Abstract
Accurate and quantitative estimation of disease severity in plants is a complex task, even for experienced agronomists and plant pathologists, where incorrect evaluation might lead to the inappropriate use of pesticides. In this paper, the authors have utilized two Convolutional Neural Networks (CNN), namely, AlexNet and ResNet18, for assessing the disease severity in Grape plant. The images for Isariopsis Leaf Spot disease in Grape plant, also known as Leaf Blight, were taken from the PlantVillage dataset, divided into three categories of severity stages (early, middle & end) and used for training the CNN models, via the transfer learning approach. The effects of fine-tuning the hyperparameters such as mini-batch size, epochs and data augmentation were also observed and analysed. For performance comparison, measures such as classification accuracy, mean F1-score, mean precision, mean recall, validation loss, ROC curves and time taken were recorded.
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References
Giomi T, Runhaar P, Runhaar H (2018) Reducing agrochemical use for nature conservation by Italian olive farmers: An evaluation of public and private governance strategies. International journal of agricultural sustainability. 16(1):94–105
Pal T, Jaiswal V, Chauhan RS (2016) DRPPP: A machine learning based tool for prediction of disease resistance proteins in plants. Comput Biol Med 78:42–48
Camargo A, Smith JS (2009) Image pattern classification for the identification of disease causing agents in plants. Comput Electron Agric 66(2):121–125
Al Bashish D, Braik M, Bani-Ahmad S (2011) Detection and classification of leaf diseases using K-means-based segmentation and Neural Networks-based Classification. Inf Technol J 10(2):267–275
Camargo A, Smith JS (2009) An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosys Eng 102(1):9–21
Kamilaris A, Prenafeta-Boldú FX (2018) Deep learning in agriculture: A survey. Comput Electron Agric 147(February):70–90
Mohanty SP, Hughes DP, Salathé M (2016) Using Deep Learning for Image-Based Plant Disease Detection. Frontiers in Plant Science. 7(September):1–10
Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318
Wang, G., Sun, Y., & Wang, J.: Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning. Computational Intelligence and Neuroscience. 1–8, (2017)
Liang Q, Xiang S, Hu Y, Coppola G, Zhang D, Sun W (2019) PD2SE-Net: Computer-assisted plant disease diagnosis and severity estimation network. Comput Electron Agric 157:518–529
Sladojevic, S., Arsenovic, M., Stefanovic, D., Anderla, A., Culibrk, D.: Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification. Computational Intelligence and Neuroscience. 1–11, (2016)
Verma, S., Chug, A., Singh, A. P., Sharma, S., Rajvanshi, P.: Deep Learning-Based Mobile Application for Plant Disease Diagnosis: A Proof of Concept With a Case Study on Tomato Plant. In Applications of Image Processing and Soft Computing Systems in Agriculture. 242–271, IGI Global, (2019)
Lu Y, Yi S, Zeng N, Liu Y, Zhang Y (2017) Identification of rice diseases using deep convolutional neural networks. Neurocomputing. 267:378–384
Sun Y, Liu Y, Wang G, Zhang H (2017) Deep learning for plant identification in natural environment. Comput Intell Neurosci 2017:1–6
Zhang S, Huang W, Zhang C (2019) Three-channel convolutional neural networks for vegetable leaf disease recognition. Cogn Syst Res 53:31–41
Toda Y, Okura F (2019) How Convolutional Neural Networks Diagnose Plant Disease. Plant Phenomics. 2019:9237136
Krizhevsky, A., Sutskever, I., Hinton, G. E.: Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097–1105, (2012)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778, (2016)
Hughes, D. P., Salathe, M.: An open access repository of images on plant health to enable the development of mobile disease diagnostics. ArXiv Preprint. https://doi.org/10.1111/1755-0998.12237, (2015)
Campbell, C. L., Neher, D. A.: Estimating disease severity and incidence. In Epidemiology and management of root diseases, pp 117–147. Springer, Berlin, Heidelberg, (1994)
https://www.mathworks.com/help/deeplearning/ug/pretrained-convolutional-neural-networks.html. Last accessed: May 2019
Acknowledgments
This study was supported by Department of Science and Technology (DST), Government of India, New Delhi, under Interdisciplinary Cyber Physical Systems (ICPS) Programme (Reference No. T-319).
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Verma, S., Chug, A., Singh, A.P. (2020). Impact of Hyperparameter Tuning on Deep Learning Based Estimation of Disease Severity in Grape Plant. In: Ghazali, R., Nawi, N., Deris, M., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2020. Advances in Intelligent Systems and Computing, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-030-36056-6_16
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DOI: https://doi.org/10.1007/978-3-030-36056-6_16
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