DOI : https://doi.org/10.18517/ijaseit.13.4.19018

Deep Learning Model for Identification of Diseases on Strawberry (Fragaria sp.) Plants

Setyo Pertiwi (1), Dandi Handoko Wibowo (2), Slamet Widodo (3)
(1) Department of Mechanical and Biosystem Engineering, Faculty of Agricultural Engineering and Technology, IPB University, Indonesia
(2) Agricultural and Biosystem Engineering Study Program, Faculty of Agricultural Engineering and Technology, IPB University, Indonesia
(3) Department of Mechanical and Biosystem Engineering, Faculty of Agricultural Engineering and Technology, IPB University, Indonesia
Fulltext View | Download
How to cite (IJASEIT) :
Pertiwi, Setyo, et al. “Deep Learning Model for Identification of Diseases on Strawberry (Fragaria sp.) Plants”. International Journal on Advanced Science, Engineering and Information Technology, vol. 13, no. 4, Aug. 2023, pp. 1342-8, doi:10.18517/ijaseit.13.4.19018.
Citation Format :
Plant diseases can significantly affect crop productivity if not effectively managed. Accurate disease identification is critical for disease control and yield enhancement. Addressing these concerns, the potential application of deep learning techniques for plant disease identification is promising in Indonesia. This research aims to formulate a deep learning model tailored to detect strawberry (Fragaria sp.) plant diseases. The study encompasses several key phases, including: (1) collecting datasets, (2) preprocessing datasets, (3) annotating datasets, (4) configuring and training deep learning models, and (5) validating and evaluating the model. The developed model employs YOLOv7 and YOLOv7-X algorithms, utilizing a dataset of 7337 instances across three disease categories: tip burn, leaf scorch, and anthracnose. These datasets were obtained from publicly accessible repositories. The evaluation of the deep learning model's performance in detecting plant diseases involved using 717 in-field plant images. The outcomes of the evaluation, employing YOLOv7 and YOLOv7-X algorithms, demonstrated accuracy rates of 92.5% and 92.3%, precision levels of 94.5% and 95.1%, and recall values of 90.5% and 89.6%, respectively. These results emphasize the effectiveness of the deep learning model in accurately and precisely identifying diseases in strawberry plants.

J. A. Diaz-Pendon, M. C. Canizares, E. Moriones, E.R. Bejarano, H. Czosnek, J. Navas-Castillo, “Tomato yellow leaf curl viruses: Menage a trois between the virus complex, the plant and whitefly vector,” Mol. Plant Pathol., vol. 11, no. 4, pp 414-450, 2010, doi:10.1111/j.1364-3703.2010.00618.X.

R. L. Gilbertson, O. Batuman, “Emerging viral and other diseases of processing tomatoes: biology diagnosis and management,” Acta Hortic. no.1, pp. 35-48, 2013, doi:10.17660/ActaHortic.2013.971.2.

J. Liu and X. Wang, “Tomato diseases and pests detection based on improved Yolo V3 convolutional neural network,” Front. Plant Sci., vol. 11, pp. 898, 2020, doi:10.3389/fpls.2020.00898.

J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Netw., vol. 61, pp. 85-117, 2015.

A. Kamilaris and F. X. Prenafeta-Boldíº, “Deep Learning in Agriculture: A Survey,” Comput. Electron. Agric., vol. 147, pp. 70-90, doi:10.1016/j.compag.2018.02.016.

X. Song, G. Zhang, F. Liu, et. al., “Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model,” J. Arid Land., vol. 8, pp. 734-748, 2016, doi:10.1007/s40333-016-0049-0.

S. Arivazhagan, R. N. Shebiah, S. N. Ananthi, and S. V. Varthini, “Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features,” Agric. Eng. Int.: CIGR J vol. 15, no. 1, pp. 211-217, 2013.

V. Singh, and A. K. Misra, “Detection of plant leaf diseases using image segmentation and soft computing techniques,” Inf. Process. Agric., vol. 4, no. 1, pp. 41-49, 2017, doi:10.1016/j.inpa.2016.10.005.

S. P. Mohanty, D .P. Hughes, and M. Salathí©, “Using deep learning for image-based plant disease detection,” Front. Plant Sci., vol. 7, pp. 1419, 2016.

