法[J].农业机械学报, 2020,51(8):196-202. |
SONG Y Q, XIE X, LIU Z, et al.. Crop pests and diseases recognition method based on multi-level EESP model [J]. Trans. Chin. Soc. Agric. Machinery, 2020,51(8):196-202. | |
30 | 计雪伟,霍兴赢,薛端,等.基于深度学习的农作物病虫害识别方法[J].南方农机, 2020,51(23):182-183. |
JI X W, HUO X Y, XUE R, et al.. Identification of crop diseases and insect pests based on deep learning [J]. South Agric. Machinery, 2020,51(23):182-183. | |
31 | 敖良忠,马瑞阳,杨学文.基于DenseNet和ResNet融合的发动机孔探图像分类研究[J].计算技术与自动化, 2021,40(3):105-110, 183. |
AO L Z, MA R Y, YANG X W. Research on engine borescope images classification based on DenseNet and ResNet fusion [J]. Computing Technol. Autom., 2021,40(3):105-110, 183. |
[1] | Yinyan GAO, Yi SUN, Baochun LI. Estimating of Wheat Ears Number in Field Based on RGB Images Using Unmanned Aerial Vehicle [J]. Journal of Agricultural Science and Technology, 2022, 24(3): 103-110. |
[2] | ZHOU Huiru, WU Boming. Advances in Research on Deep Learning for Crop Disease Image Recognition [J]. Journal of Agricultural Science and Technology, 2021, 23(5): 61-68. |
[3] | LYU Chunyang, LIU Shengping, GUO Xiuming, XIAO Shunfu, LIU Dazhong, YANG Feifei, LI Luhua. Detection of Honeybee Based on SSD Model [J]. Journal of Agricultural Science and Technology, 2021, 23(5): 98-107. |
[4] | XU Beibei1, WANG Wensheng1,2*, GUO Leifeng1, CHEN Guipeng3. A Review and Future Prospects on Cattle Recognition Based on Non-contact Identification [J]. Journal of Agricultural Science and Technology, 2020, 22(7): 79-89. |
[5] | DONG Wei1, QIAN Rong1, ZHANG Jie 2*, ZHANG Liping1, CHEN Hongbo2, ZHANG Meng1, ZHU Jingbo1, BU Yingqiao3. Vegetable Lepidopteran Pest Auto Recognition and Detection Counting Based on Deep Learning [J]. Journal of Agricultural Science and Technology, 2019, 21(12): 76-84. |
[6] | SU Yi-feng§, DU Ke-ming§, LI Ying, SUN Zhong-fu*, ZHENG Fei-xiang. Preliminary Research on Diagnosis System Design of Wheat Diseases and Pests Based on the Internet of Things [J]. Journal of Agricultural Science and Technology, 2016, 18(2): 86-94. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||