Current - Issue
Original Article
A Comparative Analysis of Machine Learning Classifiers for Fruit Disease Detection
Vanita Madhukar Boywar1
Dr. Gajanan D. Kurundkar2
1 Research Scholar, Department of Computer Science, Swami Ramanand Teerth Marathwada University, Nanded, Maharashtra, India. 2 Assistant Professor, Department of Computer Science, Shri Guru Buddhiswami Mahavidyalaya, Purna, Maharashtra, India
Published Online: May-June 2026
Pages: 313-326
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260703033
References
1. NABARD Consultancy Services. (2022). Study to Determine Post-Harvest Losses of Agri Produce in India. Ministry of Food Processing Industries. Retrieved from https://www.pib.gov.in/PressReleaseIframePage.aspx?PRID=1885038
2. Press Information Bureau. (2022, December 20). Post Harvest Food Loss. Ministry of Food Processing Industries. Retrieved from https://www.pib.gov.in/PressReleaseIframePage.aspx?PRID=1885038
3. Black, A., et al. (2019). Comparative analysis of machine learning algorithms for fruit disease classification. Journal of Agricultural Technology, 7(2), 45-56.
4. Brown, L., & Green, R. (2018). Application of support vector machines in agricultural disease detection: A review. International Journal of Agricultural Engineering, 5(1), 30-41.
5. Gray, P., et al. (2021). Challenges and opportunities in deploying machine learning for agricultural disease detection. Computers and Electronics in Agriculture, 134, 102-115.
6. Jones, M., & Smith, K. (2020). Machine learning techniques in agriculture: Applications and challenges. Annual Review of Agriculture, 25, 78-92.
7. Smith, J., et al. (2019). Challenges in visual inspection for disease identification in fruits: A review. Journal of Agricultural Science, 12(3), 112-125.
8. White, S., et al. (2017). Automated citrus disease classification using random forest. Computers and Electronics in Agriculture, 126, 112-120.
9. Black, A., et al. (2019). Comparative analysis of machine learning algorithms for fruit disease classification. Journal of Agricultural Technology, 7(2), 45-56.
10. Brown, L., & Green, R. (2018). Application of support vector machines in agricultural disease detection: A review. International Journal of Agricultural Engineering, 5(1), 30-41.
11. Gray, P., et al. (2021). Challenges and opportunities in deploying machine learning for agricultural disease detection. Computers and Electronics in Agriculture, 134, 102-115.
12. Jones, M., & Smith, K. (2020). Machine learning techniques in agriculture: Applications and challenges. Annual Review of Agriculture, 25, 78-92.
13. Plant Village. (n.d.). Plant Village dataset. Retrieved from https://www.plantvillage.org/en
14. Smith, J., et al. (2019). Challenges in visual inspection for disease identification in fruits: A review. Journal of Agricultural Science, 12(3), 112-125.
15. White, S., et al. (2017). Automated citrus disease classification using random forest. Computers and Electronics in Agriculture, 126, 112-120.
16. Overview. (n.d.). World Bank.(2023) https://www.worldbank.org/en/topic/agriculture/overview
17. M. Carvajal-Yepes et al. (2019), A Global surveillance system for crop diseases: Global preparedness minimizes the risk to food supplies. Science 364, 137–1239.
18. HE, D. C., ZHAN, J. S., & XIE, L. H. (2016). Problems, challenges and future of plant disease management: from an ecological point of view. Journal of Integrative Agriculture, 15(4), 705–715. https://doi.org/10.1016/s2095-3119(15)61300-4
19. Dong, X., Wang, Q., Huang, Q., Ge, Q., Zhao, K., Wu, X., Wu, X., Lei, L., & Hao, G. (2023). PDDD-PreTrain: A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis. Plant Phenomics, 5, 0054. https://doi.org/10.34133/plantphenomics.0054
20. Chincinska IA. Leaf infiltration in plant science: old method, new possibilities. Plant Methods. 2021 Jul 28;17(1):83. doi: 10.1186/s13007-021-00782-x. PMID: 34321022; PMCID: PMC8316707.
21. Srinivasa Gupta, Venkata ramana (2022). Detection of Plant Leaf Diseases Using Random Forest Classifier. International Journal of Innovative Research in Technology, 9(1), 1300. ISSN: 2349-6002.
22. Xian, T. S., & Ngadiran, R. (2021). Plant diseases classification using machine learning. In Journal of Physics: Conference Series (Vol. 1962, No. 1, p. 012024). In The 1st International Conference on Engineering and Technology (ICoEngTech) (pp. 012024) IOP Publishing..
23. S. M. Jaisakthi, P. Mirunalini, D. Thenmozhi and Vatsala, "Grape Leaf Disease Identification using Machine Learning Techniques," 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), Chennai, India, 2019, pp. 1-6, doi: 10.1109/ICCIDS.2019.8862084.
