Abstract
In the era of twenty-first century, artificial intelligence plays a vital role in the day to day life of human beings. Now days it has been used for many application such as medical, communication, object detection, object identification, object tracking. This paper is focused on the identification of diseases in bell pepper plant in large fields using deep learning approach. Bell Pepper farmers, in general do not notice if their plants are infected with bacterial spot disease. The spread of the disease usually causes a decrease in the yield. The solution is to detect if bacterial spot disease is present in the bell pepper plant at an early stage. We do some random sampling of few pictures from different parts of the farm. YOLOv5 is used for detecting the bacterial spot disease in bell pepper plant from the symptoms seen on the leaves. With YOLO v5 we are able to detect even a small spot of disease with considerable speed and accuracy. It takes the full image in a single instant and predicts bounding boxes and class probability. The input to the model is random picture from the farm by using a mobile phone. By viewing the output of the program, farmers can find out whether bacterial spot disease has in any way affected the plants in their farm. The proposed model is very useful for framers, as they can identify the plant diseases as soon as it appears and thus, do proper measures to prevent the spread of the disease. The motive of this paper is to come up with a method of detecting the bacterial spot disease in bell pepper plant from pictures taken from the farm.
Similar content being viewed by others
Availability of data and material
Image dataset used in this research is available online.
Code availability
Not applicable.
References
Khan, S., Tufail, M., Khan, M.T., et al.: Deep learning-based identification system of weeds and crops in strawberry and pea fields for a precision agriculture sprayer. Precision Agric. (2021). https://doi.org/10.1007/s11119-021-09808-9
Lowe, D. G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999, vol. 2, pp. 1150–1157. IEEE, (1999)
Papageorgiou, C. P., Oren, M., Poggio, T.: A general framework for object detection. In: Sixth International Conference on Computer Vision, 1998, pp. 555–562. IEEE, (1998)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886–893. IEEE, (2005)
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: Decaf: a deep convolutional activation feature for generic visual recognition. arXiv preprint http://arxiv.org/abs/1310.1531, (2013)
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: Integrated recognition, localization and detection using convolutional networks. CoRR, http://arxiv.org/abs/1312.6229, (2013)
Sultana, F., Sufian, A., Dutta, P.: A review of object detection models based on convolutional neural network. In: Mandal J., Banerjee S. (eds) Intelligent Computing: Image Processing Based Applications. Advances in Intelligent Systems and Computing, vol. 1157. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-4288-6_1
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 580–587. IEEE, (2014)
Chen, B., Miao, X.: Distribution line pole detection and counting based on YOLO using UAV inspection line video. J. Electr. Eng. Technol. 15, 441–448 (2020). https://doi.org/10.1007/s42835-019-00230-w
Wageeh, Y., Mohamed, H.E.D., Fadl, A., et al.: YOLO fish detection with Euclidean tracking in fish farms. J. Ambient Intell. Human Comput. 12, 5–12 (2021). https://doi.org/10.1007/s12652-020-02847-6
Zhao, J., Li, C., Xu, Z., et al.: Detection of passenger flow on and off buses based on video images and YOLO algorithm. Multimed Tools Appl. (2021). https://doi.org/10.1007/s11042-021-10747-w
Hou, X., Zhang, Y., Hou, J.: Application of YOLO V2 in construction vehicle detection. In:Meng, H., Lei, T., Li, M., Li, K., Xiong, N., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2020. Advances in Intelligent Systems and Computing, vol. 1348. Springer, Cham (2021) https://doi.org/10.1007/978-3-030-70665-4_135
Yang, S., Bo, C., Zhang, J., Wang, M.: Vehicle logo detection based on modified YOLOv2. In: Lu, H., Yujie, L. (eds) 2nd EAI International Conference on Robotic Sensor Networks. EAI/Springer Innovations in Communication and Computing. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-17763-8_8
Sujee, R., Shanthosh, D., Sudharsun, L.: Fabric defect detection using YOLOv2 and YOLOv3 Tiny. In: Chandrabose, A., Furbach, U., Ghosh, A., Kumar, M. A. (eds) Computational Intelligence in Data Science. ICCIDS 2020. IFIP Advances in Information and Communication Technology, vol 578.Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63467-4_15
Saranya, K. C., Thangavelu, A., Chidambaram, A., Arumugam, S., Govindraj, S.: Cyclist detection using tiny YOLO v2. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1057. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0184-5_82
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You Only Look Once: Unified, Real-Time Object Detection. published in http://arxiv.org/abs/2004.10934v1 [cs.CV] 09 May 2016
Redmon, J., Farhadi, A.: “YOLO9000: Better, Faster, Stronger” published in http://arxiv.org/abs/2004.10934v1 [cs.CV] 25 Dec 2016
Redmon, J., Farhadi, A.: “YOLOv3: An Incremental Improvement” published in http://arxiv.org/abs/2004.10934v1 [cs.CV]
Bochkovskiy, A.: “YOLOv4: Optimal Speed and Accuracy of Object Detection” published in http://arxiv.org/abs/2004.10934v1 [cs.CV] 23 Apr 2020
Yang, G., Feng, W., Jin, J., Lei1, Q., Li, X., Gui, G., Wang, W.: Face Mask Recognition System with YOLOV5 Based on Image Recognition. In: 2020 IEEE 6th International Conference on Computer and Communications
Yan, B., Fan, P., Lei, X., Liu, Z., Yang, F.: A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOV5” MDPI Remote Sens. 2021, 13, 1619. https://doi.org/10.3390/rs13091619
Zhang, E., Zhang, Y.: Average Precision. In: LIU L., ÖZSU M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_482(2009)
Towards Data Science- Evaluating Performance of an Object Detection Model https://towardsdatascience.com/evaluating-performance-of-an-object-detection-model-137a349c517b
Saeidi, M., Ahmadi, A.: High-performance and deep pedestrian detection based on estimation of different parts. J. Supercomput. 77, 2033–2068 (2021). https://doi.org/10.1007/s11227-020-03345-4
Brahimi, M., Arsenovic, M., Laraba, S., Sladojevic, S., Boukhalfa, K., Moussaoui, A.:“Deep learning for plant diseases: detection and saliency map visualisation. In: Human and Machine Learning. Eds. J. Zhou and F. Chen (Cham, Switzerland: Springer International Publishing), pp. 93–117 (2018)
Girshick, R. B.: Fast R-CNN. CoRR, http://arxiv.org/abs/1504.08083, (2015)
Redmon, J., Farhadi, A.:Yolo V3: An incremental improvement. http://arxiv.org/abs/1804.02767 [cs], pp. 1–6 (2018)
P Mathew, M., Mahesh, T. Y.: “Leaf Based Disease Detection of Bell Pepper plant Using Yolo V4” Journal of Huazhong University of Science and Technology (ISSN-1671–4512) Volume-50, Issue-5
Funding
No funding was received for conducting this study.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design and both the authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Mathew, M.P., Mahesh, T.Y. Leaf-based disease detection in bell pepper plant using YOLO v5. SIViP 16, 841–847 (2022). https://doi.org/10.1007/s11760-021-02024-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-021-02024-y