Identifiction of tomato leaf diseases using convolutional neural network with multi-scale and feature reuse

Peng Li, Nan Zhong, Wei Dong, Meng Zhang, Dantong Yang

Abstract


Various diseases seriously affect the quality and yield of tomatoes. Fast and accurate identification of disease types is of great significance for the development of smart agriculture. Many Convolution Neural Network (CNN) models have been applied to the identification of tomato leaf diseases and achieved good results. However, some of these are executed at the cost of large calculation time and huge storage space. This study proposed a lightweight CNN model named MFRCNN, which is established by the multi-scale and feature reuse structure rather than simply stacking convolution layer by layer. To examine the model performances, two types of tomato leaf disease datasets were collected. One is the laboratory-based dataset, including one healthy and nine diseases, and the other is the field-based dataset, including five kinds of diseases. Afterward, the proposed MFRCNN and some popular CNN models (AlexNet, SqueezeNet, VGG16, ResNet18, and GoogLeNet) were tested on the two datasets. The results showed that compared to traditional models, the MFRCNN achieved the optimal performance, with an accuracy of 99.01% and 98.75% in laboratory and field datasets, respectively. The MFRCNN not only had the highest accuracy but also had relatively less computing time and few training parameters. Especially in terms of storage space, the MFRCNN model only needs 2.7 MB of space. Therefore, this work provides a novel solution for plant disease diagnosis, which is of great importance for the development of plant disease diagnosis systems on low-performance terminals.
Keywords: tomato diseases, convolutional neural network, confusion matrix, multi-scale, feature reuse
DOI: 10.25165/j.ijabe.20231606.6913

Citation: Li P, Zhong N, Dong W, Zhang M, Yang D T. Identification of tomato leaf diseases using convolutional neural network with multi-scale and feature reuse. Int J Agric & Biol Eng, 2023; 16(6): 226–235.

Keywords


tomato diseases, convolutional neural network, confusion matrix, multi-scale, feature reuse

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Hassan S M, Maji A K, Jasiński M, Leonowicz Z, Jasi´nska E. Identification of plant-leaf diseases using CNN and transfer-learning approach. Electronics, 2021; 10（12）: 1388.

Karthik R, Hariharan M, Anand S, Mathikshara P, Johnson A, Menaka R. Attention embedded residual CNN for disease detection in tomato leaves. Applied Soft Computing, 2020; 86: 105933.

Bhujel A, Kim N E, Arulmozhi E, Basak J K, Kim H T. A lightweight Attention-based convolutional neural networks for tomato leaf disease classification. Agriculture, 2022; 12（2）: 228.

Thangaraj R, Anandamurugan S, Pandiyan P, Kaliappan K V. Artificial intelligence in tomato leaf disease detection: a comprehensive review and discussion. Journal of Plant Diseases and Protection, 2022; 129（3）: 469–488.

Ahad M T, Li Y, Song B, Bhuiyan T. Comparison of CNN-based deep learning architectures for rice diseases classification. Artificial Intelligence in Agriculture, 2023; 9: 22–35.

Mokhtar U, Ali M A S, Hassenian A E, Hefny. Tomato leaves diseases detection approach based on Support Vector Machines. In: 2015 11th International Computer Engineering Conference （ICENCO）, Cairo: IEEE, 2015; pp.246–250.

Tian K, Li J H, Zeng J F, Evans A, Zhang L N. Segmentation of tomato leaf images based on adaptive clustering number of K-means algorithm. Computers and Electronics in Agriculture, 2019; 165: 104962.

Chaudhary A, Kolhe S, Kamal R. An improved random forest classifier for multi-class classification. Information Processing in Agriculture, 2016; 3（4）: 215–222.

Waheed A, Goyal M, Gupta D, Khanna A, Hassanien A E, Pandey H M. An optimized dense convolutional neural network model for disease recognition and classification in corn leaf. Computers and Electronics in Agriculture, 2020; 175: 105456.

Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks. Communications of the ACM, 2017; 60（6）: 84–90.

Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 3rd Int. Conf. Learn. Represent. ICLR 2015- Conf. Track Proc., 2014; pp.1–14.

