A Machine Learning Approach for Detection and Classification of Potato Disease

Gopesh Kumar Bharti; Mr. Mahadev Bag; Dr. Tanushree Chatterjee; Dr. Vivek Kumar Sinha

doi:10.59367/0zcerc42

Authors

Gopesh Kumar Bharti Department of Computer Science and Engineering, Raipur Institute of Technology, Raipur, Raipur, India
Mr. Mahadev Bag Department of Computer Science and Engineering, Raipur Institute of Technology, Raipur, Raipur, India
Dr. Tanushree Chatterjee Department of Biotechnology and Engineering, Raipur Institute of Technology, Raipur, Raipur, India
Dr. Vivek Kumar Sinha Department of Computer Science and Engineering, Lovely Professional University , Punjab

DOI:

https://doi.org/10.59367/0zcerc42

Keywords:

Healthy, early blight, late blight disease classification

Abstract

Potato farming faces huge financial losses every year due to the prevention of diseases like early blight and late blight. A fungus causes early blight, and a specific microorganism is the most common culprit that causes late blight. The disease primarily affects leaves and stems, potentially causing defoliation and, under favorable conditions, increased susceptibility to tuber infection. Early detection and accurate identification of this disease are important to implement appropriate treatment and reduce economic losses. Fungi cause early blight, while a specific microorganism causes late blight. Prompt adoption of appropriate treatment can save resources and prevent crop waste. The objective of this project is to develop a classification system capable of identifying the type of disease present in potato plants. The three target classes are healthy, early blight, and late blight. By leveraging advanced techniques in image processing and machine learning, we aim to provide farmers with a reliable tool for early disease detection and accurate differentiation, allowing them to take timely and appropriate action to protect their potato crops and reduce financial losses.

References

1. Shiferaw Tafesse, E. Damtew, B. vanMierlo, R. Lie, B. Lemaga, K. Sharma, C. Leeuwis & P.C. Struik (2018) Farmers’ knowledge and practices of potato disease management in Ethiopia, NJAS: Wageningen Journal of Life Sciences, 86-87:1, 25-38, DOI: 10.1016/j.njas.2018.03.004

2. One Health: Fungal Pathogens of Humans, Animals, and Plants: Report on an American Academy of Microbiology Colloquium held in Washington, DC, on October 18, 2017. Washington (DC): American Society for Microbiology; 2019. Available from: https://www.ncbi.nlm.nih.gov/books/NBK549988/ doi: 10.1128/AAMCol.18Oct.2017

3. Shaheed, K.; Qureshi, I.; Abbas, F.; Jabbar, S.; Abbas, Q.; Ahmad, H.; Sajid, M.Z. EfficientRMT-Net—An Efficient ResNet-50 and Vision Transformers Approach for Classifying Potato Plant Leaf Diseases. Sensors 2023, 23, 9516. https://doi.org/10.3390/s23239516

4. Chowdhury, M., Kushwah, A., Satpute, A.N. et al. A Comprehensive Review on Potential Application of Nanomaterials in the Field of Agricultural Engineering. J. Biosyst. Eng. (2023). https://doi.org/10.1007/s42853-023-00204-x

5. Vidhya S, Vishwashankar TJ, Akshaya K, Aiswarya Premdas, Rohitj R, Smart Cropu Protection using Deep Learning Approach https://www.ijitee.org/portfolioitem/h6337068819/

6. P. Khobragade, A. Shriwas, S. Shinde, A. Mane and A. Padole, "Potato Leaf Disease Detection Using CNN," 2022 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), Bangalore, India, 2022, pp. 1-5, doi: 10.1109/SMARTGENCON56628.2022.10083986.

7. Rashid, J.; Khan, I.; Ali, G.; Almotiri, S.H.; AlGhamdi, M.A.; Masood, K. Multi-Level Deep Learning Model for Potato Leaf Disease Recognition. Electronics 2021, 10, 2064. https://doi.org/10.3390/electronics10172064

8. Shoaib M, Shah B, Ei-Sappagh S, Ali A, Ullah A, Alenezi F, Gechev T, Hussain T, Ali F. An advanced deep learning models-based plant disease detection: A review of recent research. Front Plant Sci. 2023 Mar 21;14:1158933. doi: 10.3389/fpls.2023.1158933. Erratum in: Front Plant Sci. 2023 Sep 27;14:1282443. PMID: 37025141; PMCID: PMC10070872.

9. Dhanaraju, M.; Chenniappan, P.; Ramalingam, K.; Pazhanivelan, S.; Kaliaperumal, R. Smart Farming: Internet of Things (IoT)-Based Sustainable Agriculture. Agriculture 2022, 12, 1745. https://doi.org/10.3390/agriculture12101745.

10. Tianbao Ren, Huanhuan Wang, Huilin Feng, Chensheng Xu, Guoshun Liu, Pan Ding,Study on the improved fuzzy clustering algorithm and its application in brain image segmentation,Applied Soft Computing,Volume 81,2019,105503,ISSN 15684946,https://doi.org/10.1016/j.asoc.2019.105503.

11. Seyed Emadedin Hashemi, Fatemeh Gholian-Jouybari, Mostafa Hajiaghaei-Keshteli, A fuzzy C-means algorithm for optimizing data clustering,Expert Systems with Applications, Volume 227,2023,120377,ISSN 09574174,https://doi.org/10.1016/j.eswa.2023.120377.

12. Vijai Singh, A.K. Misra,Detection of plant leaf diseases using image segmentation and soft computing techniques,Information Processing in Agriculture,Volume 4, Issue 1,2017,Pages 41-49,ISSN22143173,https://doi.org/10.1016/j.inpa.2016.10.005.

13. J., A.; Eunice, J.; Popescu, D.E.; Chowdary, M.K.; Hemanth, J. Deep Learning-Based Leaf Disease Detection in Crops Using Images for Agricultural Applications. Agronomy 2022, 12, 2395. https://doi.org/10.3390/agronomy12102395

14. Benos, L.; Tagarakis, A.C.; Dolias, G.; Berruto, R.; Kateris, D.; Bochtis, D. Machine Learning in Agriculture: A Comprehensive Updated Review. Sensors 2021, 21,3758.https://doi.org/10.3390/s21113758

15. Araújo, S.O.; Peres, R.S.; Ramalho, J.C.; Lidon, F.; Barata, J. Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future Perspectives. Agronomy 2023, 13, 2976. https://doi.org/10.3390/agronomy13122976

16. Tariq, U.; Ahmed, I.; Bashir, A.K.; Shaukat, K. A Critical Cybersecurity Analysis and Future Research Directions for the Internet of Things: A Comprehensive Review. Sensors 2023, 23, 4117. https://doi.org/10.3390/s23084117

17. Srinivas, L.N.B., Bharathy, A.M.V., Ramakuri, S.K. et al. An optimized machine learning framework for crop disease detection. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-15446-2.

18. A. Rio-Alvarez, J. de Andres-Suarez, M. Gonzalez-Rodriguez, D. Fernandez-Lanvin, B. López Pérez, "Effects of Challenging Weather and Illumination on Learning-Based License Plate Detection in Noncontrolled Environments", Scientific Programming, vol. 2019, Article ID 6897345, 16 pages, 2019. https://doi.org/10.1155/2019/6897345

19. Feng, J.; Hou, B.; Yu, C.; Yang, H.; Wang, C.; Shi, X.; Hu, Y. Research and Validation of Potato Late Blight Detection Method Based on Deep Learning. Agronomy 2023, 13, 1659. https://doi.org/10.3390/agronomy13061659.