Abstract
Assessment of disease severity is one of the major challenges which helps in the prediction of yield quantitatively and to decide the control factors that improve the yield of any crop. Hence a perfect system is essential to measure the severity level of the disease in order to improve its productivity. An intelligent state of the art technique i.e., deep learning plays an inevitable role in most of the real-time applications including smart farming. Tomato crops are frequently affected by a dangerous fungal disease i.e., early blight, resulting in 100% production loss to farmers. In this work, an identification of early blight disease in tomato leaves is performed by a recently invented paper microscope named Foldscope. Further, a deep Residual Network101 (ResNet101) of Convolutional Neural Network (CNN) architecture is used to measure the severity level of early blight disease in tomato leaves. The dataset in the model is trained by using an open database i.e., PlantVillage dataset for mild, moderate, and severely diseased leaves along with healthy tomato leaves. The results of ResNet101 architecture is compared with other pre-trained CNN such as Visual Geometry Group16 (VGG16), VGG19, GoogLeNet, AlexNet, and ResNet50. Among these networks, the deep ResNet101 architecture has achieved the highest accuracy of 94.6%. Finally, a case study has been conducted based on the estimated severity levels and the required fungicide treatment is also prescribed.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adhikari P, Oh Y, Panthee DR (2017) Current status of early blight resistance in tomato – an update. Int J Mol Sci 18. https://doi.org/10.3390/ijms18102019
Amara J, Bouaziz B, Algergawy A (2017). A deep learning based approach for banana leaf diseases classification. BTW 2017-the 17th conference on database systems for business, technology and web, Stuttgart, Germany 79-88
Atabay HA (2017) Deep residual learning for tomato plant leaf disease identification. J Theor Appl Inf Technol 95:6800–6808
Bock CH, Poole GH, Parker PE, Gottwald TR (2010) Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. CRC Crit Rev Plant Sci 29:59–107
Cruz A, Ampatzidis Y, Pierro R, Materazzi A, Panattoni A, Bellis LD, Luvisi A (2019) Detection of grapevine yellow symptoms in vitis vinifera L. with artificial intelligence. Comput Electron Agric 157:63–76
Cybulski JS, Clements J, Prakash M (2014). Foldscope: origami-based paper microscope. PLoS one 9. https://doi.org/10.1371/journal.pone.0098781.
Desta M, Yesuf M (2015). Efficacy and economics of fungicides and their application schedule for early blight (Alternaria solani) management and yield of tomato at South Tigray, Ethiopia J Plant Pathol Microbiol 6. https://doi.org/10.4172/2157-7471.1000268.
Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318
Fuentes A, Yoon S, Kim SC, Park DS (2017). A robust deep-learning-based detector for real time tomato plant diseases and pests recognition Sensors 9. https://doi.org/10.3390/s1709202217
Golhani K, Balasundram SK, Vadamalai G, Pradhan B (2018) A review of neural networks in plant disease detection using hyperspectral data. Inf Precis Agric 5:354–371
He K, Zhang X, Ren S, Sun J (2016). Deep residual learning for image recognition. 2016 IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, NV, USA 770–778.
Hughes DP, Salathe M (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv:1511.08060
Kamilaris A, Prenafeta BFX (2018) Deep learning in agriculture: a survey. Comput Electron Agric 147:70–90
Krizhevsky A, Sutskever I, Hinton, GE (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems Conference 1097–1105.
Liu B, Zhang Y, He D, Yuxiang Li (2017). Identification of apple leaf diseases based on deep convolutional neural networks Symmetry 10.
Maheswari P, Raja P, Ghangaonkar NM (2018). Intelligent disease detection system for early blight of tomato using Foldscope: A pilot study. 2018 IEEE 4th international symposium in robotics and manufacturing automation (ROMA), Perambalur, India
Mewbaze E, Owomugisha G (2016). Machine learning for plant disease incidence and severity measurements from leaf images. 2016 5th IEEE international conference on machine learning and applications (ICMLA), Anaheim, CA, USA 158-163
Mohanty SP, Hughes D, Salathe M (2016). Using deep learning for image-based plant disease detection Front Plant Sci 7. https://doi.org/10.3389/fpls.2016.014197
Singh AK, Ganapathysubramanian B, Sarkar S, Singh A (2018) Deep learning for plant stress phenotyping: trends and future perspectives. Trends Plant Sci 23:883–898
Wang G, Sun Y, Wang J (2017) Automatic image based plant disease severity estimation using deep learning. Comput Intell Neurosci 2017:1–8. https://doi.org/10.1155/2017/2917536
Wiatowski T, Bolcskeri H (2018) A mathematical theory of deep convolutional neural networks for feature extraction. IEEE Trans Inf Theory 64:1845–1866
Xie S, Girshick R, Dollar P, Tu Z, He K (2017). Aggregated residual transformations for deep neural networks. 2017 IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, USA 5987–5995. https://doi.org/10.3390/sym10010011, 10
Zhang K, Wu Q, Liu A, Meng X (2018) Can deep learning identify tomato leaf disease? Adv Multimed 2018:1–10. https://doi.org/10.1155/2018/6710865
Acknowledgments
The authors would like to acknowledge Dr. N.M. Ghangaokar, Department of Botany, Chandamal Tarachand Bora College, Shirur, Pune, India, for his support in certifying the image categories such as healthy, mild, moderate and severe early blight. The authors would also like to acknowledge Dr. Manuel Pérez Ruiz, Area of Agroforestry Engineering, Department of Aerospace Engineering and Fluid Mechanics, University of Seville, Spain, for his support in training the deep learning architecture.
Funding
This research was funded by “Department of Biotechnology (DBT), Government of India, BT/IN/Indo-US/Foldscope/39/2015 dated 20.03.2018”.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Prabhakar, M., Purushothaman, R. & Awasthi, D.P. Deep learning based assessment of disease severity for early blight in tomato crop. Multimed Tools Appl 79, 28773–28784 (2020). https://doi.org/10.1007/s11042-020-09461-w
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-09461-w