Defect Identification and Classification of Tomato Leaf Using Convolutional Neural Network


  • S. Shargunam Department of Electronics and Communication Engineering, Francis Xavier Engineering College, Tamil Nadu, India
  • G. Rajakumar Department of Electronics and Communication Engineering, Francis Xavier Engineering College, Tamil Nadu, India


Canny Edge Detection, Classification, Convolutional Neural Network


Tomatoes are the most commonly grown crop globally, and they are used in almost every kitchen. India holds second place in the production of tomatoes. Due to the various kinds of diseases, the quantity and quality of tomato crop go down. Identifying the diseases in the earlier stage is very important and will help the farmers save the crop. The first initial step is pre-processing, for the Canny edge detection method is used for detecting the edges in the tomato leaves. The classification of tomato leaves is to be carried out by extracting the features like color, shape, and texture. Extracted features from segmented images are fed into classification. The convolutional neural network algorithm will be used, which will give a better accuracy to classify the diseases in the tomato leaves.


Brahimi, Mohammed, Marko Arsenovic, Sohaib Laraba, SrdjanSladojevic, KamelBoukhalfa, and Abdelouhab Moussaoui, “Deep learning for plant diseases: detection and saliency map visualization,” in Human and machine learning, Springer, Cham, pp. 93-117, 2018.

M. Brahimi, K. Boukhalfa, and A. Moussaoui, “Deep learning for tomato diseases: classification and symptoms visualization,” Applied Artificial Intelligence, Vol. 31, pp. 299-315, Apr 2017.

C. De. Chant, T.Wiesner-Hanks, S. Chen, E. L. Stewart, J. Yosinski, M. A. Gore, R. J. Nelson, and H. Lipson, “Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning,” Phytopathology, Vol. 107, pp. 1426-1432, Nov 2017.

Fujita, Erika, Yusuke Kawasaki, Hiroyuki Uga, Satoshi Kagiwada, and Hitoshi Iyatomi, “Basic investigation on a robust and practical plant diagnostic system,” In 2016 15th IEEE international conference on machine learning and applications (ICMLA), IEEE, pp. 989-992, 2016.

A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 2017.

Huang, Gao, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q. Weinberger, “Densely connected convolutional networks,” In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 4700-4708.

Jia, Yangqing, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor

Darrell,“Caffe: Convolutional architecture for fast feature embedding,” In Proceedings of the 22nd ACM international conference on Multimedia, pp. 675-678, 2014.

Kawasaki, Yusuke, Hiroyuki Uga, Satoshi Kagiwada, and Hitoshi Iyatomi. “Basic study of automated diagnosis of viral plant diseases using convolutional neural networks,” In International symposium on visual computing, Springer, Cham, pp. 638-645, 2015.

Krizhevsky, Alex, IlyaSutskever, and Geoffrey E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in neural information processing systems, Vol. 25, pp. 1097-1105, 2012.

Mohanty, P. Sharada David P. Hughes, and Marcel Salathé, “Using deep learning for image-based plant disease detection,” Frontiers in plant science, Vol. 7, pp. 1419, 2016.

E. Moriones, J. Navas-Castillo, “Tomato yellow leaf curl virus, an emerging virus complex causing epidemics worldwide,” Virus research, Vol. 71, pp. 123-34, Nov. 2000.

J. Navas-Castillo, S. Sánchez-Campos, J. A. Díaz, E. Sáez-Alonso, E. Moriones “Tomato yellow leaf curl virus-Is causes a novel disease of common bean and severe epidemics in tomato in Spain,” Plant Disease, Vol. 83, pp. 29-32, Jan. 1999.

B. Picó, M. J. Díez, and F. Nuez, “Viral diseases causing the greatest economic losses to the tomato crop. II. The Tomato yellow leaf curl virus: A review,” Scientia Horticulturae, Vol. 67, pp. 151-196, Dec. 1996.

A. K. Rangarajan, R. Purushothaman and A. Ramesh, “Tomato crop disease classification using pre-trained deep learning algorithm,” Procedia Computer Science, Vol. 133, pp. 1040-1047, Jan 2018.

Simonyan, Karen, and Andrew Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv: pp. 1409-1556, 2014.

Szegedy, Christian, Sergey Ioffe, Vincent Vanhoucke, and Alexander A. Alemi, “Inception-v4, inception-resnet and the impact of residual connections on learning,” In Thirty-first AAAI conference on artificial intelligence, 2017.

Szegedy, Christian, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna, “Rethinking the inception architecture for computer vision,” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818-2826, 2016.

W. Tan, C. Zhao, H. Wu, “Intelligent alerting for fruit-melon lesion image based on momentum deep learning,” Multimedia Tools and Applications, Vol. 75, pp. 16741-16761, Dec. 2016.

E. C. Too, L. Yujian, S. Njuki, and L. Yingchun, “A comparative study of fine-tuning deep learning models for plant disease identification,” Computers and Electronics in Agriculture, Vol. 161, pp. 272-279, June 2019.

Wang, Jingxian, Lei Chen, Jian Zhang, Yuan Yuan, Miao Li, and WeiHui Zeng, “CNN transfer learning for automatic image-based classification of crop disease,” In Chinese Conference on Image and Graphics Technologies, Springer, Singapore, pp. 319-329, 2018.

K. Yamamoto, T. Togami, N. Yamaguchi, “Super-resolution of plant disease images for the acceleration of image-based phenotyping and vigor diagnosis in agriculture,” Sensors, Vol. 17, pp. 2557, Nov. 2017.




How to Cite

Shargunam, S., & Rajakumar, G. (2021). Defect Identification and Classification of Tomato Leaf Using Convolutional Neural Network. Asian Journal of Electrical Sciences, 10(1), 14–19. Retrieved from