Online diagnosis platform for tomato seedling diseases in greenhouse production
Keywords:
pest and disease detection, YOLO, diagnosis platform, k-means clustering, facility production baseAbstract
The facility-based production method is an important stage in the development of modern agriculture, lifting natural light and temperature restrictions and helping to improve agricultural production efficiency. To address the problems of difficulty and low accuracy in detecting pests and diseases in the dense production environment of tomato facilities, an online diagnosis platform for tomato plant diseases based on deep learning and cluster fusion was proposed by collecting images of eight major prevalent pests and diseases during the growing period of tomatoes in a facility-based environment. The diagnostic platform consists of three main parts: pest and disease information detection, clustering and decision-making of detection results, and platform diagnostic display. Firstly, based on the You Only Look Once (YOLO) algorithm, the key information of the disease was extracted by adding attention module (CBAM), multi-scale feature fusion was performed using weighted bi-directional feature pyramid network (BiFPN), and the overall construction was designed to be compressed and lightweight; Secondly, the k-means clustering algorithm is used to fuse with the deep learning results to output pest identification decision values to further improve the accuracy of identification applications; Finally, a detection platform was designed and developed using Python, including the front-end, back-end, and database of the system to realize online diagnosis and interaction of tomato plant pests and diseases. The experiment shows that the algorithm detects tomato plant diseases and insect pests with mAP (mean Average Precision) of 92.7%, weights of 12.8 Megabyte (M), inference time of 33.6 ms. Compared with the current mainstream single-stage detection series algorithms, the improved algorithm model has achieved better performance; The accuracy rate of the platform diagnosis output pests and diseases information of 91.2% for images and 95.2% for videos. It is a great significance to tomato pest control research and the development of smart agriculture. Keywords: pest and disease detection, YOLO, diagnosis platform, k-means clustering, facility production base DOI: 10.25165/j.ijabe.20241701.8433 Citation: Jin X, Zhu X W, Ji J T, Li M Y, Xie X L, Zhao B. Online diagnosis platform for tomato seedling diseases in greenhouse production. Int J Agric & Biol Eng, 2024; 17(1): 80-89.References
[1] Jia Z H, Zhang Y Y, Wang H T, Liang D. Identification method of tomato disease period based on Res2Net and bilinear attention mechanism. Transactions of the CSAM, 2022; 53(7): 259–266.
[2] Osdaghi E, Jones J B, Sharma A, Goss E M, Abrahamian P, Newberry EA, et al. A centenary for bacterial spot of tomato and pepper. Molecular Plant Pathology, 2021; 22(12): 1500–1519.
[3] Hu Z, Zhang Y. Effect of dimensionality reduction and noise reduction on hyperspectral recognition during incubation period of tomato early blight. Spectroscopy and Spectral Analysis, 2023; 43(3): 744–752.
[4] Wu K T, Gevens A J, Silva E M. Exploring grower strategies and needs for enhancing organic disease management of tomato late blight. Renewable Agriculture and Food Systems, 2022; 37(5): 382–398.
[5] Zhao T T, Pei T, Jiang J B, Yang H H, Zhang H, Li J F, et al. Understanding the mechanisms of resistance to tomato leaf mold: A review. Horticultural Plant Journal, 2022; 8: 667–675.
[6] Hu W Y, Hong W, Wang H K, Liu M Z, Liu S. A study on tomato disease and pest detection method. Applied Sciences, 2023; 13(18): 10063.
[7] Mansour A, Al-Banna L, Salem N, Alsmairat N. Disease management of organic tomato under greenhouse conditions in the Jordan Valley. Crop Protection, 2014; 60: 48–55.
[8] Ramírez C C, Gundel P E, Karley A J, Leybourne D J. To tolerate drought or resist aphids? A new challenge to plant science is on the horizon. Journal of Experimental Botany, 2023; 74(6): 1745–1750.
