Scientia Agricultura Sinica ›› 2022, Vol. 55 ›› Issue (8): 1557-1567.doi: 10.3864/j.issn.0578-1752.2022.08.007

• PLANT PROTECTION • Previous Articles     Next Articles

Intelligent Forecasting Method of Rice Sheath Blight Based on Images

HAN XiaoTong1(),YANG BaoJun2,LI SuXuan1,LIAO FuBing1,LIU ShuHua2,TANG Jian2,YAO Qing1   

  1. 1School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018
    2State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 311401
  • Received:2021-11-03 Accepted:2021-12-23 Online:2022-04-16 Published:2022-05-11
  • Contact: Qing YAO


【Objective】At present, the forecast of rice sheath blight relies on the number of diseased clusters, the number of rice plants and the severity of each plant to calculate the disease index based on manual surveys. The method is highly professional, time-consuming and laborious. The data is difficult to trace. The objective of this study is to propose a detection model of rice sheath blight lesions and a damage grading model of rice sheath blight based on images, and to provide a theoretical basis for the intelligent forecasting of rice sheath blight.【Method】Images of rice sheath blight in paddy field were collected by a portable image acquisition instrument. Different detection models (Cascade R-CNN and RetinaNet) and feature extraction networks (VGG-16 and ResNet-101) were developed to test the detection effect of disease lesions. The best model was chosen. However, the Cascade R-CNN model appeared some missing detection of sheath blight lesions. Because the sheath blight lesions are irregular in shape, and variable in size and location, the Cascade R-CNN model was improved through adding OHEM structure to balance the hard and easy samples in the network and choosing the bounding box regression loss function. The precision rate, missing rate, average precision and P-R curve were used to evaluate the detection effects of different models. Based on the detection results of the improved Cascade R-CNN-OHEM-GIOU model, two damage grading models based on the area and number of disease lesions were developed, respectively. The determination coefficient (R2) and Kappa value were used to choose the damage level model of rice sheath blight. 【Result】Under the same backbone network conditions, the Cascade R-CNN model had a better detection effect on rice sheath blight than the RetinaNet model. The Cascade R-CNN-ResNet-101 model had the best detection effect on sheath blight lesions. The precision rate was 92.4%, the average precision was 88.2% and the missing rate was 14.9%. The improved Cascade R-CNN-OHEM- GIOU model effectively solved the problem of sample imbalance, and effectively reduced the missing rate by adding a border regression loss function, which was 8.7% lower than the missing rate of the Cascade R-CNN-ResNet-101 model, and the average precision was increased to 92.3%. With the results of manual disease grading as the standard, the grading model of rice sheath blight at 0 to 5 grades based on the area of diseased lesions had the accurate rates of 96.0%, 90.0%, 82.0%, 76.0%, 74.0% and 96.0%, respectively. The average grading accuracy rate was 85.7%, and the Kappa coefficient was 0.83. The damage grade results of rice sheath blight based on images were consistent with the manual grading results.【Conclusion】The intelligent forecasting method of rice sheath blight based on images can automatically detect the disease lesions and calculate the damage grade. This method increases the intelligence level and the results are objective and traceable. It may also provide an idea for intelligent forecasting of other crop diseases.

Key words: rice sheath blight, disease lesion image, intelligent forecasting, Cascade R-CNN model, damage grading model

Fig. 1

Rice sheath blight image collection"

Fig. 2

Rice sheath blight image"

Fig. 3

Marking the lesions of rice sheath blight"

Fig. 4

Four methods of image data enhancement"

Table 1

Dataset information of rice sheath blight"

Number of images with lesions
Number of images without lesions
Lesion number
Average lesion number per image
训练集 Training set 4122 896 25093 5.0
验证集 Validation set 1766 384 12257 5.7
测试集 Testing set 250 50 1620 5.4

Fig. 5

Cascade R-CNN-OHEM-GIOU network diagram"

Table 2

Detection results of six models on rice sheath blight lesions"

Model type
Number of detected lesions
Number of correct identified lesions
Precision rate
Missing rate
AP (%)
RetinaNet-VGG-16 1404 1230 87.6 24.1 /
RetinaNet-ResNet-101 1444 1311 90.8 19.1 /
Cascade R-CNN-VGG-16 1439 1298 90.2 19.9 /
Cascade R-CNN-ResNet-101 1492 1379 92.4 14.9 88.2
Cascade R-CNN-GIOU 1520 1401 92.2 13.5 89.1
Cascade R-CNN-OHEM-GIOU 1644 1519 92.4 6.2 92.3

