Spring Onion Disease Detection and Treatment Recommendations
DOI:
https://doi.org/10.65141/ject.v1i2.n1Keywords:
disease detection, Android-based, convolutional neural network, sequential model, functionality, reliability, usefulnessAbstract
Spring onion is a delicate crop that demands much attention during its cultivation. Diseases such as purple blotch and leaf blight affect spring onion crops and, in any case, prevention of these diseases is rather complicated to detect. The study aimed to diagnose the diseases correctly and make suitable recommendations on the treatment needed. The researchers conducted a focused group discussion among spring onion farmers in Davao del Sur as a basis for the Android app that can identify spring onion disease and offer recommendations. In developing the app, the researcher used Google Colab for dataset training. The technique used in choosing the survey participants is simple purposive random sampling and a self-constructed checklist based on the ISO 9124 Likert scale was utilized to rate the app's functionality, reliability, and usability. The app is ideal for small and big farmlands, especially in regions without an internet connection, and during an experimental test, it gained an accuracy of 90% in the purple blotch and 93% in leaf blightcaptured crop diseases within a three-inch distance capture. The significant results of this study include the application's ability to detect two types of diseases, namely purple blotch and leaf blight, and its ability to provide personalized treatments, such as recommendations, chemical treatments, and care tips, based on the specific disease detected. The app's contribution to the farming community is its ability to detect crop diseases early, simplify disease detection techniques, increase harvests, decrease chemical use, and prevent minor spring onion problems that could result in major outbreaks and damage large farmlands.
References
Abbas, H. M. T., Shakoor, U., Khan, M. J., Ahmed, M., & Khurshid, K. (2019). Automated sorting and grading of agricultural products based on image processing. In 2019 8th International Conference on Information and Communication Technologies (ICICT) (pp. 78–81). IEEE. https://doi.org/10.1109/icict47744.2019.9001971
Ashok, V., & Vinod, D. S. (2021). A novel fusion of deep learning and android application for real-time mango fruits disease detection. In S. C. Satapathy, V. Bhateja, B. Janakiramaiah, & Y.-W. Chen (Eds.), Intelligent System Design: Proceedings of Intelligent System Design: INDIA 2019 (pp. 781–791). Springer. https://doi.org/10.1007/978-981-15-5400-1_74
Basallote, N. A., Gardones, M. A., Mantilla, C. J., Masaya, D. A., Sol, J. T., Soriano, A., Beano, M. G., Sigue, A., Capuno, M. E. A., & Medina, O. (2022). IoT-based growth analysis of red onion in controlled hydroponic environment using electroculture. In TENCON 2022 – 2022 IEEE Region 10 Conference (TENCON). https://doi.org/10.1109/tencon55691.2022.9977614
Bonik, C. C., Akter, F., Rashid, M. H., & Sattar, A. (2023, January). A convolutional neural network based potato leaf diseases detection using sequential model. In 2023 International Conference for Advancement in Technology (ICONAT) (pp. 1–6). IEEE. https://doi.org/10.1109/iconat57137.2023.10080063
BP-23. (2001). Diagnosis and control of onion diseases (Rev. 5/01) [Purdue Extension publication]. Purdue University, Cooperative Extension Service. Retrieved from https://www.extension.purdue.edu/extmedia/bp/bp-23-w.html
Cavan, R., Panaligan, N., & Sobejana, N. (2019a). Android-based Eucheuma species recognition and disease detection using TensorFlow API. SSRN Electronic Journal.
Cavan, R., Panaligan, N., & Sobejana, N. (2019b). Development of rice-based information and support system with simulation model for rice diseases. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3717452
Chi, J., Liu, Y., Wang, V., & Yan, J. (2022). Performance analysis of three kinds of neural networks in the classification of mask images. Journal of Physics: Conference Series, International Symposium on Artificial Intelligence and Intelligent Manufacturing (AIM 2021), 2181(1), Article 012032. https://doi.org/10.1088/1742-6596/2181/1/012032
Dawn, N., Ghosh, T., Ghosh, S., Saha, A., Mukherjee, P., Sarkar, S., Guha, S., & Sanyal, T. (2023). Implementation of artificial intelligence, machine learning, and Internet of Things (IoT) in revolutionizing agriculture: A review on recent trends and challenges. International Journal of Experimental Research and Review, 30, 190–218. https://doi.org/10.52756/ijerr.2023.v30.018
Del Ponte, E. M., Pethybridge, S. J., Bock, C. H., Michereff, S. J., Machado, F. J., & Spolti, P. (2017). Standard area diagrams for aiding severity estimation: Scientometrics, pathosystems, and methodological trends in the last 25 years. Phytopathology, 107(10), 1161–1174.