[9] Reddy G, Tangtrakulwanich K. Module of integrated insect pest management on tomato with growers’ participation. Journal of Agricultural Science, 2014; 6(5): 1619–9752.
[10] de Souza Marinke L, de Resende J T V, Hata F T, Dias D M, de Oliveira L V B, Ventura M U. Selection of tomato genotypes with high resistance to Tetranychus evansi mediated by glandular trichomes. Phytoparasitica, 2022; 50(3): 629–643.
[11] Saleem M, ul Hasan M, Sagheer M, Atiq M. Determination of insecticide resistance in Bemisia tabaci (Hemiptera: Aleyrodidae) populations from Punjab, Pakistan. International Journal of Tropical Insect Science, 2021; 41: 1799–1808.
[12] Mateos Fernández R, Petek M, Gerasymenko I, Juteršek M, Baebler Š, Kallam K, et al. Insect pest management in the age of synthetic biology. Plant Biotechnology Journal, 2022; 20(1): 25–36.
[13] Arunnehru J, Vidhyasagar B S, Anwar Basha H. Plant leaf diseases recognition using convolutional neural network and transfer learning. In: International Conference on Communication, Computing and Electronics Systems: Springer, 2020; pp.221–229. doi:10.1007/978-981-15-2612-1_21.
[14] Singh R, Ao N T, Kangjam V, Rajesha G, Banik S. Plant growth promoting microbial consortia against late blight disease of tomato under natural epiphytotic conditions. Indian Phytopathology, 2022; 75(2): 527–539.
[15] Li Y F, Wang H X, Dang L M, Sadeghi-Niaraki A, Moon H. Crop pest recognition in natural scenes using convolutional neural networks. Computers and Electronics in Agriculture, 2020; 169: 105174.
[16] Liu J, Wang X W. Plant diseases and pests detection based on deep learning: a review. Plant Methods, 2021; 17: 22.
[17] Kamilaris A, Prenafeta-Boldú F X. Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 2018; 147: 70–90.
[18] Lu J Z, Tan L J, Jiang H Y. Review on convolutional neural network (CNN) applied to plant leaf disease classification. Agriculture, 2021; 11(8): 707.
[19] Deng F M, Mao W, Zeng Z Q, Zeng H, Wei B Q. Multiple diseases and pests detection based on federated learning and improved faster R-CNN. IEEE Transactions on Instrumentation and Measurement, 2022; 71: 1–11.
[20] Jiao L, Dong S F, Zhang S Y, Xie C J, Wang H Q. AF-RCNN: An anchor-free convolutional neural network for multi-categories agricultural pest detection. Computers and Electronics in Agriculture, 2020; 174: 105522.
[21] Zhang Y, Song C L, Zhang D W. Deep learning-based object detection improvement for tomato disease. IEEE Access, 2020; 8: 56607–56614.
[22] Xie X Y, Ma Y, Liu B, He J R, Li S Q, Wang H Y. A deep-learning-based real-time detector for grape leaf diseases using improved convolutional neural networks. Frontiers in Plant Science, 2020; 11: 751.
[23] Wang J, Yu L Y, Yang J, Dong H. DBA_SSD: A novel end-to-end object detection algorithm applied to plant disease detection. Information, 2021; 12(11): 474.
[24] Sun H N, Xu H W, Liu B, He D J, He J R, Zhang H X, et al. MEAN-SSD: A novel real-time detector for apple leaf diseases using improved light-weight convolutional neural networks. Computers and Electronics in Agriculture, 2021; 189: 106379.
[25] Liu J, Wang X W. Tomato diseases and pests detection based on improved Yolo V3 convolutional neural network. Frontiers in Plant Science, 2020; 11: 898.
[26] Wang X W, Liu J, Zhu X N. Early real-time detection algorithm of tomato diseases and pests in the natural environment. Plant Methods, 2021; 17: 43.
[27] Liu J, Wang X W, Miao W Q, Liu G X. Tomato pests recognition algorithm based on improved YOLOv4. Frontiers in Plant Science, 2022; 13: 814681.