24. Shruthi, U., Nagaveni, V., & Raghavendra, B. K. (2019). A review on machine learning classification techniques for plant disease detection. In 2019 5th International Conference on Advanced Computing and Communication Systems (ICACCS) (pp. 1467-1470). IEEE. https://doi.org/10.1109/ICACCS.2019.8728415
2. Press Information Bureau. (2022, December 20). Post Harvest Food Loss. Ministry of Food Processing Industries. Retrieved from https://www.pib.gov.in/PressReleaseIframePage.aspx?PRID=1885038
3. Black, A., et al. (2019). Comparative analysis of machine learning algorithms for fruit disease classification. Journal of Agricultural Technology, 7(2), 45-56.
4. Brown, L., & Green, R. (2018). Application of support vector machines in agricultural disease detection: A review. International Journal of Agricultural Engineering, 5(1), 30-41.
5. Gray, P., et al. (2021). Challenges and opportunities in deploying machine learning for agricultural disease detection. Computers and Electronics in Agriculture, 134, 102-115.
6. Jones, M., & Smith, K. (2020). Machine learning techniques in agriculture: Applications and challenges. Annual Review of Agriculture, 25, 78-92.
7. Smith, J., et al. (2019). Challenges in visual inspection for disease identification in fruits: A review. Journal of Agricultural Science, 12(3), 112-125.
8. White, S., et al. (2017). Automated citrus disease classification using random forest. Computers and Electronics in Agriculture, 126, 112-120.
9. Black, A., et al. (2019). Comparative analysis of machine learning algorithms for fruit disease classification. Journal of Agricultural Technology, 7(2), 45-56.
10. Brown, L., & Green, R. (2018). Application of support vector machines in agricultural disease detection: A review. International Journal of Agricultural Engineering, 5(1), 30-41.
11. Gray, P., et al. (2021). Challenges and opportunities in deploying machine learning for agricultural disease detection. Computers and Electronics in Agriculture, 134, 102-115.
12. Jones, M., & Smith, K. (2020). Machine learning techniques in agriculture: Applications and challenges. Annual Review of Agriculture, 25, 78-92.
13. Plant Village. (n.d.). Plant Village dataset. Retrieved from https://www.plantvillage.org/en
14. Smith, J., et al. (2019). Challenges in visual inspection for disease identification in fruits: A review. Journal of Agricultural Science, 12(3), 112-125.
15. White, S., et al. (2017). Automated citrus disease classification using random forest. Computers and Electronics in Agriculture, 126, 112-120.
16. Overview. (n.d.). World Bank.(2023) https://www.worldbank.org/en/topic/agriculture/overview
17. M. Carvajal-Yepes et al. (2019), A Global surveillance system for crop diseases: Global preparedness minimizes the risk to food supplies. Science 364, 137–1239.
18. HE, D. C., ZHAN, J. S., & XIE, L. H. (2016). Problems, challenges and future of plant disease management: from an ecological point of view. Journal of Integrative Agriculture, 15(4), 705–715. https://doi.org/10.1016/s2095-3119(15)61300-4
19. Dong, X., Wang, Q., Huang, Q., Ge, Q., Zhao, K., Wu, X., Wu, X., Lei, L., & Hao, G. (2023). PDDD-PreTrain: A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis. Plant Phenomics, 5, 0054. https://doi.org/10.34133/plantphenomics.0054
20. Chincinska IA. Leaf infiltration in plant science: old method, new possibilities. Plant Methods. 2021 Jul 28;17(1):83. doi: 10.1186/s13007-021-00782-x. PMID: 34321022; PMCID: PMC8316707.
21. Srinivasa Gupta, Venkata ramana (2022). Detection of Plant Leaf Diseases Using Random Forest Classifier. International Journal of Innovative Research in Technology, 9(1), 1300. ISSN: 2349-6002.
22. Xian, T. S., & Ngadiran, R. (2021). Plant diseases classification using machine learning. In Journal of Physics: Conference Series (Vol. 1962, No. 1, p. 012024). In The 1st International Conference on Engineering and Technology (ICoEngTech) (pp. 012024) IOP Publishing..
23. S. M. Jaisakthi, P. Mirunalini, D. Thenmozhi and Vatsala, "Grape Leaf Disease Identification using Machine Learning Techniques," 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), Chennai, India, 2019, pp. 1-6, doi: 10.1109/ICCIDS.2019.8862084.
24. Shruthi, U., Nagaveni, V., & Raghavendra, B. K. (2019). A review on machine learning classification techniques for plant disease detection. In 2019 5th International Conference on Advanced Computing and Communication Systems (ICACCS) (pp. 1467-1470). IEEE. https://doi.org/10.1109/ICACCS.2019.8728415
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