Evin, B., Meyer, S., Schuh, C., Haugen, S., Halvorson, J., Chapara, V., Arens, A., & Friskop, A. (2020). Management of leaf rust and stripe rust in hard red spring wheat at different timings of disease onset. Plant Health Progress, 21(4), 306–311. https://doi.org/10.1094/php-06-20-0055-rs
Gai, J., Xiang, L., & Tang, L. (2021). Using a depth camera for crop row detection and mapping for under-canopy navigation of agricultural robotic vehicle. Computers and Electronics in Agriculture, 188, Article 106301. https://doi.org/10.1016/j.compag.2021.106301
Gao, Y., Xue, X., Qin, G., Li, K., Liu, J., Zhang, Y., & Li, X. (2024). Application of machine learning in automatic image identification of insects — a review. Ecological Informatics, 80, Article 102539. https://doi.org/10.1016/j.ecoinf.2024.102539
Ibrahim, M. (2024, March 13). A deep dive into learning curves in machine learning. Weights & Biases. https://wandb.ai/mostafaibrahim17/ml-articles/reports/A-Deep-Dive-Into-Learning-Curves-in-Machine-Learning--Vmlldzo0NjA1ODY0
Jaswal, D., V., S., & Soman, K. (2014). Image classification using convolutional neural networks. International Journal of Scientific and Engineering Research, 5(6), 1661–1668.
Kamble, J. K. (2018). Plant disease detector. In 2018 International Conference on Advances in Communication and Computing Technology (ICACCT) (pp. 97–101). https://doi.org/10.1109/icacct.2018.8529612
Khaki, S., Wang, L., & Archontoulis, S. V. (2020). A CNN-RNN framework for crop yield prediction. Frontiers in Plant Science, 10. https://doi.org/10.3389/fpls.2019.01750
Kim, W. S., Lee, D. H., & Kim, Y. J. (2020). Machine vision-based automatic disease symptom detection of onion downy mildew. Computers and Electronics in Agriculture, 168, Article 105099. https://doi.org/10.1016/j.compag.2019.105099
Krishnaswamy Rangarajan, A., & Purushothaman, R. (2020). Disease classification in eggplant using pre-trained VGG16 and MSVM. Scientific Reports, 10(1), Article 1–11. https://doi.org/10.1038/s41598-020-59108-x
Li, C., Schmidt, N. E., & Gitaitis, R. (2010). Detection of onion postharvest diseases by analyses of headspace volatiles using a gas sensor array and GC-MS. LWT — Food Science and Technology, 44(4), 1019–1025. https://doi.org/10.1016/j.lwt.2010.11.036
Panaligan, N. R. (2022). Automated fish fingerlings counter system: An evaluation of image segmentation algorithms in overlapping objects. International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), 11(1). https://doi.org/10.17148/ijarcce.2022.11103
Schwartz, H. F., & Mohan, S. K. (2007). Compendium of onion and garlic diseases and pests (2nd ed.). American Phytopathological Society (APS Press).
Sharma, R., Das, S., Gourisaria, M. K., Rautaray, S. S., & Pandey, M. (2020). A model for prediction of paddy crop disease using CNN. In Advances in Intelligent Systems and Computing (pp. 533–543). https://doi.org/10.1007/978-981-15-2414-1_54
Siddiqua, A., Kabir, M. A., Ferdous, T., Ali, I. B., & Weston, L. A. (2022). Evaluating plant disease detection mobile applications: Quality and limitations. Agronomy, 12(8), 1869. https://doi.org/10.3390/agronomy12081869
Zaki, M. A., Narejo, S., Ahsan, M., Zai, S., Anjum, M. R., & Din, N. U. (2021). Image-based onion disease (purple blotch) detection using deep convolutional neural network. International Journal of Advanced Computer Science and Applications, 12(5). https://doi.org/10.14569/ijacsa.2021.0120556
