Abstract
A major challenge in image classification tasks using Machine Learning, and in particular when using deep neural networks, is domain shifting in deployment. This happens when images during usage are capture in different conditions from those used during training. In this paper, we show that despite previous works on the diagnosis of apple tree diseases using standard Convolutional Neural Networks displaying high accuracy, they do so only when no domain shift is present. When the trained model is asked to classify photos of apples taken in the wild, a 22% reduction in F1 score is observed. We propose to treat the task as a segmentation problem and test two different approaches, showing that using Mask R-CNN allows not only to improve performance in the original domain by 3%, but also significantly reduce losses in the new domain (only 6% reduction in F1 score). We establish segmentation as an important alternative towards improving diagnosis of apple tree diseases from photos.
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Notes
- 1.
The dataset will be made available after publication.
- 2.
The source code for the Mask R-CNN model will be made available after publication.
References
Abd El-aziz, A.A., Darwish, A., Oliva, D., Hassanien, A.E.: Machine learning for apple fruit diseases classification system. In: Hassanien, A.-E., Azar, A.T., Gaber, T., Oliva, D., Tolba, F.M. (eds.) AICV 2020. AISC, vol. 1153, pp. 16–25. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44289-7_2
Abdulla, W.: Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow (2017). https://github.com/matterport/Mask_RCNN
Agarwal, A., Sarkar, A., Dubey, A.K.: Computer vision-based fruit disease detection and classification. In: Tiwari, S., Trivedi, M.C., Mishra, K.K., Misra, A.K., Kumar, K.K. (eds.) Smart Innovations in Communication and Computational Sciences. AISC, vol. 851, pp. 105–115. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-2414-7_11
Allehaibi, K.H.S., Nugroho, L.E., Lazuardi, L., Prabuwono, A.S., Mantoro, T., et al.: Segmentation and classification of cervical cells using deep learning. IEEE Access 7, 116925–116941 (2019)
Anantharaman, R., Velazquez, M., Lee, Y.: Utilizing mask R-CNN for detection and segmentation of oral diseases. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2197–2204. IEEE (2018)
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)
Ballester, P., Correa, U.B., Birck, M., Araujo, R.: Assessing the performance of convolutional neural networks on classifying disorders in apple tree leaves. In: Barone, D.A.C., Teles, E.O., Brackmann, C.P. (eds.) LAWCN 2017. CCIS, vol. 720, pp. 31–38. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71011-2_3
Ballester, P., Correa, U.B., Araujo, R.M.: Lateral representation learning in convolutional neural networks. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2018)
Dewliya, S., Singh, M.P.: Detection and classification for apple fruit diseases using support vector machine and chain code. Int. Res. J. Eng. Technol. (IRJET) 2(04), 2097–2104 (2015)
Dias, P.A., Tabb, A., Medeiros, H.: Multispecies fruit flower detection using a refined semantic segmentation network. IEEE Robot. Autom. Lett. 3(4), 3003–3010 (2018)
Dias, P.A., Medeiros, H.: Semantic segmentation refinement by Monte Carlo region growing of high confidence detections. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11362, pp. 131–146. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20890-5_9
Dutta, A., Zisserman, A.: The VGG image annotator (VIA). arXiv preprint arXiv:1904.10699 (2019)
Fan, S., et al.: On line detection of defective apples using computer vision system combined with deep learning methods. J. Food Eng. 286, 110102 (2020)
Gonzalez, S., Arellano, C., Tapia, J.E.: Deepblueberry: quantification of blueberries in the wild using instance segmentation. IEEE Access 7, 105776–105788 (2019)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
Lampert, C.H., Nickisch, H., Harmeling, S.: Learning to detect unseen object classes by between-class attribute transfer. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 951–958. IEEE (2009)
Lateef, F., Ruichek, Y.: Survey on semantic segmentation using deep learning techniques. Neurocomputing 338, 321–348 (2019)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Nachtigall, L.G., Araujo, R.M., Nachtigall, G.R.: Use of images of leaves and fruits of apple trees for automatic identification of symptoms of diseases and nutritional disorders. Int. J. Monit. Surveill. Technol. Res. (IJMSTR) 5(2), 1–14 (2017)
Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1717–1724 (2014)
Ramcharan, A., et al.: Assessing a mobile-based deep learning model for plant disease surveillance. arXiv preprint arXiv:1805.08692 (2018)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Shi, J., Zhou, Y., Zhang, W.X.Q.: Target detection based on improved mask RCNN in service robot. In: 2019 Chinese Control Conference (CCC), pp. 8519–8524. IEEE (2019)
Sujatha, P.K., Sandhya, J., Chaitanya, J.S., Subashini, R.: Enhancement of segmentation and feature fusion for apple disease classification. In: 2018 Tenth International Conference on Advanced Computing (ICoAC), pp. 175–181. IEEE (2018)
Wang, M., Deng, W.: Deep visual domain adaptation: a survey. Neurocomputing 312, 135–153 (2018)
Acknowledgment
This work was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq (Edital Universal 407780/2016-5) and by the Coordenaçño de Aperfeiçoamento de Pessoal de Nível Superior - CAPES (Finance Code 001). We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPU used for this research.
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de Melo, R.F. et al. (2020). Diagnosis of Apple Fruit Diseases in the Wild with Mask R-CNN. In: Cerri, R., Prati, R.C. (eds) Intelligent Systems. BRACIS 2020. Lecture Notes in Computer Science(), vol 12319. Springer, Cham. https://doi.org/10.1007/978-3-030-61377-8_18
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