https://doi.org/10.4081/jae.2024.1585
Potato powdery scab segmentation using improved GrabCut algorithm
Published: 9 May 2024
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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Potato powdery scab is a serious disease that affects potato yield and has widespread global impacts. Due to its concealed symptoms, it is difficult to detect and control the disease once lesions appear. This paper aims to overcome the drawbacks of interactive algorithms and proposes an optimized approach using object detection for the GrabCut algorithm. We design a YOLOv7-guided non-interactive GrabCut algorithm and combine it with image denoising techniques, considering the characteristics of potato powdery scab lesions. We successfully achieve effective segmentation of potato powdery scab lesions. Through experiments, the improved segmentation algorithm has an average accuracy of 88.05%, and the highest accuracy can reach 91.07%. This is an increase of 46.28% and 32.69% respectively compared to the relatively accurate K-means algorithm. Moreover, compared to the original algorithm which could not segment the lesions independently, the improvement is more significant. The experimental results indicate that the algorithm has a high segmentation accuracy, which provides strong support for further disease analysis and control.
Wu, F., Duan, J., Ai, P., Chen, Z., Yang, Z., & Zou, X. 2022. Rachis detection and three-dimensional localization of cut off point for vision-based banana robot.Comput.Electron.Agr. 198, 107079.
DOI: https://doi.org/10.1016/j.compag.2022.107079
Wu, F., Yang, Z., Mo, X., Wu, Z., Tang, W., Duan, J., & Zou, X. 2023. Detection and counting of banana bunches by integrating deep learning and classic image-processing algorithms. Comput.Electron.AGR. 209, 107827.
DOI: https://doi.org/10.1016/j.compag.2023.107827
Tang, Y., Qiu, J., Zhang, Y., Wu, D., Cao, Y., Zhao, K., & Zhu, L. 2023. Optimization strategies of fruit detection to overcome the challenge of unstructured background in field orchard environment: A review. Precis.Agric. 1-37.
DOI: https://doi.org/10.1007/s11119-023-10009-9
Arshaghi, A., Ashourian, M., & Ghabeli, L. 2023. Potato diseases detection and classification using deep learning methods. Multimed.Tools.Appl. 82(4), 5725-5742.
DOI: https://doi.org/10.1007/s11042-022-13390-1
Tang, Y., Zhou, H., Wang, H., & Zhang, Y. 2023. Fruit detection and positioning technology for a Camellia oleifera C. Abel orchard based on improved YOLOv4-tiny model and binocular stereo vision. Expert.Syst.Appl. 211, 118573.
DOI: https://doi.org/10.1016/j.eswa.2022.118573
Zhou, Y., Tang, Y., Zou, X., Wu, M., Tang, W., Meng, F., ... & Kang, H. 2022. Adaptive active positioning of Camellia oleifera fruit picking points: Classical image processing and YOLOv7 fusion algorithm. Applied Sciences. 12(24), 12959.
DOI: https://doi.org/10.3390/app122412959
Soltani Firouz, M., & Sardari, H. 2022. Defect detection in fruit and vegetables by using machine vision systems and image processing. Food Eng Rev. 14(3), 353-379.
DOI: https://doi.org/10.1007/s12393-022-09307-1
MacEachern, C. B., Esau, T. J., Schumann, A. W., Hennessy, P. J., & Zaman, Q. U. 2023. Detection of fruit maturity stage and yield estimation in wild blueberry using deep learning convolutional neural networks. Smart Agric. Technol. 3, 100099.
DOI: https://doi.org/10.1016/j.atech.2022.100099
Loddo, A., Loddo, M., & Di Ruberto, C. 2021. A novel deep learning based approach for seed image classification and retrieval. Comput Electron Agr. 187, 106269.
DOI: https://doi.org/10.1016/j.compag.2021.106269
Li, C., Tang, Y., Zou, X., Zhang, P., Lin, J., Lian, G., Pan, Y., 2022. A novel agricultural machinery intelligent design system based on integrating image processing and knowledge reasoning. Appl. Sci. 12, 7900.
DOI: https://doi.org/10.3390/app12157900
Majeed, Y., Zhang, J., Zhang, X., Fu, L., Karkee, M., Zhang, Q., Whiting, M.D., 2020. Deep learning based segmentation for automated training of apple trees on trellis wires. Comput. Electron. Agric. 170, 105277.
DOI: https://doi.org/10.1016/j.compag.2020.105277
Harrison, J. G., Searle, R. J., & Williams, N. A. 1997. Powdery scab disease of potato-a review. Plant Pathol. 46(1), 1-25.
DOI: https://doi.org/10.1046/j.1365-3059.1997.d01-214.x
Zhao B., Liu X,. Feng J.W,. Chen J.S., Qi P., Zhang K.H., Yang X., Li G.J., Yang L., He P.G, Lu C.L., Yang Y.L. 2021. Occurrence and damage of potato powdery scab caused by Spongosporasubterranea f.sp. subterranea in Yunnan province. Plant Protection. 47(02), 200-206.
Liu X., Yang Y.L.,Luo W.F. 2007.A study on the pathogen of potato powdery scab in Yunnan. Plant Protection. 2007(01):105-108.
Johnson, J., Sharma, G., Srinivasan, S., Masakapalli, S. K., Sharma, S., Sharma, J., & Dua, V. K. 2021. Enhanced field-based detection of potato blight in complex backgrounds using deep learning. Plant Phenomics. 2021.
