Dilara GERDAN, Caner KOÇ, Mustafa VATANDAŞ

Diagnosis of Tomato Plant Diseases Using Pre-trained Architectures and A Proposed Convolutional Neural Network Model

Tomatoes are of the most important vegetables in the world. Presence of diseases and pests in the growing area significantly affect the choice of variety in tomato. The aim of this study is to diagnose tomato plant diseases faster and with higher degrees of accuracy. For this purpose, deep learning was used to diagnose some diseases in tomatoes, including bacterial spot, early blight, leaf mold, septoria leaf spot, target spot, mosaic virus, and yellow leaf curl virus were analyzed CNN models. A CNN model with a 2D convolutional three layers, one flatten layer approach and several Keras models, including DenseNet201, InceptionResNetV2, MobileNet, Visual Geometry Group 16 architectures were proposed. The experimental results showed that the accuracy scores were 99.82%, 92.12%, 92.75%, 91.50% and 84.12% training accuracy, respectively. The proposed CNN model provided the opportunity for rapid diagnosis for approximately 14.9 minutes. The results obtained in this study can be used in robotic spraying and harvesting operations.

Keywords:

Automated diagnose convolutional neural network, deep learning, tomato,

PDF

___

Abramovitch RB, Anderson JC, Martin GB (2006). Bacterial elicitation and evasion of plant innate immunity. Nat. Rev. Mol. Cell Biol. 7, 601–611.
Ashok S, Kishore G, Rajesh V, Suchitra S, Sophia SGG, Pavithra B (2020). Tomato Leaf Disease Detection Using Deep Learning Techniques. 2020 5th International Conference on Communication and Electronics Systems (ICCES). Doi:10.1109/icces48766.2020.9137986
Blancard D (2012). Tomato diseases: identification, biology and control: a colour handbook. CRC Press. Brahimi M, Boukhalfa K, Moussaoui A (2017). Deep learning for tomato diseases: classification and symptoms visualization. Applied Artificial Intelligence, 31(4), 299-315.
De Luna RG, Dadios EP, Bandala AA (2018). Automated image capturing system for deep learning-based tomato plant leaf disease detection and recognition. In TENCON 2018-2018 IEEE Region 10 Conference (pp. 1414-1419). IEEE.
Deng, L., Yu, D. (2014). “Three Classes of Deep Learning Networks” in Deep learning: methods and applications. Foundations and trends in signal processing, 7(3–4), 197-387.
Durmuş H, Güneş EO, Kırcı M (2017). Disease detection on the leaves of the tomato plants by using deep learning. In 2017 6th International Conference on Agro-Geoinformatics (pp. 1-5). IEEE.
FAO (2019). Web Page: http://www.fao.org/faostat/en/#data/QC/visualize, Accessed on: 28.04.2021
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, 17(9), 2022.
Geetharamani G, Pandian A (2019). Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Computers & Electrical Engineering, 76, 323-338.
Griffiths S, Mesarich CH, Overdijk EJ, Saccomanno B, De Wit PJ, Collemare J (2018). Down‐regulation of cladofulvin biosynthesis is required for biotrophic growth of Cladosporium fulvum on tomato. Molecular plant pathology, 19(2), 369-380.
Gulli A, Kapoor A, Pal S (2019). Deep learning with TensorFlow 2 and Keras: regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API. Packt Publishing Ltd.
El-Amir..H, Hamd M (2020). “A Gentle Introduction” in Deep Learning Pipeline, Apress, Berkeley, CA, USA, 2020.
He K, Zhang X, Ren S, Sun J (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Adam H (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).
Hughes DP, Salathe M (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060.
Krizhevsky A, Sutskever I, Hinton G (2012). "ImageNet classification with deep convolutional neural networks." In NIPS’2012 . 23, 24, 27, 100, 200, 371, 456, 460.
Lawrence S, Giles CL, Tsoi AC, Back AD (1997). Face recognition: A convolutional neural-network approach. IEEE transactions on neural networks, 8(1), 98-113.
LeCun Y, Bengio Y, Hinton G (2015). "Deep learning." Nature 521(7553): 436-444.
Mkonyi L, Rubanga D, Richard M, Zekeya N, Sawahiko S, Maiseli B, Machuve D (2020). Early identification of Tuta absoluta in tomato plants using deep learning. Scientific African, 10, e00590.
Nitzany FA (1960). Transmission of tobacco mosaic virus through tomato seed and virus inactivation by methods of seed extraction and seed treatments. Ktavim, 10, 63-7.
Rangarajan AK, Purushothaman R, Ramesh A (2018). Tomato crop disease classification using pre-trained deep learning algorithm. Procedia computer science, 133, 1040-1047.
Richard D, Boyer C, Lefeuvre P, Canteros BI, Beni-Madhu S, Portier P, Pruvost O (2017). Complete genome sequences of six copper-resistant Xanthomonas strains causing bacterial spot of solaneous plants, belonging to X. gardneri, X. euvesicatoria, and X. vesicatoria, using long-read technology. Genome announcements, 5(8).
Sade D, Sade N, Brotman Y, Czosnek H (2020). Tomato yellow leaf curl virus (TYLCV)-resistant tomatoes share molecular mechanisms sustaining resistance with their wild progenitor Solanum habrochaites but not with TYLCV-susceptible tomatoes. Plant Science, 295, 110439.
Simonyan K, Zisserman A (2015). Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 1–14.
Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 31, No. 1).
Tang Y (2013). Deep learning using linear support vector machines. arXiv preprint arXiv:1306.0239.
Tm P, Pranathi A, SaiAshritha K, Chittaragi NB, Koolagudi SG (2018). Tomato leaf disease detection using convolutional neural networks. In 2018 Eleventh International Conference on Contemporary Computing (IC3) (pp. 1-5). IEEE.
Verma S, Chug A, Singh AP (2020) Application of convolutional neural networks for evaluation of disease severity in tomato plant, Journal of Discrete Mathematical Sciences and Cryptography, 23:1, 273-282, DOI: 10.1080/09720529.2020.1721890
Wang Q, Qi F, Sun M, Qu J, Xue J (2019). "Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques", Computational Intelligence and Neuroscience, vol. 2019, Article ID 9142753, 15 pages, 2019. https://doi.org/10.1155/2019/9142753
Zhang K, Wu Q, Liu A, Meng X (2018). Can Deep Learning Identify Tomato Leaf Disease? Advances in Multimedia, vol. 2018, Article ID 6710865, 10 pages, 2018. https://doi.org/10.1155/2018/6710865.
Zhang Y, Song C, Zhang D (2020). Deep Learning-based Object Detection Improvement for Tomato Disease. IEEE Access, 1–1. doi:10.1109/access.2020.2982456