Abstract
Plant diseases are a considerable threat in the agriculture sector and therefore, early detection and diagnosis of these diseases are very essential. In this paper, we have proposed a deep learning approach for plant leaf disease detection that utilizes momentum residual neural network to optimized the learning space. Further, the feature is tuned to additive angular margin to get the prime leaf disease discriminative representation. The proposed model has been extensively trained and tested on two publicly available Tomato and PlantVillage leaf disease datasets and achieved a top-1 accuracy of 99.51% and 97.16%, respectively. The proposed approach shows its superiority over the existing methodologies and sets a new benchmark for these two considered datasets.
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PyTorch [online]: https://pytorch.org/.
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References
Abayomi-Alli, O.O., Damaševičius, R., Misra, S., Maskeliūnas, R.: Cassava disease recognition from low-quality images using enhanced data augmentation model and deep learning. Expert Syst. 38, e12746 (2021)
Abbas, A., Jain, S., Gour, M., Vankudothu, S.: Tomato plant disease detection using transfer learning with C-GAN synthetic images. CEA 187, 106279 (2021)
Agarwal, M., Singh, A., Arjaria, S., Sinha, A., Gupta, S.: ToLeD: tomato leaf disease detection using convolution neural network. Procedia Comput. Sci. 167, 293–301 (2020)
Akila, M., Deepan, P.: Detection and classification of plant leaf diseases by using deep learning algorithm. IJERT 6(07), 1–5 (2018)
Altuntaş, Y., Kocamaz, F.: Deep feature extraction for detection of tomato plant diseases and pests based on leaf images. Celal Bayar Univ. J. Sci. 17(2), 145–157 (2021)
Chai, T., Prasad, S., Wang, S.: Boosting palmprint identification with gender information using DeepNet. Futur. Gener. Comput. Syst. 99, 41–53 (2019)
Deng, J., Guo, J., Xue, N., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: CVPR, pp. 4690–4699 (2019)
Elhassouny, A., Smarandache, F.: Smart mobile application to recognize tomato leaf diseases using Convolutional Neural Networks. In: IEEE ICCSRE, pp. 1–4 (2019)
Fuentes, A., Yoon, S., Kim, S.C., Park, D.S.: A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17(9), 2022 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Inés, A., Domínguez, C., Heras, J., Mata, E., Pascual, V.: Biomedical image classification made easier thanks to transfer and semi-supervised learning. Comput. Methods Programs Biomed. 198, 105782 (2021)
Karthik, R., Hariharan, M., Anand, S., Mathikshara, P., Johnson, A., Menaka, R.: Attention embedded residual CNN for disease detection in tomato leaves. Appl. Soft Comput. 86, 105933 (2020)
Li, Y., Yang, J.: Meta-learning baselines and database for few-shot classification in agriculture. CEA 182, 106055 (2021)
Matin, M.M.H., Khatun, A., Moazzam, M.G., Uddin, M.S., et al.: An efficient disease detection technique of rice leaf using AlexNet. J. Comput. Commun. 8(12), 49 (2020)
Nandhini, S., Ashokkumar, K.: Improved crossover based monarch butterfly optimization for tomato leaf disease classification using convolutional neural network. Multimedia Tools Appl. 80(12), 18583–18610 (2021). https://doi.org/10.1007/s11042-021-10599-4
Pinki, F.T., Khatun, N., Islam, S.M.: Content based paddy leaf disease recognition and remedy prediction using support vector machine. In: 2017 ICCIT, pp. 1–5. IEEE (2017)
Prasad, S., Kong, A.W.K.: Using object information for spotting text. In: ECCV, pp. 540–557 (2018)
Prasad, S., Peddoju, S.K., Ghosh, D.: AgroMobile: a cloud-based framework for agriculturists on mobile platform. IJAST 59, 41–52 (2013)
Prasad, S., Peddoju, S.K., Ghosh, D.: Multi-resolution mobile vision system for plant leaf disease diagnosis. SIViP 10(2), 379–388 (2015). https://doi.org/10.1007/s11760-015-0751-y
Prasad, S., Peddoju, S.K., Ghosh, D.: Efficient plant leaf representations: a comparative study. In: IEEE TENCON, pp. 1175–1180 (2017)
Prasad, S., Peddoju, S.K., Ghosh, D.: Agriculture as a service. IEEE Potentials 40(6), 34–43 (2021). https://doi.org/10.1109/MPOT.2015.2496327
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. ANIPS 28, 91–99 (2015)
Sander, M.E., Ablin, P., Blondel, M., Peyré, G.: Momentum residual neural networks. arXiv preprint arXiv:2102.07870 (2021)
Tian, J., Hu, Q., Ma, X., Han, M.: An improved KPCA/GA-SVM classification model for plant leaf disease recognition. J. CIS 8(18), 7737–7745 (2012)
Trivedi, J., Shamnani, Y., Gajjar, R.: Plant leaf disease detection using machine learning. In: Gupta, S., Sarvaiya, J.N. (eds.) ET2ECN 2020. CCIS, vol. 1214, pp. 267–276. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-7219-7_23
Wang, C., Ni, P., Cao, M.: Research on crop disease recognition based on multi-branch ResNet-18. In: Journal of Physics: Conference Series, vol. 1961, p. 012009. IOP Publishing (2021)
Wang, W., Lai, Q., Fu, H., Shen, J., Ling, H., Yang, R.: Salient object detection in the deep learning era: an in-depth survey. IEEE Trans. PAMI 44(6), 3239–3259 (2021)
Widiyanto, S., Fitrianto, R., Wardani, D.T.: Implementation of convolutional neural network method for classification of diseases in tomato leaves. In: IEEE ICIC, pp. 1–5 (2019)
Yang, B., Xu, Y.: Applications of deep-learning approaches in horticultural research: a review. Horticult. Res. 8(1), 1–31 (2021)
Zhang, K., Wu, Q., Liu, A., Meng, X.: Can deep learning identify tomato leaf disease? Adv. Multimedia 2018, 1–10 (2018)
Zhang, S., Huang, W., Zhang, C.: Three-channel convolutional neural networks for vegetable leaf disease recognition. Cogn. Syst. Res. 53, 31–41 (2019)
Acknowledgement
This work is carried out at Computer Science and Engineering, CIT Kokrajhar, India and would like to thank all individuals of the department. Also a special thanks to Google Research for Colaboratory (Colab) which allows to combine deep learning codes along with images, HTML, LaTeX and more, in a single document.
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Prasad, S., Singh, P.P., Kumar, P. (2022). Momentum Residual Embedding with Angular Marginal Loss for Plant Pathogen Biometrics. In: El Yacoubi, M., Granger, E., Yuen, P.C., Pal, U., Vincent, N. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2022. Lecture Notes in Computer Science, vol 13364. Springer, Cham. https://doi.org/10.1007/978-3-031-09282-4_11
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