Fig. 6

P-R curve of three Cascade R-CNN models"

Fig. 7

Image of detection results before and after improved models on sheath blight lesions"

Fig. 8

Disease grade prediction model based on area of diseased lesions"

Fig. 9

Disease grade prediction model based on the number of disease lesions"

Table 3

Confusion matrix of rice sheath blight grading"

Manual grading
模型分级Model grading
0 1 2 3 4 5 总计Total
0 48 2 0 0 0 0 50
1 1 45 4 0 0 0 50
2 0 2 41 7 0 0 50
3 0 0 2 38 10 0 50
4 0 0 0 1 37 12 50
5 0 0 0 0 2 48 50
总计Total 49 49 47 46 49 60 300
Kappa 0.83
[1] 中华人民共和国国家标准. 稻纹枯病测报技术规范: GB/T 15791- 2011. 北京: 中国标准出版社, (2011-09-29) [2021-11-03].
National Standards of the People’s Republic of China. Rules of monitoring and forecasting for the rice sheath blight (Rhizoctonia solani Kukn):GB/T 15791-2011. Beijing: China Standards Press, (2011-09-29) [2021-11-03]. (in Chinese)
[2] 王献锋, 张善文, 王震, 张强. 基于叶片图像和环境信息的黄瓜病害识别方法. 农业工程学报, 2014, 30(14): 148-153.
WANG X F, ZHANG S W, WANG Z, ZHANG Q. Recognition of cucumber diseases based on leaf image and environmental information. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(14): 148-153. (in Chinese)
[3] 胡耀华, 平学文, 徐明珠, 单卫星, 何勇. 高光谱技术诊断马铃薯叶片晚疫病的研究. 光谱学与光谱分析, 2016, 36(2): 515-519.
HU Y H, PING X W, XU M Z, SHAN W X, HE Y. Detection of late blight disease on potato leaves using hyperspectral imaging technique. Spectroscopy and Spectral Analysis, 2016, 36(2): 515-519. (in Chinese)
[4] 葛婧. 基于计算机图像处理技术的作物病害等级检测[D]. 合肥: 安徽农业大学, 2007.
GE J. Detection of crops plant disease rank based on computer image processing technology[D]. Hefei: Anhui Agricultural University, 2007. (in Chinese)
[5] 马德贵, 邵陆寿, 葛婧, 丁克坚, 钱良存. 水稻稻瘟病及水稻纹枯病病害程度图像检测. 中国农学通报, 2008, 24(9): 485-489.
MA D G, SHAO L S, GE J, DING K J, QIAN L C. Detection of the harm degree of rice blast and rice sheath blight. Chinese Agricultural Science Bulletin, 2008, 24(9): 485-489. (in Chinese)
[6] 袁媛, 陈雷, 吴娜, 李淼. 水稻纹枯病图像识别处理方法研究. 农机化研究, 2016, 38(6): 84-87, 92.
YUAN Y, CHEN L, WU N, LI M. Recognition of rice sheath blight based on image procession. Journal of Agricultural Mechanization Research, 2016, 38(6): 84-87, 92. (in Chinese)
[7] KRISHNA R V V, KUMAR S S.Computer vision based identification of nitrogen and potassium deficiency in FCV tobacco//Proceedings of the International Conference on Computational Science and Engineering, 2016: 105-111.
[8] RAMCHARAN A, BARANOWSKI K, MCCLOSKEY P, AHMED B, LEGG J, HUGHES D P. Deep learning for image-based cassava disease detection. Frontiers in Plant Science, 2017, 8: 1852.
doi: 10.3389/fpls.2017.01852
[9] 苏婷婷, 牟少敏, 董萌萍, 时爱菊. 深度迁移学习在花生叶部病害图像识别中的应用. 山东农业大学学报(自然科学版), 2019, 50(5): 865-869.
SU T T, MU S M, DONG M P, SHI A J. Application of deep transfer learning in image recognition of peanut leaf diseases. Journal of Shandong Agricultural University (Natural Science Edition), 2019, 50(5): 865-869. (in Chinese)
[10] FUENTES A, YOON S, KIM S C, PARK D S. A robust deep- learning-based detector for real-time tomato plant diseases and pests recognition. Sensors, 2017, 17(9): 2022.
doi: 10.3390/s17092022
[11] RAMESH S, VYDEKI D. Recognition and classification of paddy leaf diseases using optimized deep neural network with Jaya algorithm. Information Processing in Agriculture, 2020, 7(2): 249-260.
