A comparative analysis of deep learning models for cucumber disease classification using transfer learning
Keywords:cucumber disease classification, deep learning, digital agriculture, plant disease, transfer learning
Increasing agricultural productivity continues to be a major challenge for society due to the rapid growth of the global human population and economic prosperity. However, improving agricultural productivity requires proper identification and minimization of diseases that degrade both the quality and quantity of the crops. The scientific community has stressed that the use of recent technologies such as deep learning, the internet of things, computer vision, etc. are vital to address various challenges in the agriculture sector. Furthermore, the use of computer vision to automatically identify diseases is growing in popularity. This paper provides a comparative analysis of six pre-trained deep learning models, namely VGG16, VGG19, ResNet50, ResNet101, InceptionV3, and Xception, for disease detection in cucumber plants. The pre-trained models are fine-tuned using transfer learning and evaluated using different metrics such as training accuracy, testing accuracy, and the number of epochs. The results obtained demonstrate that VGG16, despite being the smallest model in terms of the number of layers, outperforms the rest of the models in all of the evaluation metrics. The VGG16 models obtain testing accuracy of 98% and training accuracy of 99.91% while being trained for 8 epochs. In addition, it is observed that models with a larger number of layers, such as ResNet50 and ResNet101, exhibit fluctuations in accuracy while training due to the execution of fairly large models on a comparatively small dataset. However, InceptionV3 and Xception, despite having a greater number of layers, perform better than ResNet models due to the presence of Inception modules which are better equipped to detect different-sized targets. The findings of this study may be utilized to optimize the best-performing models for disease classification in other plants, and the fine-tuned VGG16 model can be integrated with mobile devices for real-time disease classification.
Atila, Ü., Uçar, M., Akyol, K., & Uçar, E. (2021). Plant leaf disease classification using EfficientNet deep learning model. Ecological Informatics, 61, 101182. https://doi.org/10.1016/j.ecoinf.2020.101182
Bao, W., Huang, X., Hu, G., & Liang, D. (2021). Identification of maize leaf diseases using improved convolutional neural network. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 37(6), 160-167. https://doi.org/10.11975/j.issn.1002-6819.2021.06.020
Becherer, N., Pecarina, J., Nykl, S., & Hopkinson, K. (2019). Improving optimization of convolutional neural networks through parameter fine-tuning. Neural Computing and Applications, 31(8), 3469-3479. https://doi.org/10.1007/s00521-017-3285-0
Bhatt, P., Sarangi, S., Shivhare, A., Singh, D., & Pappula, S. (2019). Identification of diseases in corn leaves using convolutional neural networks and boosting. In ICPRAM 2019 - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods, 894–899. https://doi.org/10.5220/0007687608940899
Bosilj, P., Aptoula, E., Duckett, T., & Cielniak, G. (2020). Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture. Journal of Field Robotics, 37(1), 7-19. https://doi.org/10.1002/rob.21869
Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 1800-1807. https://doi.org/10.1109/CVPR.2017.195
Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009, June). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition (pp. 248-255). Ieee.
Dhaka, V. S., Meena, S. V., Rani, G., Sinwar, D., Ijaz, M. F., & Woźniak, M. (2021). A survey of deep convolutional neural networks applied for prediction of plant leaf diseases. Sensors, 21(14), 4749. https://doi.org/10.3390/s21144749
Duggal, N. (2022, Dec 21). Top 10 Python Libraries for Data Science for 2023. Simplilearn. Retrived form https://www.simplilearn.com/difference-between-descriptive-inferential-statistics-article
Fan, X., Luo, P., Mu, Y., Zhou, R., Tjahjadi, T., & Ren, Y. (2022). Leaf image based plant disease identification using transfer learning and feature fusion. Computers and Electronics in Agriculture, 196, 106892. https://doi.org/10.1016/j.compag.2022.106892
Ganatra, N., & Patel, A. (2020). Performance Analysis Of Fine-Tuned Convolutional Neural Network Models For Plant Disease Classification. International Journal of Control and Automation, 13(3), 293-305.
