Optimized Deep Learning Model for Disease Prediction in Potato Leaves

Virendra Kumar Shrivastava; Chetan J Shelke; Aastik Shrivastava; Sachi Nandan Mohanty; Nonita Sharma

doi:10.4108/eetpht.9.4001

Authors

Virendra Kumar Shrivastava Alliance University
Chetan J Shelke Alliance University
Aastik Shrivastava Siemens Healthineers, India
Sachi Nandan Mohanty Vellore Institute of Technology University
Nonita Sharma Indira Gandhi Delhi Technical University for Women

DOI:

https://doi.org/10.4108/eetpht.9.4001

Keywords:

Deep Learning, Artificial Intelligence, Machine Learning, Deep Convolutional Neural Network, Optimized Deep Convolutional Neural Network Model, Disease Prediction

Abstract

Food crops are important for nations and human survival. Potatoes are one of the most widely used foods globally. But there are several diseases hampering potato growth and production as well. Traditional methods for diagnosing disease in potato leaves are based on human observations and laboratory tests which is a cumbersome and time-consuming task. The new age technologies such as artificial intelligence and deep learning can play a vital role in disease detection. This research proposed an optimized deep learning model to predict potato leaf diseases. The model is trained on a collection of potato leaf image datasets. The model is based on a deep convolutional neural network architecture which includes data augmentation, transfer learning, and hyper-parameter tweaking used to optimize the proposed model. Results indicate that the optimized deep convolutional neural network model has produced 99.22% prediction accuracy on Potato Disease Leaf Dataset.

Downloads

Download data is not yet available.

References

Iqbal, Z., Khan, M. A., Sharif, M., Shah, J. H., ur Rehman, M. H., & Javed, K. (2018). An automated detection and classification of citrus plant diseases using image processing techniques: A review. Computers and electronics in agriculture, 153, 12-32. DOI: https://doi.org/10.1016/j.compag.2018.07.032

https://www.statista.com/statistics/382174/global-potato-production/

Shrivastava, V. K., Shrivastava, A., Sharma, N., Mohanty, S. N., & Pattanaik, C. R. (2023). Deep learning model for temperature prediction: an empirical study. Modeling Earth Systems and Environment, 9(2), 2067-2080. DOI: https://doi.org/10.1007/s40808-022-01609-x

Batra, R., Shrivastava, V. K., & Goel, A. K. (2021). Anomaly Detection over SDN Using Machine Learning and Deep Learning for Securing Smart City. In Green Internet of Things for Smart Cities (pp. 191-204). CRC Press. DOI: https://doi.org/10.1201/9781003032397-13

Mohanty, S., Mishra, A., & Saxena, A. (2021). Medical data analysis using machine learning with KNN. In International Conference on Innovative Computing and Communications: Proceedings of ICICC 2020, Volume 2 (pp. 473-485). Springer Singapore. DOI: https://doi.org/10.1007/978-981-15-5148-2_42

Batra, R., Mahajan, M., Shrivastava, V. K., & Goel, A. K. (2021). Detection of COVID-19 using textual clinical data: a machine learning approach. Impact of AI and data science in response to coronavirus pandemic, 97-109. DOI: https://doi.org/10.1007/978-981-16-2786-6_5

Sumathy, B., Dadheech, P., Jain, M., Saxena, A., Hemalatha, S., Liu, W., & Nuagah, S. J. (2022). A Liver Damage Prediction Using Partial Differential Segmentation with Improved Convolutional Neural Network. Journal of Healthcare Engineering, 2022.

Saini, V., Rai, N., Sharma, N., & Shrivastava, V. K. (2022, December). A Convolutional Neural Network Based Prediction Model for Classification of Skin Cancer Images. In International Conference on Intelligent Systems and Machine Learning (pp. 92-102). Cham: Springer Nature Switzerland. DOI: https://doi.org/10.1007/978-3-031-35078-8_9

Sumathy, B., Dadheech, P., Jain, M., Saxena, A., Hemalatha, S., Liu, W., & Nuagah, S. J. (2022). A Liver Damage Prediction Using Partial Differential Segmentation with Improved Convolutional Neural Network. Journal of Healthcare Engineering, 2022. DOI: https://doi.org/10.1155/2022/4055491

Revanth Kumar, P., Katti, A., Nandan Mohanty, S., & Nath Senapati, S. (2022). A Deep Learning-Based Approach for an Automated Brain Tumor Segmentation in MR Images. In Pattern Recognition and Data Analysis with Applications (pp. 87-97). Singapore: Springer Nature Singapore. DOI: https://doi.org/10.1007/978-981-19-1520-8_7

Singhal, A., Phogat, M., Kumar, D., Kumar, A., Dahiya, M., & Shrivastava, V. K. (2022). Study of deep learning techniques for medical image analysis: A review. Materials Today: Proceedings, 56, 209-214. DOI: https://doi.org/10.1016/j.matpr.2022.01.071

Lalli, K., Shrivastava, V. K., & Shekhar, R. (2023, April). Detecting Copy Move Image Forgery using a Deep Learning Model: A Review. In 2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance technology Conference (ATCON-1) (pp. 1-7). IEEE. DOI: https://doi.org/10.1109/ICAIA57370.2023.10169568

Shrivastava, V. K., Shrivastava, A., Sharma, N., Mohanty, S. N., & Pattanaik, C. R. (2023). Deep learning model for temperature prediction: A case study in New Delhi. Journal of Forecasting. DOI: https://doi.org/10.1002/for.2966

Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685-695. DOI: https://doi.org/10.1007/s12525-021-00475-2

Emmert-Streib, F., Yang, Z., Feng, H., Tripathi, S., & Dehmer, M. (2020). An introductory review of deep learning for prediction models with big data. Frontiers in Artificial Intelligence, 3, 4. DOI: https://doi.org/10.3389/frai.2020.00004

Goodfellow, I., Bengio, Y., Courville, A., & Learning, D. (2016). Cambridge, MA.

