基于深度学习的月季多叶片病虫害检测研究

doi:10.13733/j.jcam.issn.2095-5553.2021.08.23

摘要/Abstract

摘要： 月季病虫害严重影响月季产量和观赏性,将目标检测算法应用到月季病虫害检测中有利于提高月季病虫害检测效率,对实现月季智能化种植培育起到重要支撑作用。针对实际种植场景中复杂背景对病虫害检测的影响,以及病虫害形状大小特点,提出两阶段月季病虫害检测方法TSDDP,首先添加调优后的Inception模块改进YOLOv3模型特征提取与融合能力对自然环境下拍摄的月季多叶片图像进行叶片检测,去除复杂背景中存在的影响因素,然后通过K-means聚类Anchor box优化Faster R-CNN以满足月季病虫害目标检测需求,基于叶片检测结果对叶片病虫害进行检测。通过比较YOLOv3、Faster R-CNN和TSDDP对自然环境下的月季多叶片病虫害检测效果,试验结果表明TSDDP的检测精度和定位准确度均高于其他算法,最终病虫害平均检测精度达到82.26%,有效减少复杂背景造成误检的同时改善小尺度病虫害的检测和定位效果。

关键词: 病虫害检测, YOLOv3, 特征融合, Faster R-CNN

Abstract: Diseases and insect pests of roses seriously affect the yield and ornamental value. The application of a target detection algorithm in the detection of rose diseases and insect pests is conducive to improve the detection efficiency and plays an important role in the realization of the intelligent cultivation of roses. In view of the influence of complex background on the detection of diseases and insect pests in the actual planting scene, a twostage rose pest detection method TSDDP was proposed in this paper. Firstly, the optimized Inception module was added to improve the ability of feature extraction and fusion of YOLOv3 model. The leaf detection of multileaf images of roses in the natural environment was carried out to remove the shadow in the complex background. Then, Faster RCNN was optimized by Kmeans clustering Anchor box to meet the needs of target detection of rose diseases and insect pests. By comparing the detection effects of YOLOv3, Faster RCNN, and TSDDP in the natural environment, the results showed that the detection accuracy and positioning accuracy of TSDDP were higher than other algorithms, and the final average detection accuracy of pests and diseases reached 82.26%. This can effectively reduce the false detection caused by complex backgrounds and improve the detection and location effect of smallscale diseases and insect pests.

Key words: diseases and pests detection, YOLOv3, feature fusion, Faster RCNN

中图分类号:

李子茂, 刘恋冬, 夏梦, 帖军, 张玥, . 基于深度学习的月季多叶片病虫害检测研究[J]. 中国农机化学报, 2021, 42(8): 169-176.

Li Zimao, Liu Liandong, Xia Meng, Tie Jun, Zhang Yue. . Detection of rose diseases and insect pests based on deep learning[J]. Journal of Chinese Agricultural Mechanization, 2021, 42(8): 169-176.

参考文献

［１］　
林君宇, 李奕萱, 郑聪尉, 等. 应用卷积神经网络识别花卉及其病症［J］. 小型微型计算机系统, 2019, 40(6): 1330-1335.

Lin Junyu, Li Yixuan, Zheng Congwei, et al. Classifying flowers and their diseases by using convolutional neural network ［J］. Journal of Chinese Computer Systems, 2019, 40(6): 1330-1335.

［2］　
刘建凤, 吉春明, 卫甜, 等. 月季主要病虫害的诊断与综合防治技术［J］. 江苏农业科学, 2019, 47(8): 17-119.

［3］　
曹乐平. 基于机器视觉的植物病虫害实时识别方法［J］. 中国农学通, 2015, 31(20): 244-249.

［4］　
汪京京, 张武, 刘连忠, 等. 农作物病虫害图像识别技术的研究综述［J］. 计算机工程与科学, 2014(7): 1363-1370.

Wang Jingjing, Zhang Wu, Liu Lianzhong, et al. Summary of crop diseases and pests image recognition technology ［J］. Computer Engineering & Science. 2014(7): 1363-1370.

［5］　
曹静, 夏秀红. 月季常见病害的发生及其防治技术［J］. 农业工程技术(温室园艺), 2009(3): 72-73.

［6］　
张开兴, 吕高龙, 贾浩, 等. 基于图像处理和BP神经网络的玉米叶部病害识别［J］. 中国农机化学报, 2019, 40(8): 122-126.

Zhang Kaixing, Lü Gaolong, Jia Hao, et al. Identification of corn leaf disease based on image processing and BP neural network ［J］. Journal of Chinese Agricultural Mechanization, 2019, 40(8): 122-126.

［7］　
刘翠翠, 杨涛, 马京晶, 等. 基于PCASVM的麦冬叶部病害识别系统［J］. 中国农机化学报, 2019, 40(8): 132-136.

Liu Cuicui, Yang Tao, Ma Jingjing, et al. Identification system for leaf diseases of ophipogon japonicus based on PCASVM ［J］. Journal of Chinese Agricultural Mechanization, 2019, 40(8): 132-136.

［8］　
Arivazhagan S, Shebiah R N, Ananthi S, et al. Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features ［J］. Agricultural Engineering International: The CIGR ejournal, 2013, 15(1): 211-217.

