BNPL-Dataset: A New Benchmark Dataset for Visual Disease Detection of Barberry, Jujube, and Pomegranate Trees

Zarei, Jalaluddin; Khosravi, Mohammad Hossein

doi:10.22044/jadm.2024.14480.2552

Articles in Press

Document Type : Applied Article

Authors

Electrical and Computer Engineering Faculty, University of Birjand, Birjand, Iran.

10.22044/jadm.2024.14480.2552

Abstract

Agricultural experts try to detect leaf diseases in the shortest possible time. However, limitations such as lack of manpower, poor eyesight, lack of sufficient knowledge, and quarantine restrictions in the transfer of diseases to the laboratory can be acceptable reasons to use digital technology to detect pests and diseases and finally dispose of them. One of the available solutions in this field is using convolutional neural networks. On the other hand, the performance of CNNs depends on the large amount of data. While there is no suitable dataset for the native trees of South Khorasan province, this motivates us to create a suitable dataset with a large amount of data. In this article, we introduce a new dataset in 9 classes of images of Healthy Barberry leaves, Barberry Rust disease, Barberry Pandemis ribeana Tortricidae pest, Healthy Jujube leaves, Jujube Ziziphus Tingid disease, Jujube Parenchyma-Eating Butterfly pest, Healthy Pomegranate leaves, Pomegranate Aphis punicae pest, and Pomegranate Leaf-Cutting Bees pest and also check the performance of several well-known convolutional neural networks using all gradient descent optimizer algorithms on this dataset. Our most important achievement is the creation of a dataset with a high data volume of pests and diseases in different classes. In addition, our experiments show that common CNN architectures, along with gradient descent optimizers, have an acceptable performance on the proposed dataset. We call the proposed dataset ”Birjand Native Plant Leaves (BNPL) Dataset”.
It is available at the address https://kaggle.com/datasets/ec17162ca01825fb362419503cbc84c73d162bffe936952253ed522705228e06.

Keywords

Main Subjects

H.5. Image Processing and Computer Vision

References

[1] J. Ma, K. Du, F. Zheng, L. Zhang, Z. Gong, and Z. Sun, "A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network," Computers and Electronics in Agriculture, vol. 154, pp. 18-24, 2018.

[2] T. Sanida, D. Tsiktsiris, A. Sideris, and M. Dasygenis, "A heterogeneous implementation for plant disease identification using deep learning," Multimedia Tools and Applications, vol. 81, no. 11, pp. 15041-15059, 2022.

[3] Y. Zhao, C. Sun, X. Xu, and J. Chen, "RIC-Net: A plant disease classification model based on the fusion of Inception and residual structure and embedded attention mechanism," Computers and Electronics in Agriculture, vol. 193, no. 2, p. 106644, 2022.

[4] R. K. Singh, A. Tiwari, and R. K. Gupta, "Deep transfer modeling for classification of Maize Plant Leaf Disease," Multimedia Tools and Applications, vol. 81, no. 5, pp. 6051-6067, 2022.

[5] H. Afzaal, A. A. Farooque, A. W. Schumann, N. Hussain, A. McKenzie-Gopsill, T. Esau, F. Abbas and B. Acharya, "Detection of a Potato Disease (Early Blight) Using Artificial Intelligence," Remote Sensing, vol. 13, no. 3, p. 411, 2021.

[6] Ü. Atila, M. Uçar, K. Akyol, and E. Uçar, "Plant leaf disease classification using EfficientNet deep learning model," Ecological Informatics, vol. 61, no. 1, p. 101182, 2021.

[7] A. Elhassouny and F. Smarandache, "Smart mobile application to recognize tomato leaf diseases using Convolutional Neural Networks," in 2019 International Conference of Computer Science and Renewable Energies (ICCSRE), pp. 1-4, 2019.

[8] F. Salimian Najafabadi and M. T. Sadeghi, "AgriNet: a New Classifying Convolutional Neural Network for Detecting Agricultural Products’ Diseases," Journal of AI and Data Mining, vol. 10, no. 2, pp. 285-302, 2022.

[9] A. Abbas, S. Jain, M. Gour, and S. Vankudothu, "Tomato plant disease detection using transfer learning with C-GAN synthetic images," Computers and Electronics in Agriculture, vol. 187, no. 8, p. 106279, 2021.