[28] Qi J T, Liu X N, Liu K, Xu F R, Guo H, Tian X L, et al. An improved YOLOv5 model based on visual attention mechanism: Application to recognition of tomato virus disease. Computers and Electronics in Agriculture, 2022; 194: 106780.
[29] Chen Z Y, Wu R H, Lin Y Y, Li C Y, Chen S Y, Yuan Z N, et al. Plant disease recognition model based on improved YOLOv5. Agronomy, 2022; 12(2): 365.
[30] Hughes D P, Salathé M. An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv, 2015; In press. doi: 10.48550/arXiv.1511.08060.
[31] Shi Z J, Shi M Y, Lin W G. The implementation of crawling news page based on incremental web crawler. In: 2016 4th Intl Conf on Applied Computing and Information Technology/3rd Intl Conf on Computational Science/Intelligence and Applied Informatics/1st Intl Conf on Big Data, Cloud Computing, Data Science & Engineering (ACIT-CSII-BCD): IEEE, 2016; pp.348–351. doi: 10.1109/ACIT-CSII-BCD.2016.073.
[32] Zhu J-Y, Park T, Isola P, Efros A A. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision: 2017. 2223–2232. doi: 10.1109/ICCV.2017.244.
[33] Bochkovskiy A, Wang C-Y, Liao H-Y M. Yolov4: Optimal speed and accuracy of object detection. arXiv, 2020; In press. doi: 10.48550/arXiv.2004.10934.
[34] Zhang H Y, Cisse M, Dauphin Y N, Lopez-Paz D. mixup: Beyond empirical risk minimization. arXiv, 2017; In press. doi: 10.48550/arXiv.1710.09412.
[35] Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition: 2016. 779–788. doi: 10.1109/CVPR.2016.91.
[36] Hao S Y, Lee D-H, Zhao D. Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system. Transportation Research Part C:Emerging Technologies, 2019; 107: 287–300.
[37] Woo S, Park J, Lee J-Y, Kweon I S. Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), Munich, Germany: Computer Vision Foundation, 2018; pp.3–19. doi: 10.1007/978-3-030-01234-2_1.
[38] Tan M X, Pang R M, Le Q V. Efficientdet: Scalable and efficient object detection. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA: IEEE, 2020; pp.10778–10787. doi: 10.1109/CVPR42600.2020.01079
[39] Han K, Wang Y H, Tian Q, Guo J Y, Xu C J, Xu C. Ghostnet: More features from cheap operations. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA: IEEE, 2020; 1580–1589. doi: 10.1109/CVPR42600.2020.00165.
[40] Liu H F, Chen J X, Dy J, Fu Y. Transforming complex problems into k-means solutions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023; 45: 9149–9168.
[41] Zhang Y J, Ma B X, Hu Y T, Li C, Li Y J. Accurate cotton diseases and pests detection in complex background based on an improved YOLOX model. Computers and Electronics in Agriculture, 2022; 203: 107484.
[2] Osdaghi E, Jones J B, Sharma A, Goss E M, Abrahamian P, Newberry EA, et al. A centenary for bacterial spot of tomato and pepper. Molecular Plant Pathology, 2021; 22(12): 1500–1519.
[3] Hu Z, Zhang Y. Effect of dimensionality reduction and noise reduction on hyperspectral recognition during incubation period of tomato early blight. Spectroscopy and Spectral Analysis, 2023; 43(3): 744–752.
[4] Wu K T, Gevens A J, Silva E M. Exploring grower strategies and needs for enhancing organic disease management of tomato late blight. Renewable Agriculture and Food Systems, 2022; 37(5): 382–398.
[5] Zhao T T, Pei T, Jiang J B, Yang H H, Zhang H, Li J F, et al. Understanding the mechanisms of resistance to tomato leaf mold: A review. Horticultural Plant Journal, 2022; 8: 667–675.
