Corn Disease Detection Using Transfer Learning

Yıl 2023, , 387 - 393, 15.10.2023

Cevher Özden

https://doi.org/10.34248/bsengineering.1322907

Cited By: 1

Öz

Detecting plant disease is a complicated yet important task to enable sustainable production in agriculture. Especially, early and on-field disease detection provides an opportunity to producers to take necessary precautions before it causes dramatic losses. Corn is one of the most important agricultural products for many countries around the world. It constitutes the main nutrient intake for large populations. This study examines and analyzes the applicability of the pretrained models in corn disease detection. A number of well-known pretrained models including Xception, ResNet50, VGG16, EfficientNetB0, MobileNet and InceptionV3 have been employed for this purpose. SMOTE is employed to solve the imbalanced data and resulting bias problem, which is a common problem in plant disease dataset. The study results indicate that SMOTE provides a good solution to the imbalanced data problem and MobileNet, VGG16 and Xception can be used as base models to develop AI applications to detect corn diseases.

Anahtar Kelimeler

Disease detection, Classification, Transfer learning, Imbalanced data

Kaynakça

• Abbas A, Jain S, Gour M, Vankudothu S. 2021. Tomato plant disease detection using transfer learning with C-GAN synthetic images. Comput Electron Agri, 187: 106279. 10.1016/j.compag.2021.106279.
• Bir P, Kumar R, Singh G. 2020. Transfer learning based tomato leaf disease detection for mobile applications. Proceedings of 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON), Oct 2-4, 2020, Galgotias University, Greater Noida, UP, India, pp: 34-39.
• Chawla N, Bowyer K, Hall L, Kegelmeyer W. 2002. SMOTE: Synthetic minority over-sampling technique. J Artif Intell Res, 16: 321-357. 10.1613/jair.953.
• Chen J, Zhang D, Nanehkaran Y, Li D. 2020. Detection of rice plant diseases based on deep transfer learning. J Sci Food Agri, 2020: 100. 10.1002/jsfa.10365.
• Hasan M, Tanawala B, Patel K. 2019. Deep learning precision farming: Tomato leaf disease detection by transfer learning. SSRN Electronic J, 2019: 1-5. 10.2139/ssrn.3349597.
• Hughes DP, Salathé M. 2015. An open access repository of images on plant health to enable the development of mobile disease diagnostics through machine learning and crowdsourcing. URL: https://arxiv.org/abs/1511.08060 (accessed date: March 23, 2022).
• Feng L, Wu B, He Y, Zhang C. 2021. Hyperspectral imaging combined with deep transfer learning for rice disease detection. Frontiers Plant Sci, 12: 693521. 10.3389/fpls.2021.693521.
• Kathiresan G, Anirudh M, Mathimariappan N, Ramamurthy K. 2021. Disease detection in rice leaves using transfer learning techniques. J Physics, 1911: 012004. 10.1088/1742-6596/1911/1/012004.
• Khasawneh N, Faouri E, Fraiwan M. 2022. Automatic detection of tomato diseases using deep transfer learning. Applied Sci, 12: 8467. 10.3390/app12178467.
• Mukti I, Biswas D. 2019. Transfer learning based plant diseases detection using ResNet50. Proceedings of International Conference on Electrical Information and Communication Technology (EICT), Oct 2-4, 2020, Greater Noida, UP, India, pp: 1-6. 10.1109/EICT48899.2019.9068805.
• Paymode AS, Malode VB. 2022. Transfer learning for multi-crop leaf disease image classification using convolutional neural network VGG. Artificial Intell Agric, 2022: 23-33. 10.1016/j.aiia.2021.12.002.
• Reddy T, Rekha K. 2021. Deep leaf disease prediction framework (dldpf) with transfer learning for automatic leaf disease detection. 1408-1415. Proceedings of 5th International Conference on Computing Methodologies and. Communication (ICCMC 2021), 8 – 10 April 2021, Erode, India, pp: 1-6.
• Shahoveisi F, Taheri GH, Shahabi S. 2023. Application of image processing and transfer learning for the detection of rust disease. Sci Rep, 13: 5133. https://doi.org/10.1038/s41598-023-31942-9.
• TUIK. 2022. Plant Production Statistics. URL: https://data.tuik.gov.tr/Bulten/Index?p=Bitkisel-Uretim-Istatistikleri-2022-45504. (accessed data: August 2, 2023).
• Vallabhajosyula S, Sistla V, Kolli V. K. K. 2021. Transfer learning-based deep ensemble neural network for plant leaf disease detection. J Plant Diseases Protect, 129: 1-14. 10.1007/s41348-021-00465-8.