https://doi.org/10.4081/jae.2022.1432
Tomato leaf diseases recognition based on deep convolutional neural networks
Published: 25 August 2022
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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Tomato disease control remains a major challenge in the agriculture sector. Early stage recognition of these diseases is critical to reduce pesticide usage and mitigate economic losses. While many research works have been inspired by the success of deep learning in computer vision to improve the performance of recognition systems for crop diseases, few of these studies optimized the deep learning models to generalize their findings to practical use in the field. In this work, we proposed a model for identifying tomato leaf diseases based on both in-house data and public tomato leaf images databases. Three deep learning network architectures (VGG16, Inception_v3, and Resnet50) were trained and tested. We packaged the trained model into an Android application named TomatoGuard to identify nine kinds of tomato leaf diseases and healthy tomato leaf. The results showed that TomatoGuard could be adopted as a model for identifying tomato diseases with a 99% test accuracy, showing significantly better performance compared with APP Plantix, a widely used APP for general purpose plant disease detection.
Abadi, M. et al. 2015. TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from tensorflow.org.
Bow, S. T. 2002. Pattern recognition and image preprocessing. CRC press.
DOI: https://doi.org/10.1201/9780203903896
Brahimi, M., Arsenovic, M., Laraba, S., Sladojevic, S., Boukhalfa, K., & Moussaoui, A. 2018. Deep learning for plant diseases: detection and saliency map visualisation. In Human and machine learning (pp. 93-117). Springer, Cham.
DOI: https://doi.org/10.1007/978-3-319-90403-0_6
Brahimi, M., Boukhalfa, K., & Moussaoui, A. 2017. Deep learning for tomato diseases: classification and symptoms visualization. Applied Artificial Intelligence, 31(4):299-315.
DOI: https://doi.org/10.1080/08839514.2017.1315516
Chollet, F. et al. 2015. Keras, GitHub. https://github.com/fchollet/keras
Chowdhury, M. E., Rahman, T., Khandakar, A., Ibtehaz, N., Khan, A. U., Khan, M. S., ... & Ali, S. H. M. 2021. Tomato Leaf Diseases Detection Using Deep Learning Technique. Technology in Agriculture, 453.
Chung, C. L., Huang, K. J., Chen, S. Y., Lai, M. H., Chen, Y. C., & Kuo, Y. F. 2016. Detecting Bakanae disease in rice seedlings by machine vision. Computers and electronics in agriculture, 121:404-411.
DOI: https://doi.org/10.1016/j.compag.2016.01.008
Dhingra, G., Kumar, V., & Joshi, H. D. 2018. Study of digital image processing techniques for leaf disease detection and classification. Multimedia Tools and Applications, 77(15):19951-20000.
DOI: https://doi.org/10.1007/s11042-017-5445-8
Durmuş, H., Güneş, E. O., & Kırcı, M. 2017, August. 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).
DOI: https://doi.org/10.1109/Agro-Geoinformatics.2017.8047016
Fuentes, A., Yoon, S., Kim, S. C., & Park, D. S. 2017. A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors, 17(9):2022.
DOI: https://doi.org/10.3390/s17092022
Fukushima, K., Miyake, S., & Ito, T. 1983. Neocognitron: A neural network model for a mechanism of visual pattern recognition. IEEE transactions on systems, man, and cybernetics, (5):826-834.
Gleason, M. L., & Edmunds, B. A. 2005. Tomato diseases and disorders. Ames, IA: Iowa State University, University Extension.
Glorot, X., & Bengio, Y. 2010, March. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics (pp. 249-256). JMLR Workshop and Conference Proceedings.
Goodfellow, I., Bengio, Y., & Courville, A. 2017. Deep learning (adaptive computation and machine learning series). Cambridge Massachusetts, 321-359.
Hanssen, I. M., Lapidot, M. & Thomma, B. P. 2010. Emerging viral diseases of tomato crops. Mol. plant-microbe interactions, 23:539–548.
DOI: https://doi.org/10.1094/MPMI-23-5-0539
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).
DOI: https://doi.org/10.1109/CVPR.2016.90
Heisel, S., Kovačević, T., Briesen, H., Schembecker, G., & Wohlgemuth, K. 2017. Variable selection and training set design for particle classification using a linear and a non-linear classifier. Chemical Engineering Science, 173:131-144.
DOI: https://doi.org/10.1016/j.ces.2017.07.030
Hoagland, D. R., & Arnon, D. I. 1950. The water-culture method for growing plants without soil. Circular. California agricultural experiment station, 347(2nd edit).
Hughes, D., & Salathé, M. 2015. An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060.
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. 2017. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).
DOI: https://doi.org/10.1109/CVPR.2017.243
Kawasaki, Y., Uga, H., Kagiwada, S., & Iyatomi, H. 2015, December. Basic study of automated diagnosis of viral plant diseases using convolutional neural networks. In International symposium on visual computing (pp. 638-645. Springer, Cham).
DOI: https://doi.org/10.1007/978-3-319-27863-6_59
Kezhu, T., Yuhua, C., Weixian, S., & Xiaoda, C. 2014. Identification of diseases for soybean seeds by computer vision applying BP neural network. International Journal of Agricultural and Biological Engineering, 7(3):43-50.
Kotikalapudi, R. & contributors. 2017. keras-vis.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324.