[10] A. Dey and S. Biswas, "Shot-ViT: Cricket Batting Shots Classification with Vision Transformer Network," International Journal of Engineering, vol. 37, no. 12, pp. 2463-2472, 2024.

[11] D. Hughes and M. Salathé, "An open access repository of images on plant health to enable the development of mobile disease diagnostics," arXiv preprint arXiv:1511.08060, 2015.

[12] D. Singh, N. Jain, P. Jain, P. Kayal, S. Kumawat, and N. Batra, "PlantDoc: a dataset for visual plant disease detection," in Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, pp. 249-253, 2020.

[13] H. B. Prajapati, J. P. Shah, and V. K. Dabhi, "Detection and classification of rice plant diseases," Intelligent Decision Technologies, vol. 11, no. 3, pp. 357-373, 2017.

[14] T. Wiesner-Hanks, E. L. Stewart, N. Kaczmar, C. DeChant, H. Wu, R. J. Nelson, H. Lipson and M. A. Gore, "Image set for deep learning: field images of maize annotated with disease symptoms," BMC research notes, vol. 11, no. 1, pp. 1-3, 2018.

[15] J. G. A. Barbedo, L. V. Koenigkan, B. A. Halfeld-Vieira, R. V. Costa, K. L. Nechet, C. V. Godoy, M. L. Junior, F. R. A. Patricio, V. Talamini, L. G. Chitarra, S. A. S. Oliveira, A. K. N. Ishida, J. M. C. Fernandes, T. T. Santos, F. R. Cavalcanti, D. Terao and F. Angelotti, "Annotated Plant Pathology Databases for Image-Based Detection and Recognition of Diseases," IEEE Latin America Transactions, vol. 16, no. 6, pp. 1749-1757, 2018.

[16] X. Wu, C. Zhan, Y.-K. Lai, M.-M. Cheng, and J. Yang, "Ip102: A large-scale benchmark dataset for insect pest recognition," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 8787-8796, 2019.

[17] E. Moupojou, A. Tagne, F. Retraint, A. Tadonkemwa, D. Wilfried, H. Tapamo and M. Nkenlifack, "FieldPlant: A dataset of field plant images for plant disease detection and classification with deep learning," IEEE Access, vol. 11, pp. 35398-35410, 2023.

[18] H. Zeraatgar, G. Tavakkoli-Korghond, S. Z. Mousavi, N. Pourfateh, "Seedless barberry, limitations and damaging factors," Agricultural Research, Education and Extension Organization (AREEO), 2017. (In Persian)

[19] G. Tavakkoli-Korghond, H. Zeraatgar, K. Ghos, M. Yousefi, A. R. Rezaei-Gazik, M. R. Mirzaee, " Jujube Ziziphus Tingid, preliminary knowledge and available experiences for its control, " Journal of barberry and jujube, vol. 3, no. 1, pp. 22-29, 2021. (In Persian)

[20] V. Gonzalez-Huitron, J. A. León-Borges, A. E. Rodriguez-Mata, L. E. Amabilis-Sosa, B. Ramírez-Pereda, and H. Rodriguez, "Disease detection in tomato leaves via CNN with lightweight architectures implemented in Raspberry Pi 4," Computers and Electronics in Agriculture, vol. 181, no. 2, p. 105951, 2021.

[21] A. Ghosh, A. Sufian, F. Sultana, A. Chakrabarti, and D. De, "Fundamental concepts of convolutional neural network," Recent trends and advances in artificial intelligence and Internet of Things, pp. 519-567, 2020.

[22] S. M. R. Hashemi, H. Hassanpour, E. Kozegar, and T. Tan, "Cystoscopic image classification by unsupervised feature learning and fusion of classifiers," IEEE Access, vol. 9, pp. 126610-126622, 2021.

BNPL-Dataset: A New Benchmark Dataset for Visual Disease Detection of Barberry, Jujube, and Pomegranate Trees

References

References

Articles in Press, Accepted Manuscript Available Online from 24 September 2024

Articles in Press, Accepted Manuscript
Available Online from 24 September 2024