Abstract
Indian economy greatly depends on agricultural productivity. Major provocation in the field of agriculture is plant diseases and pests. To reduce substantial economic losses, there is a need to have a system that could detect plant diseases in accurate and faster manner. This research paper contributes to this detection by proposing an approach based on deep learning technique that automates the process of classifying tomato leaf diseases. Convolution neural network (CNN) is trained from scratch to classify the image datasets based on the visible effects of diseases on plant leaves. We train a deep convolution neural network using a dataset that consists of leaf images acquired from different sources to identify early blight and late blight fungal diseases that occur in tomato plants. Sequential model of deep neural network is developed with an accuracy of 97.25%, demonstrating the feasibility of our system. The novelty of proposed system is its testing on a dataset that consists of data from real-world sources combined with Internet downloaded images and plant village standard dataset [1–5].
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mohanty SP, Hughes DP, Salathe M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7. Article 1419
Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng IEEE 22(10):1345e1359
Cruz A, Luvisi A, De Bellis L, Ampatzidis Y (2017) X-FIDO: an effective application for detecting olive quick decline syndrome with deep learning and data fusion. Front Plant Sci 8
Deep Learning—Part 4: Convolutional Neural Network. https://towardsdatascience.com/applied-deep-learning-part-4-convolutional-neural-networks-584bc134c1e2. Last accessed 2017/11/08
The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3). https://adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html. Last accessed 2019/11/25
Home. In: Food and Agriculture Organization of the United Nations. http://www.fao.org/home/en/. Accessed 25 Nov 2019
Jacobs IS, Bean CP (1963) Fine particles, thin films, and exchange anisotropy: (effects of finite dimensions and interfaces on the basic properties of ferromagnets). Research Information Section. The Knolls, Schenectady, NY
Srivastava S, Boyat S, Sadistap S (2014) A novel vision sensing system for tomato quality detection. Int J Food Sci 2014:1–11
Sabrol H, Kumar S (2016) Fuzzy and neural network based tomato plant disease classification using natural outdoor images. Indian J Sci Technol 9(44)
Fuentes A et al (2017) A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors (Basel, Switzerland)
Amara J, Bouaziz B, Algergawy A (2017) A deep learning based approach for banana leaf diseases classification. In Lecture Notes in Informatics (LNI). Gesellschaft für Informatik, Bonn, Germany, pp 79e88
Brahimi M, Boukhalfa K, Moussaoui A (2017) Deep learning for tomato diseases: classification and symptoms visualization. Appl Artif Intell J
Oppenheim D, Shani G (2017) Potato disease classification using convolution neural networks. Advances in animal biosciences: precision agriculture. In: 11th European conference on precision agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK
Liu B, Zhang Y, He D, Li Y (2017) Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry 10:11
Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145(2018):311–318
Lu Y, Yi S, Zeng N, Liu Y, Zhang Y (2017) Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267:378–384
Brosnan T, Sun D-W (2004) Improving quality inspection of food products by computer vision—a review. J Food Eng 61:3–16. https://doi.org/10.1016/s0260-8774(03)00183-3
TM (2019) dataset.zip, Google Docs. https://drive.google.com/file/d/1DVy0LyUUfJciyo7BUFm1sHKSRdTVJgjF/view. Last accessed 2019/03/10
Validation loss increases while validation accuracy is still improving. https://github.com/keras-team/keras/issues/3755. Last accessed 2019/11/21
Aydogdu MF (2017) Comparison of three different CNN architectures for age classification. In: 2017 IEEE 11th international conference on semantic computing (ICSC)
Durmus H, Gunes EO, Kirci M (2017) Disease detection on the leaves of the tomato plants by using deep learning. In: 6th international conference on agro-geoinformatics. https://doi.org/10.1109/agro-geoinformatics.2017.8047016
Acknowledgements
We would like to show our gratitude to Mr. Swapnil Dekhane, Research Officer at Tansa Farm, Bhiwandi, for assisting in data collection process.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Khan, S., Narvekar, M. (2020). Disorder Detection in Tomato Plant Using Deep Learning. In: Vasudevan, H., Michalas, A., Shekokar, N., Narvekar, M. (eds) Advanced Computing Technologies and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-3242-9_19
Download citation
DOI: https://doi.org/10.1007/978-981-15-3242-9_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3241-2
Online ISBN: 978-981-15-3242-9
eBook Packages: EngineeringEngineering (R0)