Hassan, S. M., Maji, A. K., Jasiński, M., Leonowicz, Z., & Jasińska, E. (2021). Identification of plant-leaf diseases using cnn and transfer-learning approach. Electronics (Switzerland), 10(12), 1388. https://doi.org/10.3390/electronics10121388
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 770-778. https://doi.org/10.1109/CVPR.2016.90
Hussain, R., Karbhari, Y., Ijaz, M. F., Woźniak, M., Singh, P. K., & Sarkar, R. (2021). Revise-net: Exploiting reverse attention mechanism for salient object detection. Remote Sensing, 13(23), 4941. https://doi.org/10.3390/rs13234941
Jadhav, S., & Garg, B. (2022). Comprehensive Review on Machine Learning for Plant Disease Identification and Classification with Image Processing. In Proceedings of International Conference on Intelligent Cyber-Physical Systems. https://doi.org/10.1007/978-981-16-7136-4_20
Jin, X. B., Yu, X. H., Wang, X. Y., Bai, Y. T., Su, T. L., & Kong, J. L. (2020). Deep learning predictor for sustainable precision agriculture based on internet of things system. Sustainability, 12(4). https://doi.org/10.3390/su12041433
Kumar, M., Kumar, A., & Palaparthy, V. S. (2021). Soil Sensors-Based Prediction System for Plant Diseases Using Exploratory Data Analysis and Machine Learning. IEEE Sensors Journal, 21(16), 17455-17468. https://doi.org/10.1109/JSEN.2020.3046295
Kundu, N., Rani, G., Dhaka, V. S., Gupta, K., Nayak, S. C., Verma, S., ... & Woźniak, M. (2021). Iot and interpretable machine learning based framework for disease prediction in pearl millet. Sensors, 21(16), 5386. https://doi.org/10.3390/s21165386
Ma, L., Shuai, R., Ran, X., Liu, W., & Ye, C. (2020). Combining DC-GAN with ResNet for blood cell image classification. Medical and Biological Engineering and Computing, 58(6), 1251-1264. https://doi.org/10.1007/s11517-020-02163-3
Moawed, M. M. (2016). Evaluation of morphological and anatomical characters for discrimination and verification of some Medicago sativa (L.) Cultivars. Indian Journal of Agricultural Research, 50(2), 183-192. https://doi.org/10.18805/ijare.v50i2.9589
Nagasubramanian, G., Sakthivel, R. K., Patan, R., Sankayya, M., Daneshmand, M., & Gandomi, A. H. (2021). Ensemble Classification and IoT-Based Pattern Recognition for Crop Disease Monitoring System. IEEE Internet of Things Journal, 8(16), 12847-12854. https://doi.org/10.1109/JIOT.2021.3072908
Naveen, P., & Sivakumar, P. (2021). A deep convolution neural network for facial expression recognition. Journal of Current Science and Technology, 11(3), 402-410. https://doi.org/10.14456/jcst.2021.40
Negm, K. (2020). Cucumber plant diseases dataset. Retrieved form https://www.kaggle.com/datasets/kareem3egm/cucumber-plant-diseases-dataset
Paymode, A. S., & Malode, V. B. (2022). Transfer Learning for Multi-Crop Leaf Disease Image Classification using Convolutional Neural Network VGG. Artificial Intelligence in Agriculture, 6, 23-33. https://doi.org/10.1016/j.aiia.2021.12.002
Press Information Bureau Government of India Ministry of Commerce & Industry. (2022). Retrived form https://pib.gov.in/newsite/PrintRelease.aspx?relid=229987
Saleem, M. H., Potgieter, J., & Arif, K. M. (2020). Plant disease classification: A comparative evaluation of convolutional neural networks and deep learning optimizers. Plants, 9(10), 1-17. https://doi.org/10.3390/plants9101319
Schapire, R. E. (2013). Explaining adaboost. In Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik (pp. 37-52). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-41136-6_5
Shagun, K. (2021). Agri share in GDP hit 20% after 17 years: Economic Survey. Down to Earth. Retrieved form https://www.downtoearth.org.in/news/agriculture/agri-share-in-gdp-hit-20-after-17-years-economic-survey-75271
Sharma, M., Kumar, C. J., & Deka, A. (2022). Early diagnosis of rice plant disease using machine learning techniques. Archives of Phytopathology and Plant Protection, 55(3), 259-283. https://doi.org/10.1080/03235408.2021.2015866
Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings.
Singh, U. P., Chouhan, S. S., Jain, S., & Jain, S. (2019). Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease. IEEE Access, 7, 43721-43729. https://doi.org/10.1109/ACCESS.2019.2907383
Sinha, A., Shrivastava, G., & Kumar, P. (2019). Architecting user-centric internet of things for smart agriculture. Sustainable Computing: Informatics and Systems, 23, 88-102. https://doi.org/10.1016/j.suscom.2019.07.001
Subramanian, M., Shanmugavadivel, K., & Nandhini, P. S. (2022). On fine-tuning deep learning models using transfer learning and hyper-parameters optimization for disease identification in maize leaves. Neural Computing and Applications, 34. https://doi.org/10.1007/s00521-022-07246-w
Suwanagul, D., Kokaew, J., & Suwanagul, A. (2013). Diversity of causal fungi in weed diseases and potential use as a biological weed control for vegetable plots in Thailand. Rangsit Journal of Arts and Sciences, 3(1), 39-43.
Syed-Ab-Rahman, S. F., Hesamian, M. H., & Prasad, M. (2022). Citrus disease detection and classification using end-to-end anchor-based deep learning model. Applied Intelligence, 52(1), 927-938. https://doi.org/10.1007/s10489-021-02452-w
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9). https://doi.org/10.1109/CVPR.2015.7298594
Trading Economics. (n.d.). India - Employment in agriculture (% of total employment). Reteived form https://tradingeconomics.com/india/employment-in-agriculture-percent-of-total-employment-wb-data.html
Zeng, Q., Ma, X., Cheng, B., Zhou, E., & Pang, W. (2020). GANS-based data augmentation for citrus disease severity detection using deep learning. IEEE Access, 8, 172882-172891. https://doi.org/10.1109/ACCESS.2020.3025196
Zhou, C., Zhou, S., Xing, J., & Song, J. (2021). Tomato Leaf Disease Identification by Restructured Deep Residual Dense Network. IEEE Access, 9, 28822-28831. https://doi.org/10.1109/ACCESS.2021.3058947
How to Cite
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.