［9］　
付中正, 何潇, 方逵, 等. 基于改进SSD网络的西兰花叶片检测研究［J］. 中国农机化学报, 2020, 41(4): 92-97.

Fu Zhongzheng, He Xiao, Fang Kui, et al. Study on the detection of broccoli leaves based on the improved SSD network ［J］. Journal of Chinese Agricultural Mechanization, 2020, 41(4): 92-97.

［10］　
孙鹏, 陈桂芬, 曹丽英. 基于注意力卷积神经网络的大豆害虫图像识别［J］. 中国农机化学报, 2020, 41(2): 171-176.

Sun Peng, Chen Guifen, Cao Liying. Image recognition of soybean pests based on attention convolutional neural network ［J］. Journal of Chinese Agricultural Mechanization, 2020, 41(2): 171-176.

［11］　
陈梅香, 杨信延, 石宝才, 等. 害虫自动识别与计数技术研究进展与展望［J］. 环境昆虫学报, 2015, 37(1): 176-183.

Chen Meixiang, Yang Xinyan, Shi Baocai, el at. Research progress and prospect of technologies for automatic identifying and counting of pests ［J］. Journal of Environmental Entomology, 2015, 37(1): 176-183.

［12］　
Sabrol H, Satish K. Tomato plant disease classification in digital images using classification tree ［C］. International Conference on Communication & Signal Processing. IEEE, 2016.

［13］　
Jaisakthi S M, Mirunalini P, Thenmozhi D, et al. Grape leaf disease identification using machine learning techniques ［C］. International Conference on Computational Intelligence in Data Science (ICCIDS), 2019.

［14］　
Dechant C, WiesnerHanks T, Chen S, et al. Automated identification of northern leaf blightinfected maize plants from field imagery using deep learning ［J］. Phytopathology, 2017: 1426-1432.

［15］　
Jiang P, Chen Y, Liu B, et al. Realtime detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks ［J］. IEEE Access, 2019, 7: 59069-59080.

［16］　
Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions ［C］. Boston, MA, USA: IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1-9.

［17］　
Ozguven M M, Adem K. Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms ［J］. Physica A: Statistical Mechanics and its Applications, 2019, 535: 122537.

［18］　
Ren S, He K, Girshick R, et al. Faster RCNN: Towards realtime object detection with region proposal networks ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 39(6).

［19］　
Liu J, Wang X. Tomato diseases and pests detection based on improved Yolo V3 convolutional neural network ［J］. Frontiers in Plant Science, 2020, 11: 898.

［20］　
Redmon J, Farhadi A. YOLOv3: An incremental improvement ［J］. arXiv preprint arXiv: 1804.02767, 2018.

［21］　
Simonyan K, Zisserman A. Very deep convolutional networks for largescale image recognition ［J］. Computer Science, 2014.

［22］　
He K, Zhang X, Ren S, et al. Deep residual learning for image recognition ［J］. Computer Vision and Pattern Recognition, 2016: 770-778.

［23］　
陈正斌, 叶东毅, 朱彩霞, 等. 基于改进YOLOv3的目标识别方法［J］. 计算机系统应用, 2020, 29(1): 49-58.

Chen Zhengbin, Ye Dongyi, Zhu Caixia, et al. Object recognition method based on improved YOLOv3 ［J］. Computer Systems & Applications, 2020, 29(1): 49-58.

［24］　
Uijlings J R R, Van De Sande K E A, Gevers T, et al. Selective search for object recognition ［J］. International Journal of Computer Vision, 2013, 104(2): 154-171.

[1]	何颖, 陈丁号, 彭琳. 基于改进YOLOv5模型的经济林木虫害目标检测算法研究[J]. 中国农机化学报, 2022, 43(4): 106-115.
[2]	高文强, , 肖志云, . 多尺度特征融合1D—CNN的马铃薯植株高光谱数据地物分类和缺素识别#br#[J]. 中国农机化学报, 2022, 43(3): 111-119.
[3]	鲁梦瑶, 周强, 姜舒文, 王聪, 陈栋, 陈天恩, . 基于深度学习与多尺度特征融合的烤烟烟叶分级方法[J]. 中国农机化学报, 2022, 43(1): 158-166.
[4]	成伟;张文爱;冯青春;张万豪;. 基于改进YOLOv3的温室番茄果实识别估产方法[J]. 中国农机化学报, 2021, 42(4): 176-182.
[5]	宋中山, 刘越, 郑禄, 帖军, 汪进. 基于改进YOLOV3的自然环境下绿色柑橘的识别算法^*[J]. 中国农机化学报, 2021, 42(11): 159-165.
[6]	高菊玲;. 基于多视图特征融合的植物病害识别算法[J]. 中国农机化学报, 2020, 41(12): 147-152.
[7]	丁顺荣;肖珂;. 基于粒子群优化SVM和多特征融合的鱼类分类方法研究[J]. 中国农机化学报, 2020, 41(11): 113-118+170.
[8]	李秀昊;马旭;李泽华;邓向武;李宏伟;. 基于特征融合和SVM的稻谷品种识别[J]. 中国农机化学报, 2019, 40(7): 97-102.