Detection of cotton leaf disease with machine learning model

Abstract

This study aims to use a machine learning (ML) model to accurately classify four datasets of cotton crop leaves as either infected or healthy. Bacterial blight, Curly virus, Fussarium Wilt, and healthy leaves were used as the datasets for the study. ML is a useful tool in detecting cotton leaf diseases and can minimize the rate of disease. The problem is that without machine learning technique it is very difficult and time consuming to detect the diseases then to sort out this problem a machine learning model is proposed and to test the accuracy of the proposed model, the confusion matrix concept was used. The researchers have done their research works to diagnose the diseases by using (ML) model but the drawback of their research was that the results which were given by the different (ML) models were not accurate. The target of the study was to identify diseases affecting the cotton plant in the early stages using traditional techniques. However, utilizing various image processing techniques and machine learning algorithms, including a convolutional neural network, proved to be helpful in diagnosing the diseases. This technological approach can simplify the detection of damaged leaves and minimize the efforts of farmers in detecting those diseases. Cotton is a natural fiber produced on a large scale, and it is grown on 2.5% of overall agronomic land. The detection of cotton leaf diseases is crucial to maintain the crop's productivity and provide reliable earnings to farmers. A confusion matrix is N X N matrix used for evaluating the performance of a classification model, where N is the number of target classes. The matrix compares the actual target values with those predicted by machine learning model. This technique has four parameters to test the accuracy of the results which is given in my research work.

Keywords

• Machine learning
• Convolutional neural network
• Diseases
• Artificial neural networks

References

1. Azath, M., Zekiwos, M., & Bruck, A. (2021). Deep learning-based image processing for cotton leaf disease and pest diagnosis. Journal of Electrical and Computer Engineering, 2021, 1-10. https://doi.org/10.1155/2021/9981437
2. Caldeira, R. F., Santiago, W. E., & Teruel, B. (2021). Identification of cotton leaf lesions using deep learning techniques. Sensors, 21(9), 3169. https://doi.org/10.3390/s21093169
3. Chi, B. J., Zhang, D. M., & Dong, H. Z. (2021). Control of cotton pests and diseases by intercropping: a review. Journal of Integrative Agriculture, 20(12), 3089-3100. https://doi.org/10.1016/S2095-3119(20)63318-4
4. Dunne, R., Desai, D., Sadiku, R., & Jayaramudu, J. (2016). A review of natural fibres, their sustainability and automotive applications. Journal of Reinforced Plastics and Composites, 35(13), 1041-1050. https://doi.org/10.1177/0731684416633898
5. 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. https://doi.org/10.1016/j.compag.2018.07.032
6. Amjad, K., & Ghous, H. (2021). Critical review on multi-crops leaves disease detection using artificial intelligence methods. International Journal of Scientific & Engineering Research, 12(2), 879-912.
7. Kumar, A., & Singh, R. (2022). The Different Techniques for Detection of Plant Leaves Diseases. International Journal of Artificial Intelligence, 9(1), 1-7. https://doi.org/10.36079/lamintang.ijai-0901.342
8. Kumbhar, S., Nilawar, A., Patil, S., Mahalakshmi, B., & Nipane, M. (2019). Farmer buddy-web based cotton leaf disease detection using CNN. International Journal of Applied Engineering Research, 14(11), 2662-2666.