Khalifa, N. E. M., Taha, M. H. N., Abou El-Maged, L. M., & Hassanien, A. E. (2021). Artificial intelligence in potato leaf disease classification: a deep learning approach. Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges, 63-79. DOI: https://doi.org/10.1007/978-3-030-59338-4_4

Sanjeev, K., Gupta, N. K., Jeberson, W., & Paswan, S. (2021). Early prediction of potato leaf diseases using ANN classifier. Oriental Journal of Computer Science and Technology, 13(2, 3), 129-134. DOI: https://doi.org/10.13005/ojcst13.0203.11

Rozaqi, A. J., & Sunyoto, A. (2020, November). Identification of disease in potato leaves using Convolutional Neural Network (CNN) algorithm. In 2020 3rd International Conference on Information and Communications Technology (ICOIACT) (pp. 72-76). IEEE. DOI: https://doi.org/10.1109/ICOIACT50329.2020.9332037

Barman, U., Sahu, D., Barman, G. G., & Das, J. (2020, July). Comparative assessment of deep learning to detect the leaf diseases of potato based on data augmentation. In 2020 International Conference on Computational Performance Evaluation (ComPE) (pp. 682-687). IEEE. DOI: https://doi.org/10.1109/ComPE49325.2020.9200015

Lee, T. Y., Yu, J. Y., Chang, Y. C., & Yang, J. M. (2020, February). Health detection for potato leaf with convolutional neural network. In 2020 Indo–Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN) (pp. 289-293). IEEE. DOI: https://doi.org/10.1109/Indo-TaiwanICAN48429.2020.9181312

Islam, M., Dinh, A., Wahid, K., & Bhowmik, P. (2017, April). Detection of potato diseases using image segmentation and multiclass support vector machine. In 2017 IEEE 30th canadian conference on electrical and computer engineering (CCECE) (pp. 1-4). IEEE. DOI: https://doi.org/10.1109/CCECE.2017.7946594

Tiwari, D., Ashish, M., Gangwar, N., Sharma, A., Patel, S., & Bhardwaj, S. (2020, May). Potato leaf diseases detection using deep learning. In 2020 4th international conference on intelligent computing and control systems (ICICCS) (pp. 461-466). IEEE. DOI: https://doi.org/10.1109/ICICCS48265.2020.9121067

Geetharamani, G., & Pandian, A. (2019). Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Computers & Electrical Engineering, 76, 323-338. DOI: https://doi.org/10.1016/j.compeleceng.2019.04.011

Liang, Q., Xiang, S., Hu, Y., Coppola, G., Zhang, D., & Sun, W. (2019). PD2SE-Net: Computer-assisted plant disease diagnosis and severity estimation network. Computers and electronics in agriculture, 157, 518-529. DOI: https://doi.org/10.1016/j.compag.2019.01.034

Kamal, K. C., Yin, Z., Wu, M., & Wu, Z. (2019). Depthwise separable convolution architectures for plant disease classification. Computers and electronics in agriculture, 165, 104948. DOI: https://doi.org/10.1016/j.compag.2019.104948

https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset

https://www.kaggle.com/code/hussainsalih/potato-disease-acc-98/data accessed on 1st March 2023.

Ioffe, S., & Szegedy, C. (2015, June). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning (pp. 448-456). pmlr.

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. DOI: https://doi.org/10.1145/3065386

Li, D., Wang, R., Xie, C., Liu, L., Zhang, J., Li, R., ... & Liu, W. (2020). A recognition method for rice plant diseases and pests video detection based on deep convolutional neural network. Sensors, 20(3), 578. DOI: https://doi.org/10.3390/s20030578

Bansal, P., Kumar, R., & Kumar, S. (2021). Disease detection in apple leaves using deep convolutional neural network. Agriculture, 11(7), 617. DOI: https://doi.org/10.3390/agriculture11070617

Bergstra, J., Bardenet, R., Bengio, Y., & Kégl, B. (2011). Algorithms for hyper-parameter optimization. Advances in neural information processing systems, 24.

Shrivastava, V. K., Kumar, A., Shrivastava, A., Tiwari, A., Thiru, K., & Batra, R. (2021, August). Study and trend prediction of Covid-19 cases in India using deep learning techniques. In Journal of Physics: Conference Series (Vol. 1950, No. 1, p. 012084). IOP Publishing. DOI: https://doi.org/10.1088/1742-6596/1950/1/012084

Khatri, I., & Shrivastava, V. K. (2016). A survey of big data in healthcare industry. In Advanced Computing and Communication Technologies: Proceedings of the 9th ICACCT, 2015 (pp. 245-257). Springer Singapore. DOI: https://doi.org/10.1007/978-981-10-1023-1_25

Sholihati, R. A., Sulistijono, I. A., Risnumawan, A., & Kusumawati, E. (2020, September). Potato leaf disease classification using deep learning approach. In 2020 international electronics symposium (IES) (pp. 392-397). IEEE. DOI: https://doi.org/10.1109/IES50839.2020.9231784

Krishna, K. S., & Narayana, G. V. (2022, September). Early Blight and Late Blight Disease Prediction using CNN for Potato Leaves. In 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA) (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/ICCSEA54677.2022.9936100