Android-based Corn Disease Automated Recognition Tool Using Convolutional Neural Network

Grace Dipiao Bulawit; Thelma Domingo Palaoag; Benjamin Enriquez Bulawit Jr.

doi:10.52756/ijerr.2023.v30.021

Authors

Grace Dipiao Bulawit Isabela State University-Echague Campus, San Fabian, Echague, Isabela, 3309, Philippines https://orcid.org/0000-0001-7541-1590
Thelma Domingo Palaoag University of the Cordilleras, Baguio City, 2600, Philippines https://orcid.org/0000-0002-5474-7260
Benjamin Enriquez Bulawit Jr. Isabela State University-Angadanan Campus, Centro 3, Angadanan, Isabela, 3307, Philippines https://orcid.org/0000-0002-6783-3715

DOI:

https://doi.org/10.52756/ijerr.2023.v30.021

Keywords:

Android application, convolutional neural network, corn diseases, ISO 25010, recognition tool, transfer learning

Abstract

One of the most significant crops in the world today, corn, is under attack from several diseases. Typically, visual review and evaluation are used to identify diseases, but they are regarded as being unreliable. Corn farmers needed an automated disease recognition tool to identify different diseases that affect corn. In this study, a pre-trained convolutional neural network (CNN) was employed to create an android-based recognition tool for recognizing corn diseases. A dataset of healthy corn leaves and three (3) maize diseases—common rust, gray leaf spot, and northern leaf blight—was created using an open-source dataset of Plant Village and field images. The researchers used data augmentation, trained the generated neural network, and put it to the test. The dataset was created using a 75–25 split, trained using the transfer learning concept, then fine-tuned using the VGG-16 CNN model. The CNN model was trained using Tensor flow Keras. The model can identify corn diseases, as evidenced by its accuracy of 93.42 percent and F1-score of 93.53 percent. A mobile application employing the Dynamic System Development methodology (DSDM) was created using the methodology. The trained CNN model file was used to create the android application, which serves as a tool for identifying maize diseases. The produced application was deemed to be extremely compliant according to the participants' assessment of the android application using the ISO 25010 software quality standard, with an overall weighted mean of 4.22. The results show that the participants recognized the CDARS application's potential to offer farmers important information and as an automated corn disease recognition tool that could promote more sustainable and secure food production.

References

Afifi, A., Alhumam, A., & Abdelwahab, A. (2021). Convolutional neural network for automatic identification of plant diseases with limited data. Plants, 10(1), 1–16. https://doi.org/10.3390/plants10010028

Ahmed, A. A., & Harshavardhan Reddy, G. (2021). A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Learning. AgriEngineering, 3(3), 478–493. https://doi.org/10.3390/agriengineering3030032

Anandhakrishnan, T., & Jaisakthi, S. M. (2022). Deep Convolutional Neural Networks for image based tomato leaf disease detection. Sustainable Chemistry and Pharmacy, 30, 100793. https://doi.org/10.1016/J.SCP.2022.100793

Baskin, C. C. (2022). Effects of climate change on annual crops: the case of maize production in Africa. Plant Regeneration from Seeds: A Global Warming Perspective, 213–228. https://doi.org/10.1016/B978-0-12-823731-1.00020-2

Bebber, D. P. (2019). Climate change effects on Black Sigatoka disease of banana. Philosophical Transactions of the Royal Society B: Biological Sciences, 374(1775). https://doi.org/10.1098/RSTB.2018.0269

Chakrabortya, S. ., Tiedemann, VP., & Teng, S. (2000). Environmental Pollution Keynote review Climate change: potential impact on plant diseases. Environmental Pollution, 108(3), 317–326.

Chen, J., Chen, J., Zhang, D., Sun, Y., & Nanehkaran, Y. A. (2020). Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture, 173, 105393. https://doi.org/10.1016/j.compag.2020.105393

De Oliveira, J. R. C. P., & Romero, R. A. F. (2018). Transfer Learning Based Model for Classification of Cocoa Pods. Proceedings of the International Joint Conference on Neural Networks, 2018-July, 1–6. https://doi.org/10.1109/IJCNN.2018.8489126

Fones, H. N., Bebber, D. P., Chaloner, T. M., Kay, W. T., Steinberg, G., & Gurr, S. J. (2020). Threats to global food security from emerging fungal and oomycete crop pathogens. Springer Nature. In Nature Food, 1(6), 332–342. https://doi.org/10.1038/s43016-020-0075-0