S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, D., and D. Stefanovic, “Deep neural networks based recognition of plant diseases by leaf image classification,” Comput. Intell. Neurosci, vol. 2016, pp 3289801, 2016.

K. P. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Comput. Electron. Agric., vol. 145, pp. 311-318, 2018, doi: 10.1016/j.compag.2018.01.009.

V. Tiwari, R.C. Joshi, and M. K. Dutta, “Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images,” Ecol. Inform., vol. 63, pp. 101289, 2021.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” In Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 779-788, 2016, doi:10.1109/CVPR.2016.91.

H. Li, C. Li, G. Li, and L. Chen, “A real-time table grape detection method based on improved YOLOv4-tiny network in complex background,” Biosyst. Eng., Vol 212, pp. 347-359, 2021, doi:10.1016/j.biosystemseng.2021.11.011.

P. Zhang and D. Li, D, “EPSA-YOLO-V5s: A novel method for detecting the survival rate of rapeseed in a plant factory based on multiple guarantee mechanisms,’ Comput. Electron. Agric., vol. 193, pp. 106714, 2022, doi:10.1016/j.compag.2022.106714.

M. P. Mathew, and T. Y. Mahesh, “Leaf-based disease detection in bell pepper plant using YOLO v5,” SIViP, vol. 16, pp. 841-847, 2022, doi:10.1007/s11760-021-02024-y.

M. J. A. Soeb, M. F. Jubayer, T. A. Tarin, et al., “Tea leaf disease detection and identification based on YOLOv7 (YOLO-T). Sci. Rep.. vol. 13, pp. 6078, 2023, doi:10.1038/s41598-023-33270-4.

M. Hariri, and E. AvÅŸar, “Tipburn disorder detection in strawberry leaves using convolutional neural networks and particle swarm optimization,” Multimed. Tools. Appl., Vol. 81, no. 8, pp. 11795-11822, 2022, doi:10.1007/s11042-022-12759-6.

D. Hughes, and M. Salathí©, “An open access repository of images on plant health to enable the development of mobile disease diagnostics”, arXiv preprint arXiv:1511.08060, 2015.

U. Afzaal, B. Bhattarai, Y.R. Pandeya, and J. Lee, “An Instance Segmentation Model for Strawberry Diseases Based on Mask R-CNN,” Sensors, vol. 21, pp. 6565, 2021.

E.F. Cox, J. M. T. McKee, “A Comparison of Tipburn Susceptibility in Lettuce Under Field and Glasshouse Conditions,” J. Hortic. Sci. vol. 51, pp. 117-122, 1976.

M. Kroggel, and C. Kubota, “Controlled environment strategies for tipburn management in greenhouse strawberry production,” In VIII International Strawberry Symposium vol.1156, pp. 529-536, August 2016.

G. N. Agrios, “Plant Pathology”, 5th Ed., San Diego California, Academic Press. I. N. 635 p, 2005.

B. J. Smith, “Epidemiology and pathology of strawberry anthracnose: a North American perspective,” HortScience, vol. 43, no. 1, pp. 69-73, 2008.

C. Y. Wang, A. Bochkovskiy, and H. Y. M. Liao, “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” In Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 7464-7475, 2022, doi:10.48550/arXiv.2207.02696.

D. Rosell, J. Matthews, and N. Talagala, “Managing bias in AI,” In Companion Proc. of 2019 TheWebConf., pp. 539-544, May 2019.

I. Model and L. Shamir, “Comparison of Data Set Bias in Object Recognition Benchmarks”, IEEE Access, vol. 3, no. 1, pp. 1953-1962, 2015.

M. H. Hamidon and T. Ahamed, “Detection of Tip-Burn Stress on Lettuce Grown in an Indoor Environment Using Deep Learning Algorithms.” Sensors, vol. 22, pp. 7251, 2022, doi:10.3390/s22197251.

I. Abbas, J. Liu, M. Amin, A. Tariq, and M. H. Tunio, “Strawberry fungal leaf scorch disease identification in real-time strawberry field using deep learning architectures,” Plants, vol. 10, no. 12, pp. 2643, 2021.

O. Natan, A. I. Gunawan, and B. S. B. Dewantara, “Grid SVM: Machine Learning Applications in Aquaculture Data Processing,” (in bahasa), Jurnal Rekayasa Elektrika vol. 15, no. 1, pp. 7-17, 2019, doi:10.17529/jre.v15i1.13298.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).