9. Liang, X. (2021). Few-shot cotton leaf spots disease classification based on metric learning. Plant Methods, 17, 1-11. https://doi.org/10.1186/s13007-021-00813-7
10. Lambat, R. K. M., Kothari, R., & Mane, M. K. (2022). Plant disease detection using inceptionv3. International Research Journal of Engineering and Technology (IRJET), 9(6), 2295-2300.
11. Memon, M. S., Kumar, P., & Iqbal, R. (2022). Meta deep learn leaf disease identification model for cotton crop. Computers, 11(7), 102. https://doi.org/10.3390/computers11070102
12. Noon, S. K., Amjad, M., Ali Qureshi, M., & Mannan, A. (2021). Computationally light deep learning framework to recognize cotton leaf diseases. Journal of Intelligent & Fuzzy Systems, 40(6), 12383-12398. https://doi.org/10.3233/JIFS-210516
13. Pandhare, N., Panchal, V., Mishra, S. S., & Tambe, D. (2022). Cotton plant disease detection using deep learning. International Research Journal of Modernization in Engineering Technology and Science, 4(4), 1156-1160.
14. Pechuho, N., Khan, Q., & Kalwar, S. (2020). Cotton crop disease detection using machine learning via tensorflow. Pakistan Journal of Engineering and Technology, 3(2), 126-130.
15. Pantazi, X. E., Moshou, D., & Tamouridou, A. A. (2019). Automated leaf disease detection in different crop species through image features analysis and One Class Classifiers. Computers and Electronics in Agriculture, 156, 96-104. https://doi.org/10.1016/j.compag.2018.11.005
16. Tripathi, M. K., & Maktedar, D. D. (2021). Detection of various categories of fruits and vegetables through various descriptors using machine learning techniques. International Journal of Computational Intelligence Studies, 10(1), 36-73. https://doi.org/10.1504/IJCISTUDIES.2021.113819
17. Pham, T. N., Van Tran, L., & Dao, S. V. T. (2020) Early disease classification of mango leaves using feed-forward neural network and hybrid metaheuristic feature selection. IEEE Access, 8, 189960-189973. https://doi.org/10.1109/ACCESS.2020.3031914
18. Thangaraj, R., Anandamurugan, S., & Kaliappan, V. K. (2021). Automated tomato leaf disease classification using transfer learning-based deep convolution neural network. Journal of Plant Diseases and Protection, 128(1), 73-86. https://doi.org/10.1007/s41348-020-00403-0
19. Mhatre, R., & Lanke, V. (2021). Cotton leaves disease detection and cure using deep learning. International Research Journal of Modernization in Engineering Technology and Science, 3(1), 1369-1374.
20. Ranjan, M., Weginwar, M. R., Joshi, N., & Ingole, A. B. (2015). Detection and classification of leaf disease using artificial neural network. International Journal of Technical Research and Applications, 3(3), 331-333.
21. Sarwar, R., Aslam, M., Khurshid, K. S., Ahmed, T., Martinez-Enriquez, A. M., & Waheed, T. (2021). Detection and classification of cotton leaf diseases using faster R-CNN on field condition images. Acta Scientific Agrıculture, 5(10), 29-37.
22. Ramacharan, S. (2021). A 3-stage method for disease detection of cotton plant leaf using deep learning CNN algorithm. International Journal for Research in Applied Science & Engineering Technology., 9(VII), 2503-2510.
23. Kumar, S., Ratan, R., & Desai, J. V. (2022). Cotton disease detection using tensorflow machine learning technique. Advances in Multimedia, 1812025. https://doi.org/10.1155/2022/1812025
24. Annabel, L. S. P., Annapoorani, T., & Deepalakshmi, P. (2019). Machine learning for plant leaf disease detection and classification–a review. In 2019 International Conference on Communication and Signal Processing (ICCSP), 538-542. https://doi.org/10.1109/ICCSP.2019.8698004
25. Saha, P., & Nachappa, M. N. (2022). Cotton Plant Disease Prediction Using Deep Learning. International Journal for Research in Applied Science & Engineering Technology, 10(3), 744-746. https://doi.org/10.22214/ijraset.2022.40731
26. Patil, S. V., Sharma, A. K., Kamble, B. R., & Jadhav, K. B. (2022). Cotton leaf disease detection using deep learning. International Journal of Creative Research Thoughts, 10(5), 9804-9810.
27. Singh, V., & Misra, A. K. (2017). Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture, 4(1), 41-49. https://doi.org/10.1016/j.inpa.2016.10.005
28. Tripathy, S. (2021) Detection of cotton leaf disease using image processing Techniques. In Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/2062/1/012009
29. Gupta, S., Geetha, A., Sankaran, K. S., Zamani, A. S., Ritonga, M., Raj, R., ... & Mohammed, H. S. (2022). Machine learning-and feature selection-enabled framework for accurate crop yield prediction. Journal of Food Quality, 2022, 1-7. https://doi.org/10.1155/2022/6293985
30. Wang, G., Sun, Y., & Wang, J. (2017). Automatic image-based plant disease severity estimation using deep learning. Computational Intelligence and Neuroscience, 2017, 2917536. https://doi.org/10.1155/2017/2917536
31. Zhou, C., Zhou, S., Xing, J., & Song, J. (2021). Tomato leaf disease identification by restructured deep residual dense network. IEEE Access, 9, 28822-28831. https://doi.org/10.1109/ACCESS.2021.3058947