Garrett, K. A., Bebber, D. P., Etherton, B. A., Gold, K. M., Plex Sulá, A. I., & Selvaraj, M. G. (2022). Climate Change Effects on Pathogen Emergence: Artificial Intelligence to Translate Big Data for Mitigation. Annual Review of Phytopathology, 60, 357–378. https://doi.org/10.1146/ANNUREV-PHYTO-021021-042636

Gasparetto, A., Ressi, D., Bergamasco, F., Pistellato, M., Cosmo, L., Boschetti, M., Ursella, E., & Albarelli, A. (2018). Cross-Dataset Data Augmentation for Convolutional Neural Networks Training. Proceedings - International Conference on Pattern Recognition, 2018 (August), 910–915. https://doi.org/10.1109/ICPR.2018.8545812

Mishra, S., Sachan, R., & Rajpal, D. (2020). Deep Convolutional Neural Network based Detection System for Real-time Corn Plant Disease Recognition. Procedia Computer Science, 167, 2003–2010. ttps://doi.org/10.1016/j.procs.2020.03.236

Mukti, I. Z., & Biswas, D. (2019). Transfer Learning Based Plant Diseases Detection Using ResNet50. 2019 4th International Conference on Electrical Information and Communication Technology, EICT 2019, December, pp. 1–6. https://doi.org/10.1109/EICT48899.2019.9068805

Ngugi, L. C., Abelwahab, M., & Abo-Zahhad, M. (2021). Recent advances in image processing techniques for automated leaf pest and disease recognition – A review. Information Processing in Agriculture, 8(1), 27–51. https://doi.org/10.1016/j.inpa.2020.04.004

Oraño, J. F. V., Maravillasb, E. A., & Aliac, C. J. G. (2020). Classification of Jackfruit Fruit Damage Using Color Texture Features and Backpropagation Neural Network. International Journal on Advanced Science, Engineering and Information Technology, 10(5), 1813–1820. https://doi.org/10.18517/ijaseit.10.5.8508

Rosegrant, M. W., Perez, N., Pradesha, A., & Thomas, T. S. (2015). The Economywide Impacts of Climate Change on Philippine Agriculture. International Food Policy Research Institute, September, pp. 1–12.

Rusdiana, L. (2018). Dynamic Systems Development Method dalam Membangun Aplikasi Data Kependudukan Pada Kelurahan Rantau Pulut. TRANSFORMTIKA, 16(1), 84–90.

Sudana, O., Witarsyah, D., Putra, A., & Raharja, S. (2020). Mobile application for identification of coffee fruit maturity using digital image processing. International Journal on Advanced Science, Engineering and Information Technology, 10(3), 980–986. https://doi.org/10.18517/ijaseit.10.3.11135

Tian, J., Zhang, Y., Wang, Y., Wang, C., Zhang, S., & Ren, T. (2019). A Method of Corn Disease Identification Based on Convolutional Neural Network. Proceedings - 2019 12th International Symposium on Computational Intelligence and Design, ISCID 2019, 1, 245–248. https://doi.org/10.1109/ISCID.2019.00063

Tripathi, A., Tripathi, D. K., Chauhan, D. K., Kumar, N., & Singh, G. S. (2016). Paradigms of climate change impacts on some major food sources of the world: A review on current knowledge and future prospects. Agriculture, Ecosystems & Environment, 216, 356–373. https://doi.org/10.1016/J.AGEE.2015.09.034

Ummenhofer, C. C., Xu, H., Twine, T. E., Girvetz, E. H., McCarthy, H. R., Chhetri, N., & Nicholas, K. A. (2015). How climate change affects extremes in maize and wheat yield in two cropping regions. Journal of Climate, 28(12), 4653–4687. https://doi.org/10.1175/JCLI-D-13-00326.1

Valdoria, J. C., Caballeo, A. R., Fernandez, B. I. D., & Condino, J. M. M. (2019). IDahon: An Android Based Terrestrial Plant Disease Detection Mobile Application Through Digital Image Processing Using Deep Learning Neural Network Algorithm. Proceedings of 2019 4th International Conference on Information Technology: Encompassing Intelligent Technology and Innovation Towards the New Era of Human Life, InCIT 2019, pp. 94–98. https://doi.org/10.1109/INCIT.2019.8912053

Wason, R. (2018). Deep learning: Evolution and expansion. Cognitive Systems Research, 52(August), 701–708. https://doi.org/10.1016/j.cogsys.2018.08.023

Xia, X., Wu, Y., Lu, Q., & Fan, C. (2019). Experimental study on crop disease detection based on deep learning. IOP Conference Series: Materials Science and Engineering, 569(5). https://doi.org/10.1088/1757-899X/569/5/052034