DOI: https://doi.org/10.34133/2021/9835724
Afzaal, H., Farooque, A. A., Schumann, A. W., Hussain, N., McKenzie-Gopsill, A., Esau, T., Abbas F., & Acharya, B. 2021. Detection of a potato disease (early blight) using artificial intelligence. Remote Sensing. 13(3), 411.
DOI: https://doi.org/10.3390/rs13030411
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., ... & Girshick, R. 2023. Segment anything. arXiv preprint arXiv:2304.02643.
DOI: https://doi.org/10.1109/ICCV51070.2023.00371
Shahinfar, S., Meek, P., & Falzon, G. 2020. “How many images do I need?” Understanding how sample size per class affects deep learning model performance metrics for balanced designs in autonomous wildlife monitoring. Ecol.Inform. 57, 101085.
DOI: https://doi.org/10.1016/j.ecoinf.2020.101085
Singh, V., & Misra, A. K. 2017. Detection of plant leaf diseases using image segmentation and soft computing techniques. Information processing in Agriculture. 4(1), 41-49.
DOI: https://doi.org/10.1016/j.inpa.2016.10.005
Gu B.,Deng L.L.,Li W.,Lv B. 2019. Research on maize disease image recognition method based on GrabCut algorithms. Journal of Chinese Agricultural Mechanization. 2019,40(11):143-149.
Li G., Cao S.Y., Qian T.T., Lu S.L. 2021. Image segmentation of cucumber plants based on improved GrabCut algorithm. Journal of Chinese Agricultural Mechanization. 2021,42(03):159-165.
Liang Y.L., Shuang W., Liu X.L., Li F.G. 2018.The GrabCut Algorithm for the Automatic Segmentation of Target Leaves under the Complex Background. Journal of South China Normal University(Natural Science Edition). 2018,50(06):112-118.
Wang, C. Y., Bochkovskiy, A., & Liao, H. Y. M. 2023. 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 (pp. 7464-7475).
DOI: https://doi.org/10.1109/CVPR52729.2023.00721
Rother, C., Kolmogorov, V., & Blake, A. 2004. " GrabCut" interactive foreground extraction using iterated graph cuts. ACM.T.Graphic. 23(3), 309-314.
DOI: https://doi.org/10.1145/1015706.1015720
Durmuş H., Güneş E.O., Kırcı M. 2017. Disease detection on the leaves of the tomato plants by using deep learning. In: 2017 6th International Conference on Agro-Geoinformatics. Fairfax, VA. pp. 1-5. IEEE.
DOI: https://doi.org/10.1109/Agro-Geoinformatics.2017.8047016
Masood S.Z., Shu G., Dehghan A., Ortiz, E.G. 2017. License plate detection and recognition using deeply learned convolutional neural networks. arXiv preprint arXiv:1703.07330.
Lu, Y., & Young, S. 2020. A survey of public datasets for computer vision tasks in precision agriculture. Comput.Electron.Agr. 178, 105760.
DOI: https://doi.org/10.1016/j.compag.2020.105760
Mavridou, E., Vrochidou, E., Papakostas, G. A., Pachidis, T., & Kaburlasos, V. G. 2019. Machine vision systems in precision agriculture for crop farming. Journal of Imaging. 5(12), 89.
DOI: https://doi.org/10.3390/jimaging5120089
Duarte-Carvajalino, J. M., Alzate, D. F., Ramirez, A. A., Santa-Sepulveda, J. D., Fajardo-Rojas, A. E., & Soto-Suárez, M. 2018. Evaluating late blight severity in potato crops using unmanned aerial vehicles and machine learning algorithms. Remote Sensing. 10(10), 1513.
DOI: https://doi.org/10.3390/rs10101513
Suarez Baron, M. J., Gomez, A. L., & Diaz, J. E. E. 2022. Supervised Learning-Based Image Classification for the Detection of Late Blight in Potato Crops. Applied Sciences. 12(18), 9371.
DOI: https://doi.org/10.3390/app12189371
Bangari, S., Rachana, P., Gupta, N., Sudi, P. S., & Baniya, K. K. 2022. A survey on disease detection of a potato leaf using cnn. In 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS) (pp. 144-149). IEEE.
DOI: https://doi.org/10.1109/ICAIS53314.2022.9742963
Amrani, M., Bey, A., & Amamra, A. 2022. New SAR target recognition based on YOLO and very deep multi-canonical correlation analysis. Int.J.Remote.Sens. 43(15-16), 5800-5819.
DOI: https://doi.org/10.1080/01431161.2021.1953719
Tian, K., Zeng, J., Song, T., Li, Z., Evans, A., & Li, J. 2023. Tomato leaf diseases recognition based on deep convolutional neural networks. Journal of Agricultural Engineering. 54(1).
DOI: https://doi.org/10.4081/jae.2022.1432
Ghimire, D., Kil, D., & Kim, S. H. 2022. A survey on efficient convolutional neural networks and hardware acceleration. Electronics. 11(6), 945.
DOI: https://doi.org/10.3390/electronics11060945
Samanta, D., Chaudhury, P. P., & Ghosh, A. 2012. Scab diseases detection of potato using image processing. International Journal of Computer Trends and Technology. 3(1), 109-113.
How to Cite
Liu, R. (2024) “Potato powdery scab segmentation using improved GrabCut algorithm”, Journal of Agricultural Engineering, 55(3). doi: 10.4081/jae.2024.1585.
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