doi: 10.1016/j.inpa.2019.09.002
[12] 曹英丽, 江凯伦, 于正鑫, 肖文, 刘亚帝. 基于深度卷积神经网络的水稻纹枯病检测识别. 沈阳农业大学学报, 2020, 51(5): 568-575.
CAO Y L, JIANG K L, YU Z X, XIAO W, LIU Y D. Detection and recognition of rice sheath blight based on deep convolutional neural network. Journal of Shenyang Agricultural University, 2020, 51(5): 568-575. (in Chinese)
[13] 俞佩仕, 郭龙军, 姚青, 杨保军, 唐健, 许渭根, 陈渝阳, 朱旭华, 陈宏明, 张晨光, 段德康, 贝文勇, 彭晴晖. 基于移动终端的稻田飞虱调查方法. 昆虫学报, 2019, 62(5): 615-623.
YU P S, GUO L J, YAO Q, YANG B J, TANG J, XU W G, CHEN Y Y, ZHU X H, CHEN H M, ZHANG C G, DUAN D K, BEI W Y, PENG Q H. A survey method based on mobile terminal for rice planthoppers in paddy fields. Acta Entomologica Sinica, 2019, 62(5): 615-623. (in Chinese)
[14] EVERINGHAM M, VAN GOOL L, WILLIAMS C K I, WINN J, ZISSERMAN A. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 2010, 88(2): 303-338.
doi: 10.1007/s11263-009-0275-4
[15] CAI Z, VASCONCELOS N. Cascade R-CNN: Delving into high quality object detection//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 6154-6162.
[16] REN S, HE K, GIRSHICK R, SUN J. Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, 2015, 28: 91-99.
[17] SHRIVASTAVA A, GUPTA A, GIRSHICK R. Training region-based object detectors with online hard example mining//Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 761-769.
[18] REZATOFIGHI H, TSOI N, GWAK J Y, SADEGHIAN A, REID I, SAVARESE S. Generalized intersection over union: A metric and a loss for bounding box regression//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 658-666.
[19] ALTMAN E I, FINANCE M A. Predicting financial distress of companies:Revisiting the Z-Score and ZETA models//Handbook of Research Methods & Applications in Empirical Finance. Edward Elgar Publishing, 2013.
[20] 刘明. 支持向量机中Sigmoid核函数的研究[D]. 西安: 西安电子科技大学, 2009.
LIU M. The study on sigmoid kernel function in support vector machine[D]. Xi’an: Xidian University, 2009. (in Chinese)
[21] LIN T Y, GOYAL P, GIRSHICK R, HE K, DOLLAR P. Focal loss for dense object detection//Proceedings of the IEEE International Conference on Computer Vision, 2017: 2980-2988.
[22] LIU X, CHI M, ZHANG Y F, QIN Y Q. Classifying high resolution remote sensing images by fine-tuned VGG deep networks//IGARSS IEEE International Geoscience and Remote Sensing Symposium, 2018: 7137-7140.
[23] HE K, ZHANG X, REN S. Deep residual learning for image recognition//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
[24] BUCKLAN M, GEY F. The relationship between recall and precision. Journal of the American Society for Information Science, 1994, 45(1): 12-19.
doi: 10.1002/(SICI)1097-4571(199401)45:1<12::AID-ASI2>3.0.CO;2-L
[25] 赵松山. 对拟合优度R2的影响因素分析与评价. 东北财经大学学报, 2003(3): 56-58.
ZHAO S S.Analysis and evaluation of influencing factors of goodness of fit R2. Journal of Dongbei University of Finance and Economics, 2003(3): 56-58. (in Chinese)
[26] 许文宁, 王鹏新, 韩萍, 严泰来, 张树誉. Kappa系数在干旱预测模型精度评价中的应用--以关中平原的干旱预测为例. 自然灾害学报, 2011, 20(6): 81-86.
XU W N, WANG P X, HAN P, YAN T L, ZHANG S Y. Application of Kappa coefficient in accuracy assessments of drought forecasting model: A case study of Guanzhong Plain. Journal of Natural Disasters, 2011, 20(6): 81-86. (in Chinese)
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