[6] Hu W Y, Hong W, Wang H K, Liu M Z, Liu S. A study on tomato disease and pest detection method. Applied Sciences, 2023; 13(18): 10063.
[7] Mansour A, Al-Banna L, Salem N, Alsmairat N. Disease management of organic tomato under greenhouse conditions in the Jordan Valley. Crop Protection, 2014; 60: 48–55.
[8] Ramírez C C, Gundel P E, Karley A J, Leybourne D J. To tolerate drought or resist aphids? A new challenge to plant science is on the horizon. Journal of Experimental Botany, 2023; 74(6): 1745–1750.
[9] Reddy G, Tangtrakulwanich K. Module of integrated insect pest management on tomato with growers’ participation. Journal of Agricultural Science, 2014; 6(5): 1619–9752.
[10] de Souza Marinke L, de Resende J T V, Hata F T, Dias D M, de Oliveira L V B, Ventura M U. Selection of tomato genotypes with high resistance to Tetranychus evansi mediated by glandular trichomes. Phytoparasitica, 2022; 50(3): 629–643.
[11] Saleem M, ul Hasan M, Sagheer M, Atiq M. Determination of insecticide resistance in Bemisia tabaci (Hemiptera: Aleyrodidae) populations from Punjab, Pakistan. International Journal of Tropical Insect Science, 2021; 41: 1799–1808.
[12] Mateos Fernández R, Petek M, Gerasymenko I, Juteršek M, Baebler Š, Kallam K, et al. Insect pest management in the age of synthetic biology. Plant Biotechnology Journal, 2022; 20(1): 25–36.
[13] Arunnehru J, Vidhyasagar B S, Anwar Basha H. Plant leaf diseases recognition using convolutional neural network and transfer learning. In: International Conference on Communication, Computing and Electronics Systems: Springer, 2020; pp.221–229. doi:10.1007/978-981-15-2612-1_21.
[14] Singh R, Ao N T, Kangjam V, Rajesha G, Banik S. Plant growth promoting microbial consortia against late blight disease of tomato under natural epiphytotic conditions. Indian Phytopathology, 2022; 75(2): 527–539.
[15] Li Y F, Wang H X, Dang L M, Sadeghi-Niaraki A, Moon H. Crop pest recognition in natural scenes using convolutional neural networks. Computers and Electronics in Agriculture, 2020; 169: 105174.
[16] Liu J, Wang X W. Plant diseases and pests detection based on deep learning: a review. Plant Methods, 2021; 17: 22.
[17] Kamilaris A, Prenafeta-Boldú F X. Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 2018; 147: 70–90.
[18] Lu J Z, Tan L J, Jiang H Y. Review on convolutional neural network (CNN) applied to plant leaf disease classification. Agriculture, 2021; 11(8): 707.
[19] Deng F M, Mao W, Zeng Z Q, Zeng H, Wei B Q. Multiple diseases and pests detection based on federated learning and improved faster R-CNN. IEEE Transactions on Instrumentation and Measurement, 2022; 71: 1–11.
[20] Jiao L, Dong S F, Zhang S Y, Xie C J, Wang H Q. AF-RCNN: An anchor-free convolutional neural network for multi-categories agricultural pest detection. Computers and Electronics in Agriculture, 2020; 174: 105522.
[21] Zhang Y, Song C L, Zhang D W. Deep learning-based object detection improvement for tomato disease. IEEE Access, 2020; 8: 56607–56614.
[22] Xie X Y, Ma Y, Liu B, He J R, Li S Q, Wang H Y. A deep-learning-based real-time detector for grape leaf diseases using improved convolutional neural networks. Frontiers in Plant Science, 2020; 11: 751.
[23] Wang J, Yu L Y, Yang J, Dong H. DBA_SSD: A novel end-to-end object detection algorithm applied to plant disease detection. Information, 2021; 12(11): 474.
[24] Sun H N, Xu H W, Liu B, He D J, He J R, Zhang H X, et al. MEAN-SSD: A novel real-time detector for apple leaf diseases using improved light-weight convolutional neural networks. Computers and Electronics in Agriculture, 2021; 189: 106379.