He K M, Zhang X Y, Ren S Q, Sun J. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition （CVPR）, Las Vegas: IEEE, 2016; pp.770–778.

Szegedy C, Liu W, Jia Y Q, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition （CVPR）, Boston: IEEE, 2015; pp.1–9.

Huang G, Liu Z, Van Der Maaten L, Weinberger K Q. Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition （CVPR）, Honolulu: IEEE, 2017; pp.2261–2269.

Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition （CVPR）, Las Vegas: IEEE, 2016; pp.2818–2826.

Howard A G, Zhu M L, Chen B, Kalenichenko D, Wang W J, Weyand T, et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv, 2017; arXiv: 1704.04861.

Chollet F. Xception: Deep Learning with Depthwise Separable Convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition （CVPR）, Honolulu: IEEE, 2017; pp.1800–1807.

Mohanty S P, Hughes D P, Salathé M. Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 2016; 7: 1419.

Rangarajan, A K, Purushothaman R, Ramesh A. Tomato crop disease classification using pre-trained deep learning algorithm. Procedia Computer Science, 2018; 133: 1040–1047.

Durmus H, Gunes E O, Kirci M. Disease detection on the leaves of the tomato plants by using deep learning. In: 2017 6th International Conference on Agro-Geoinformatics, Fairfax: IEEE, 2017; pp.1–5.

Zhang K K, Wu Q F, Liu A W, Meng X Y. Can deep learning identify tomato leaf disease? Advances in Multimedia, 2018; 2018: 6710865.

Ahmad I, Hamid M, Yousaf S, Shah S T, Ahmad M O. Optimizing pretrained convolutional neural networks for tomato leaf disease detection. Complexity, 2020; 2020: 8812019.

Yan Q, Yang B H, Wang W Y, Wang B, Chen P, Zhang J. Apple leaf diseases recognition based on an improved convolutional neural network. Sensors, 2020; 20（12）: 3535.

Liu B, Zhang Y, He D J, Li Y X. Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry, 2018; 10（1）: 11.

Zhang S W, Zhang S B, Zhang C L, Wang X F, Shi Y. Cucumber leaf disease identification with global pooling dilated convolutional neural network. Computers and Electronics in Agriculture, 2019; 162: 422–430.

Agarwal M, Singh A, Arjaria S, Sinha A, Gupta S. ToLeD: Tomato leaf disease detection using Convolution Neural Network. Procedia Computer Science, 2020; 167: 293–301.

Agarwal M, Gupta S K, Biswas K K. Development of efficient CNN model for Tomato crop disease identification. Sustainable Computign:Informatics and Systems, 2020; 28: 100407.

Geetharamani G, Arun Pandian J. Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Computers & Electrical Engineering, 2019; 76: 323–338.

Li Yang, Nie J, Chao X W. Do we really need deep CNN for plant diseases identification? Computers and Electronics in Agriculture, 2020; 178: 105803.

Huang G, Liu Z, Van Der Maaten L, Weinberger K Q. Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition （CVPR）, Honolulu: IEEE, 2017; pp.2261–2269.

Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. International Conference on Machine Learning （ICML'15）, 2015; 448–456.

Ayan E, Erbay H, Varcin F. Crop pest classification with a genetic algorithm-based weighted ensemble of deep convolutional neural networks. Computers and Electronics in Agriculture, 2020; 179: 105809.

Saleem M H, Potgieter J, Arif K M. Plant disease detection and classification by deep learning. Plants, 2019; 8（11）: 468.

Iandola F N, Han S, Moskewicz M W, Ashraf K, Dally W J, Keutzer K. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. arXiv, 2016; arXiv: 1602.07360v4.

Hang J, Zhang D X, Chen P, Zhang J, Wang B. Classification of plant leaf diseases based on improved convolutional neural network. Sensors, 2019; 19（19）: 4161.

Wu Q F, Chen Y P, Meng J. DCGAN-based data augmentation for tomato leaf disease identification. IEEE Access, 2020; 8: 98716–98728.

Singh G B, Rani R, Sharma N, Kakkar D. Identification of tomato leaf diseases using deep Convolutional Neural Networks. International Journal of Agricultural and Environmental Information Systems, 2021; 12（4）: oa3.




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