Plant Disease Detection through the Implementation of Diversified and Modified Neural Network Algorithms

  • Fatema Nihar Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh
  • Nazmun Nahar Khanom Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh
  • Syed Sahariar Hassan Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh
  • Amit Kumar Das Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh
Keywords: Artificial Neural Network, Agriculture, Disease Detection, Monitoring

Abstract

In the era of artificial systems, disease detection is becoming easier. For detecting disease, monitoring the plants 24 hours, visiting the agricultural office, or asking for help from a specialist seem difficult. This situation demands a user-friendly plant disease detection system, which allows people to detect whether the plant is diseased or not in an easier way.  If the plant is diseased, a treatment plan will also be notified. In this way, people can easily save time, money, and, most importantly, plants. In this study, the researchers have collected data of vegetables from a field and applied multiple diversified Neural Network Algorithms such as CNN, MCNN, FRCNN, and, along with that, also proposed a new modified neural network architecture (ModCNN), which has produced 97.69% accuracy. The authors have also classified the bean leaf diseases into four categories according to their symptoms, which will help to identify diseases accurately.

References

Barbedo, J.G., 2018. Factors influencing the use of deep learning for plant disease recognition. Biosystems engineering, 172, pp.84-91. DOI: https://doi.org/10.1016/j.biosystemseng.2018.05.013

Zhang, S., Huang, W. and Zhang, C., 2019. Three-channel convolutional neural networks for vegetable leaf disease recognition. Cognitive Systems Research, 53, pp.31-41. DOI: https://doi.org/10.1016/j.cogsys.2018.04.006

Too, E.C., Yujian, L., Njuki, S. and Yingchun, L., 2019. A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture, 161, pp.272-279. DOI: https://doi.org/10.1016/j.compag.2018.03.032

Singh, K.K., 2018, November. An artificial intelligence and cloud based collaborative platform for plant disease identification, tracking and forecasting for farmers. In 2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) (pp. 49-56). IEEE. DOI: https://doi.org/10.1109/CCEM.2018.00016

Sardogan, M., Tuncer, A. and Ozen, Y., 2018, September. Plant leaf disease detection and classification based on CNN with LVQ algorithm. In 2018 3rd International Conference on Computer Science and Engineering (UBMK) (pp. 382-385). IEEE. DOI: https://doi.org/10.1109/UBMK.2018.8566635

Mohanty, S.P., Hughes, D.P. and Salathé, M., 2016. Using deep learning for image-based plant disease detection. Frontiers in plant science, 7, p.1419. DOI: https://doi.org/10.3389/fpls.2016.01419

Dalakouras, A., Wassenegger, M., Dadami, E., Ganopoulos, I., Pappas, M.L. and Papadopoulou, K., 2020. Genetically modified organism-free RNA interference: exogenous application of RNA molecules in plants. Plant physiology, 182(1), pp.38-50. DOI: https://doi.org/10.1104/pp.19.00570

Jiang, P., Chen, Y., Liu, B., He, D. and Liang, C., 2019. Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks. IEEE Access, 7, pp.59069-59080. DOI: https://doi.org/10.1109/ACCESS.2019.2914929

Singh, U.P., Chouhan, S.S., Jain, S. and Jain, S., 2019. Multilayer convolution neural network for the classification of mango leaves infected by anthracnose disease. IEEE Access, 7, pp.43721-43729. DOI: https://doi.org/10.1109/ACCESS.2019.2907383

Zhou, G., Zhang, W., Chen, A., He, M. and Ma, X., 2019. Rapid detection of rice disease based on FCM-KM and faster R-CNN fusion. IEEE Access, 7, pp.143190-143206. DOI: https://doi.org/10.1109/ACCESS.2019.2943454

Bhuiyan, M., Rahman, A., Ullah, M. and Das, A.K., 2019. iHealthcare: Predictive model analysis concerning big data applications for interactive healthcare systems. Applied Sciences, 9(16), p.3365. DOI: https://doi.org/10.3390/app9163365

Cynthia, S.T., Hossain, K.M.S., Hasan, M.N., Asaduzzaman, M. and Das, A.K., 2019, December. Automated detection of plant diseases using image processing and faster R-CNN algorithm. In 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI) (pp. 1-5). IEEE. DOI: https://doi.org/10.1109/STI47673.2019.9068092

Paul, S., Joy, J.I., Sarker, S., Ahmed, S. and Das, A.K., 2019, December. An Unorthodox Way of Farming Without Intermediaries Through Blockchain. In 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI) (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/STI47673.2019.9068007

Islam, S., Khan, S.I.A., Abedin, M.M., Habibullah, K.M. and Das, A.K., 2019, July. Bird species classification from an image using vgg-16 network. In Proceedings of the 2019 7th International Conference on Computer and Communications Management (pp. 38-42). DOI: https://doi.org/10.1145/3348445.3348480

Kaur, S., Pandey, S. and Goel, S., 2018. Semi-automatic leaf disease detection and classification system for soybean culture. IET Image Processing, 12(6), pp.1038-1048. DOI: https://doi.org/10.1049/iet-ipr.2017.0822

Published
2021-03-12
  • Abstract view127
How to Cite
Nihar, F., Khanom, N. N., Hassan, S. S., & Das, A. K. (2021). Plant Disease Detection through the Implementation of Diversified and Modified Neural Network Algorithms. Journal of Engineering Advancements, 2(01), 48-57. https://doi.org/10.38032/jea.2021.01.007
Section
Research Articles

Most read articles by the same author(s)