[25] Liu J, Wang X W. Tomato diseases and pests detection based on improved Yolo V3 convolutional neural network. Frontiers in Plant Science, 2020; 11: 898.
[26] Wang X W, Liu J, Zhu X N. Early real-time detection algorithm of tomato diseases and pests in the natural environment. Plant Methods, 2021; 17: 43.
[27] Liu J, Wang X W, Miao W Q, Liu G X. Tomato pests recognition algorithm based on improved YOLOv4. Frontiers in Plant Science, 2022; 13: 814681.
[28] Qi J T, Liu X N, Liu K, Xu F R, Guo H, Tian X L, et al. An improved YOLOv5 model based on visual attention mechanism: Application to recognition of tomato virus disease. Computers and Electronics in Agriculture, 2022; 194: 106780.
[29] Chen Z Y, Wu R H, Lin Y Y, Li C Y, Chen S Y, Yuan Z N, et al. Plant disease recognition model based on improved YOLOv5. Agronomy, 2022; 12(2): 365.
[30] Hughes D P, Salathé M. An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv, 2015; In press. doi: 10.48550/arXiv.1511.08060.
[31] Shi Z J, Shi M Y, Lin W G. The implementation of crawling news page based on incremental web crawler. In: 2016 4th Intl Conf on Applied Computing and Information Technology/3rd Intl Conf on Computational Science/Intelligence and Applied Informatics/1st Intl Conf on Big Data, Cloud Computing, Data Science & Engineering (ACIT-CSII-BCD): IEEE, 2016; pp.348–351. doi: 10.1109/ACIT-CSII-BCD.2016.073.
[32] Zhu J-Y, Park T, Isola P, Efros A A. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision: 2017. 2223–2232. doi: 10.1109/ICCV.2017.244.
[33] Bochkovskiy A, Wang C-Y, Liao H-Y M. Yolov4: Optimal speed and accuracy of object detection. arXiv, 2020; In press. doi: 10.48550/arXiv.2004.10934.
[34] Zhang H Y, Cisse M, Dauphin Y N, Lopez-Paz D. mixup: Beyond empirical risk minimization. arXiv, 2017; In press. doi: 10.48550/arXiv.1710.09412.
[35] Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition: 2016. 779–788. doi: 10.1109/CVPR.2016.91.
[36] Hao S Y, Lee D-H, Zhao D. Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system. Transportation Research Part C:Emerging Technologies, 2019; 107: 287–300.
[37] Woo S, Park J, Lee J-Y, Kweon I S. Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), Munich, Germany: Computer Vision Foundation, 2018; pp.3–19. doi: 10.1007/978-3-030-01234-2_1.
[38] Tan M X, Pang R M, Le Q V. Efficientdet: Scalable and efficient object detection. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA: IEEE, 2020; pp.10778–10787. doi: 10.1109/CVPR42600.2020.01079
[39] Han K, Wang Y H, Tian Q, Guo J Y, Xu C J, Xu C. Ghostnet: More features from cheap operations. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA: IEEE, 2020; 1580–1589. doi: 10.1109/CVPR42600.2020.00165.
[40] Liu H F, Chen J X, Dy J, Fu Y. Transforming complex problems into k-means solutions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023; 45: 9149–9168.
[41] Zhang Y J, Ma B X, Hu Y T, Li C, Li Y J. Accurate cotton diseases and pests detection in complex background based on an improved YOLOX model. Computers and Electronics in Agriculture, 2022; 203: 107484.
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Published
2024-03-31
How to Cite
Jin, X., Zhu, X., Ji, J., Li, M., Xie, X., & Zhao, B. (2024). Online diagnosis platform for tomato seedling diseases in greenhouse production. International Journal of Agricultural and Biological Engineering, 17(1), 80–89. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/8433
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Animal, Plant and Facility Systems
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