Journal Press India®

A Broad Review of Various Machine Learning Models for Disease Detection in the Domain of Agriculture

Vol 3 , Issue 2 , July - December 2023 | Pages: 153-170 | Research Paper  

https://doi.org/10.17492/computology.v3i2.2308


Author Details ( * ) denotes Corresponding author

1. * Madhavi ., PhD Scholar, CSE, Bennett University, Greater Noida, Uttar Pradesh, India (drmadhavi.phd@gmail.com)

The Agriculture domain is a vast domain with various grain and food categories which helps to boost the economy. Among all these categories leaf disease is the one common issue that impacts the overall growth of crops and can be dealt with with the use of models that have been proposed in ML and DL. In this paper, we have given an introduction to such models which have performed extraordinarily for disease classification and detection in the field of agriculture. Various crops have been listed according to their accuracy and the model has been proposed by various researchers. The algorithms’ performance is typically assessed using a variety of metrics, such as F1 score, accuracy, and precision. Researchers working in this field who are searching for different effective ML and DL-based classifiers for leaf disease detection would find this review to be useful.

Keywords

ML; DL; Agriculture; Plant Disease

  1. Beltran-Perez, C., Wei, H.-L., & Rubio-Solis, A. (2020). Generalized multiscale rbf networks and the dct for breast cancer detection. International Journal of Automation and Computing, 17, 55–70.
  2. Sharma, P., Leigh, L., Chang, J., Maimaitijiang, M. & Caffé, M. (2022). Above-ground biomass estimation in oats using uav remote sensing and machine learning. Sensors, 22(2), 601.
  3. Wu, N., Zhang, Y., Na, R., Mi, C., Zhu, S., He, Y. & Zhang, C. (2019). Variety identification of oat seeds using hyperspectral imaging: Investigating the representation ability of deep convolutional neural network. RSC Advances, 9(22), 12635–12644.
  4. Kumar, S., Deo, M. M., Kumar, K., Rathore, M., Akram, M. & Pratap, A. (2023). Detection and classification of yellow mosaic disease in vigna mungo using convolutional neural network deep learning models. Ecology Environment and Conservation, 29, 177-181.
  5. Mallick, M. T., Biswas, S., Das, A. K., Saha, H. N., Chakrabarti, A., & Deb, N. (2023). Deep learning based automated disease detection and pest classification in indian mung bean. Multimedia Tools and Applications, 82(8), 12017–12041.
  6. Kumar, S., Kumar, K., Tewari, K., Sagar, P., Pandey, J., Shanmugavadivel, P., Rathore, M., Kumar, V., Akram, M., & Singh, A.K., (2022). Gene expression and biochemical profiling of contrasting vigna mungo genotypes against mungbean yellow mosaic india virus (mymiv). Journal of Food Legumes, 35(2), 107–116.
  7. Basak, J., Kundagrami, S., Ghose, T. & Pal, A. (2005). Development of yellow mosaic virus (ymv) resistance linked dna marker in vigna mungo from populations segregating for ymv-reaction. Molecular Breeding, 14, 375–383.
  8. Harika, S., Sandhyarani, G., Sagar, D. & Reddy, G. S. (2023). Image-based black gram crop disease detection. In 2023 International Conference on Inventive Computation Technologies (ICICT), pp. 529–533. IEEE.
  9. Khan, H., Haq, I.U., Munsif, M., Khan, S.U. & Lee, M.Y. (2022).Automated wheat diseases classification framework using advanced machine learning technique. Agriculture, 12(8), 1226. Retrieved from DOI:10.3390/agriculture12081226
  10. Kumar, D. & Kukreja, V. (2021). N-cnn based transfer learning method for classification of powdery mildew wheat disease. In 2021 International Conference on Emerging Smart Computing and Informatics (ESCI), pp. 707–710. IEEE.
  11. Wang, Y., Wang, H. & Peng, Z. (2021). Rice diseases detection and classification using attention based neural network and bayesian optimization. Expert Systems with Applications, 178(2), 114770.
  12. Sethy, P. K., Barpanda, N. K., Rath, A. K. & Behera, S. K. (2020). Deep feature based rice leaf disease identification using support vector machine. Computers and Electronics in Agriculture, 175(1), 105527.
  13. Panigrahi, K. P., Sahoo, A. K. & Das, H. (2020). A cnn approach for corn leaves disease detection to support digital agricultural system. In 2020 4th International Con- ference on Trends in Electronics and Informatics (ICOEI)(48184), pp. 678–683. Retrieved from https://doi.org/10.1109/ICOEI48184.2020.9142871