DOI: https://doi.org/10.1109/5.726791
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. 1989. Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4):541-551.
DOI: https://doi.org/10.1162/neco.1989.1.4.541
Lin, M., Chen, Q., & Yan, S. 2013. Network in network. arXiv preprint arXiv:1312.4400.
Liu, B., Zhang, Y., He, D. & Li, Y. 2017. Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry, 10, 11.
DOI: https://doi.org/10.3390/sym10010011
Liu, N., Han, J., Zhang, D., Wen, S., & Liu, T. 2015. Predicting eye fixations using convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 362-370.
Lu, Y., Yi, S., Zeng, N., Liu, Y., & Zhang, Y. 2017. Identification of rice diseases using deep convolutional neural networks. Neurocomputing, 267:378-384.
DOI: https://doi.org/10.1016/j.neucom.2017.06.023
Masood, S. Z., Shu, G., Dehghan, A., & Ortiz, E. G. 2017. License plate detection and recognition using deeply learned convolutional neural networks. arXiv preprint arXiv:1703.07330.
Mohanty, S. P., Hughes, D. P., & Salathé, M. 2016. Using deep learning for image-based plant disease detection. Frontiers in plant science, 7:1419.
DOI: https://doi.org/10.3389/fpls.2016.01419
Mokhtar, U., Ali, M. A., Hassanien, A. E., & Hefny, H. 2015. Identifying two of tomatoes leaf viruses using support vector machine. In Information systems design and intelligent applications (pp. 771-782. Springer, New Delhi.
DOI: https://doi.org/10.1007/978-81-322-2250-7_77
Parkhi, O. M., Vedaldi, A., Zisserman, A. 2015. Deep face recognition. In BMVC, vol. 1, 6.
DOI: https://doi.org/10.5244/C.29.41
Polston, J. E., McGovern, R. J., & Brown, L. G. 1999. Introduction of tomato yellow leaf curl virus in Florida and implications for the spread of this and other geminiviruses of tomato. Plant Disease, 83(11):984-988.
DOI: https://doi.org/10.1094/PDIS.1999.83.11.984
Prasad, S., & Singh, P. P. 2017a. November. Medicinal plant leaf information extraction using deep features. In TENCON 2017-2017 IEEE Region 10 Conference (pp. 2722-2726). IEEE.
DOI: https://doi.org/10.1109/TENCON.2017.8228324
Prasad, S., & Singh, P. P. 2017b. Vision system for medicinal plant leaf acquisition and analysis. In Applications of Cognitive Computing Systems and IBM Watson (pp. 37-45. Springer, Singapore.
DOI: https://doi.org/10.1007/978-981-10-6418-0_5
Riley M B, Williamson M R, Maloy O. 2002. Plant disease diagnosis. The plant health instructor. 1.
DOI: https://doi.org/10.1094/PHI-I-2002-1021-01
Romualdo, L. M., Luz, P. H. C., Devechio, F. F. S., Marin, M. A., Zúñiga, A. M. G., Bruno, O. M., & Herling, V. R. 2014. Use of artificial vision techniques for diagnostic of nitrogen nutritional status in maize plants. Computers and electronics in agriculture, 104:63-70.
DOI: https://doi.org/10.1016/j.compag.2014.03.009
Sabanci, K., Kayabasi, A., & Toktas, A. 2017. Computer vision‐based method for classification of wheat grains using artificial neural network. Journal of the Science of Food and Agriculture, 97(8):2588-2593.
DOI: https://doi.org/10.1002/jsfa.8080
Shrivastava, S., Singh, S. K., & Hooda, D. S. 2017. Soybean plant foliar disease detection using image retrieval approaches. Multimedia Tools and Applications, 76(24):26647-26674.
DOI: https://doi.org/10.1007/s11042-016-4191-7
Simonyan, K., & Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D. 2016. Deep neural networks based recognition of plant diseases by leaf image classification. Computational intelligence and neuroscience, 2016.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. 2015. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9).
DOI: https://doi.org/10.1109/CVPR.2015.7298594
Tamaazousti, Y., Le Borgne, H., Hudelot, C., & Tamaazousti, M. 2019. Learning more universal representations for transfer-learning. IEEE transactions on pattern analysis and machine intelligence, 42(9):2212-2224.
DOI: https://doi.org/10.1109/TPAMI.2019.2913857
Tomè, D., Monti, F., Baroffio, L., Bondi, L., Tagliasacchi, M., & Tubaro, S. 2016. Deep convolutional neural networks for pedestrian detection. Signal processing: image communication, 47:482-489.
DOI: https://doi.org/10.1016/j.image.2016.05.007
Wolfert, S., Ge, L., Verdouw, C. & Bogaardt, M.-J. 2017. Big data in smart farming–a review. Agric. Syst. 153:69–80.
DOI: https://doi.org/10.1016/j.agsy.2017.01.023
Zhang, C. L., Luo, J. H., Wei, X. S., & Wu, J. 2017, September. In defense of fully connected layers in visual representation transfer. In Pacific Rim Conference on Multimedia (pp. 807-817). Springer, Cham.
DOI: https://doi.org/10.1007/978-3-319-77383-4_79
How to Cite
Tian, K. (2022) “Tomato leaf diseases recognition based on deep convolutional neural networks”, Journal of Agricultural Engineering, 54(1). doi: 10.4081/jae.2022.1432.
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