Disease Segmentation in Purple Sweet Potato Images Using Yolov7
Main Article Content
Abstract
Purple sweet potato is a very important plant in many parts of the world and a major crop in tropical and subtropical climates. Its cultivation can significantly increase production and consumption, and it is beneficial for the nutritional status of people in both rural and urban areas. However, purple sweet potatoes are susceptible to disease outbreaks, which can cause substantial losses to the agricultural industry. To prevent the spread of these diseases and minimize financial losses, it is crucial for farmers to identify purple sweet potato diseases as early as possible. Utilizing deep learning technology to separate areas of purple sweet potatoes marked with disease can effectively address this problem. In this study, researchers employed a segmentation method using the YOLOv7 algorithm. The study's results demonstrated a mean Average Precision (mAP) value of 98.6% from a dataset of 1500 images, divided into two classes: healthy sweet potatoes and diseased sweet potatoes with tuber rot. The mAP value for healthy sweet potatoes was 96.1%, while the mAP for diseased sweet potatoes with tuber rot was 98.6%. The YOLOv7 method, therefore, produces high accuracy values for the segmentation of purple sweet potato diseases. This research significantly contributes to agriculture by enhancing the productivity and quality of sweet potato harvests and can assist farmers in improving the efficiency and sustainability of purple sweet potato production.
Downloads
Article Details
Copyright (c) 2024 khusniyatul latifah, Aviv Yuniar Rahman, Istiadi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
M. K. Alam, “A comprehensive review of sweet potato (Ipomoea batatas [L.] Lam): Revisiting the associated health benefits,” Trends in Food Science and Technology, vol. 115, no. April, pp. 512–529, 2021, doi: 10.1016/j.tifs.2021.07.001.
S. Behera et al., “Biology and biotechnological aspect of sweet potato (Ipomoea batatas L.): a commercially important tuber crop,” Planta, vol. 256, no. 2, 2022, doi: 10.1007/s00425-022-03938-8.
A. Li et al., “Research advances of purple sweet potato anthocyanins: Extraction, identification, stability, bioactivity, application, and biotransformation,” Molecules, vol. 24, no. 21, 2019, doi: 10.3390/molecules24213816.
R. A. Pratiwi, “Pengolahan Ubi Jalar Menjadi Aneka Olahan Makanan : Review,” Jurnal Triton, vol. 11, no. 2, pp. 42–50, 2020, doi: 10.47687/jt.v11i2.112.
K. Zhang et al., “The spread and transmission of sweet potato virus disease (SPVD) and its effect on the gene expression profile in sweet potato,” Plants, vol. 9, no. 4, pp. 1–21, 2020, doi: 10.3390/plants9040492.
A. Ahmad, D. Saraswat, and A. El Gamal, “A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools,” Smart Agricultural Technology, vol. 3, no. June 2022, p. 100083, 2023, doi: 10.1016/j.atech.2022.100083.
M. M. Ali, N. A. Bachik, N. A. Muhadi, T. Norizan, and T. Yusof, “Physiological and Molecular Plant Pathology Non-destructive techniques of detecting plant diseases : A review,” Physiological and Molecular Plant Pathology, vol. 108, no. July, p. 101426, 2019, doi: 10.1016/j.pmpp.2019.101426.
K. Anwar and S. Setyowibowo, “Segmentasi Kerusakan Daun Padi pada Citra Digital,” Jurnal Edukasi dan Penelitian Informatika (JEPIN), vol. 7, no. 1, p. 39, 2021, doi: 10.26418/jp.v7i1.42331.
C. Zuo et al., Deep learning in optical metrology: a review, vol. 11, no. 1. Springer US, 2022. doi: 10.1038/s41377-022-00714-x.
N. O’Mahony et al., “Deep Learning vs. Traditional Computer Vision,” Advances in Intelligent Systems and Computing, vol. 943, no. Cv, pp. 128–144, 2020, doi: 10.1007/978-3-030-17795-9_10.
J. Naskath, G. Sivakamasundari, and A. A. S. Begum, “A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN,” Wireless Personal Communications, vol. 128, no. 4, pp. 2913–2936, 2023, doi: 10.1007/s11277-022-10079-4.
S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, and D. Terzopoulos, “Image Segmentation Using Deep Learning: A Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 7, pp. 3523–3542, 2022, doi: 10.1109/TPAMI.2021.3059968.
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 Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2023, pp. 7464–7475.
S. Wang et al., “Annotation-efficient deep learning for automatic medical image segmentation,” Nature Communications, vol. 12, no. 1, pp. 1–13, 2021, doi: 10.1038/s41467-021-26216-9.
H. Liu and B. Lang, “Machine learning and deep learning methods for intrusion detection systems: A survey,” Applied Sciences (Switzerland), vol. 9, no. 20, 2019, doi: 10.3390/app9204396.
R. Bharti, A. Khamparia, M. Shabaz, G. Dhiman, S. Pande, and P. Singh, “Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning,” Computational Intelligence and Neuroscience, vol. 2021, 2021, doi: 10.1155/2021/8387680.
D. I. Mercurio and A. A. Hernandez, “Classification of sweet potato variety using convolutional neural network,” 2019 IEEE 9th International Conference on System Engineering and Technology, ICSET 2019 - Proceeding, vol. 6, pp. 120–125, 2019, doi: 10.1109/ICSEngT.2019.8906329.
S. Lestari, A. Y. Rahman, and Istiadi, “Quality Detection of Export Purple Sweet Potatoes Using Yolov4-Tiny,” International Seminar on Intelligent Technology and Its Applications (ISITIA), 2023, doi: 10.1109/ISITIA59021.2023.10221110.
R. I. Rovita, A. Y. Rahman, and Istiadi, “Fertilizer Quality Detection For Purple Sweet Potato Plants Using YOLOv4-Tiny,” International Seminar on Application for Technology of Information and Communication (iSemantic). IEEE, 2023, doi: 10.1109/iSemantic59612.2023.10295298.
R. Silvia, A. Y. Rahman, and Istiadi, “Quality Detection of Sweet Potato Leaves Using,” International Seminar on Intelligent Technology and Its Applications (ISEMATIC), 2023, doi: 10.1109/iSemantic59612.2023.10295332.
M. Islam, K. N. Khan, and M. S. Khan, “Evaluation of Preprocessing Techniques for U-Net Based Automated Liver Segmentation,” 2021 International Conference on Artificial Intelligence, ICAI 2021, pp. 187–192, 2021, doi: 10.1109/ICAI52203.2021.9445204.
A. Barriuso and A. Torralba, “Notes on image annotation,” arXiv preprint arXiv:1210.3448, 2012.
K. Sharma, S. S. Rawat, D. Parashar, S. Sharma, S. Roy, and S. Sahoo, “State of-the-Art Analysis of Multiple Object Detection Techniques using Deep Learning,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 6, pp. 527–534, 2023, doi: 10.14569/IJACSA.2023.0140657.
L. Cao, X. Zheng, and L. Fang, “The Semantic Segmentation of Standing Tree Images Based on the Yolo V7 Deep Learning Algorithm,” Electronics (Switzerland), vol. 12, no. 4, 2023, doi: 10.3390/electronics12040929.
H. H. Al Asyhar, S. A. Wibowo, and G. Budiman, “Implementasi dan analisis performansi metode you only look once (yolo) sebagai sensor pornografi pada video,” eProceedings of Engineering, vol. 7, no. 2, 2020.