  14. Xu, P., Fu, L., Xu, K., Sun, W., Tan, Q., Zhang, Y., Zha, X. & Yang, R. (2023). Investigation into maize seed disease identification based on deep learning and multi-source spectral information fusion techniques. Journal of Food Composition and Analysis, 119, 105254.
  15. Qi, H., Liang, Y., Ding, Q. & Zou, J. (2021). Automatic identification of peanut-leaf diseases based on stack ensemble. Applied Sciences, 11(4), 1950.
  16. Bevers, N., Sikora, E. J., & Hardy, N. B. (2022). Soybean disease identification using original field images and transfer learning with convolutional neural networks. Computers and Electronics in Agriculture, 203, 107449.
  17. Mustafa, H., Umer, M., Hafeez, U., Hameed, A., Sohaib, A., Ullah, S. & Madni, H.A. (2023). Pepper bell leaf disease detection and classification using optimized convolutional neural network. Multimedia Tools and Applications, 82(8), 12065–12080.
  18. Hridoy, R.H., Arni, A.D. & Hassan, M.A. (2022). Recognition of mustard plant dis- eases based on improved deep convolutional neural networks. In 2022 IEEE Region 10 Symposium (TENSYMP), pp. 1–6 (2022). Retrived from https://doi.org/10.1109/ TENSYMP54529.2022.9864487
  19. Song, Y. & Lu, Y. (2015). Decision tree methods: Applications for classification and prediction. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/ PMC4466856/
  20. Navada, A., Ansari, A.N., Patil, S. & Sonkamble, B.A. (2011). Overview of use of decision tree algorithms in machine learning. In 2011 IEEE Control and System Graduate Research Colloquium, pp. 37–42.
  21. Biau, G. & Scornet, E. (2016). A random forest guided tour – TEST. Retrieved from https://link.springer.com/article/10.1007/s11749-016-0481-7
  22. Liu, Y., Wang, Y. & Zhang, J. (2012). New machine learning algorithm: Random forest. In Information Computing and Applications: Third International Conference, ICICA 2012, Chengde, China, September 14-16, 2012. Proceedings 3, pp. 246–252. Springer.
  23. Suthaharan, S. & Suthaharan, S. (2016). Support vector machine. Machine learning models and algorithms for big data classification: thinking with examples for effective learning, 207–235. Retrieved from DOI:10.1007/978-1-4899-7641-3
  24. Rish, I. (2001). An empirical study of the naive Bayes classifier. Retrieved from https://sites.cc.gatech.edu/home/isbell/classes/reading/papers/Rish.pdf
  25. Berrar, D. (2018). Bayes’ theorem and naive bayes classifier. Encyclopedia of bioinformatics and computational biology. ABC of Bioinformatics 403, 412. Retrieved from DOI:10.1016/B978-0-12-809633-8.20473-1
  26. Peterson, L. E. (2009). K-nearest neighbour. Scholarpedia. Retrieved from http://scholarpedia.org/article/K-nearest neighbor.
  27. Bijalwan, V., Kumar, V., Kumari, P. & Pascual, J. (2014). Knn based machine learn- ing approach for text and document mining. International Journal of Database Theory and Application, 7(1), 61–70.
  28. Maulud, D. & Abdulazeez, A. M. (2020). A review on linear regression comprehensive in machine learning. Journal of Applied Science and Technology Trends, 1(4), 140–147.
  29. Christodoulou, E., Ma, J., Collins, G.S., Steyerberg, E.W., Verbakel, J.Y., & Van Calster, B. (2019). A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. Journal of Clinical Epidemiology, 110, 12–22.
  30. Ying, C., Qi-Guang, M., Jia-Chen, L. & Lin, G. (2013). Advance and prospects of adaboost algorithm. Acta Automatica Sinica, 39(6), 745–758.
  31. Howley, T., Madden, M. G., O’Connell, M.-L. & Ryder, A.G. (2005). The effect of principal component analysis on machine learning accuracy with high dimensional spectral data. In International Conference on Innovative Techniques and Applications of Artificial Intelligence, pp. 209–222. Springer
  32. Tharwat, A., Gaber, T., Ibrahim, A. & Hassanien, A. E. (2017). Linear discriminant analysis: A detailed tutorial. AI Communications, 30(2), 169–190.
  33. Fine, S., Singer, Y. & Tishby, N. (1998). The hierarchical hidden markov model: Analysis and applications. Machine Learning, 32, 41–62.
  34. O’Shea, K. & Nash, R. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458.
  35. Medsker, L.R. & Jain, L. (2001). Recurrent neural networks. Design and Applications, 5(64-67), 2.
  36. Sainath, T. N., Vinyals, O., Senior, A. & Sak, H. (2015). Convolutional, long short-term memory, fully connected deep neural networks. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4580–4584. IEEE.
Abstract Views: 4
PDF Views: 144

By continuing to use this website, you consent to the use of cookies in accordance with our Cookie Policy.