A multimodal fusion framework to diagnose cotton leaf curl virus using machine vision techniques

References

• Aher, S. B., & Lobo, L. M. R. J. (2011). Data mining in educational system using WEKA. International Conference on Emerging Technology Trends (ICETT), Kerala, India. (pp. 1–17).
   Google Scholar
• Akhtar, K. P., Hussain, M., Khan, A. I., Haq, M. A., & Iqbal, M. M. (2004). Influence of plant age, whitefly population and cultivar resistance on infection of cotton plants by cotton leaf curl virus (CLCuV) in Pakistan. Field Crops Research, 86(1), 15–21. https://doi.org/10.1016/S0378-4290(03)00166-7
   Web of Science ®Google Scholar
• Ali, H., Lali, M. I., Nawaz, M. Z., Sharif, M., & Saleem, B. A. (2017). Symptom based automated detection of citrus diseases using color histogram and textural descriptors. Computers and Electronics in Agriculture, 138, 92–104. https://doi.org/10.1016/j.compag.2017.04.008
   Web of Science ®Google Scholar
• Anwar, S., Kolachi, A. R., Baloch, S. K., & Soomro, S. R. (2022). Bacterial blight and cotton leaf curl virus detection using inception V4 based CNN model for cotton crops . IEEE 5th International Conference on Image Processing Applications and Systems (IPAS). Genova, Italy. https://doi.org/10.1109/ipas55744.2022.10052835
   Google Scholar
• Barbhuiya, A. J., & Hemachandran, K. (2013). Wavelet transformations & its major applications in digital image processing. International Journal of Engineering Research & Technology, 2(3), 1–5. https://doi.org/10.17577/IJERTV2IS3538
   Google Scholar
• Blachnik, M., & Laaksonen, J. (2008). Image classification by histogram features created with learning vector quantization. Artificial Neural Networks – ICANN (pp. 827–836). https://doi.org/10.1007/978-3-540-87536-9_85
   Google Scholar
• Farooq, J., Farooq, A., Riaz, M., Shahid, M. R., Saeed, F., Iqbal, M. S., & Mahmood, A. (2014). Cotton leaf curl virus disease a principle cause of decline in cotton productivity in Pakistan. Canadian Journal of Plant Protection, 2, 9–16. https://repo.mel.cgiar.org/handle/20.500.11766/564
   Google Scholar
• Feng, L., Wu, B., Zhu, S., Wang, J., Su, Z., Liu, F., He, Y., & Zhang, C. (2020). Investigation on data fusion of multisource spectral data for rice leaf diseases identification using machine learning Methods. Frontiers in Plant Science, 11, 577063. https://doi.org/10.3389/fpls.2020.577063
   PubMed Web of Science ®Google Scholar
• Feng, Q., Liu, J., & Gong, J. (2015). UAV remote sensing for urban vegetation mapping using random forest and texture analysis. Remote Sensing, 7(1), 1074–1094. https://doi.org/10.3390/rs70101074
   Web of Science ®Google Scholar
• Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318. https://doi.org/10.1016/j.compag.2018.01.009
   Web of Science ®Google Scholar
• Gelli, J. S., Madduri, L. A., Tanveer, R., Bhanu, U., & Kishore, G. K. (2020). Plant disease detection using VGG and DJANGO. International Research Journal of Engineering and Technology, (IRJET ), 7(8), 4369–4373. https://irjet.net/archives/V7/i8/IRJET-V7I8753.pdf
   Google Scholar
• Gu, Q., Sheng, L., Zhang, T., Lu, Y., Zhang, Z., Zheng, K., Hu, H., & Zhou, H. (2019). Early detection of tomato spotted wilt virus infection in tobacco using the hyperspectral imaging technique and machine learning algorithms. Computers and Electronics in Agriculture, 167, 105066. https://doi.org/10.1016/j.compag.2019.105066
   Web of Science ®Google Scholar
• Gudela, Y. S., Jangam, E., & Mogili, S. (2023). Cotton crop disease detection using mobilenet snapshot ensemble technique and vision transformers. 8th International Conference on Communication and Electronics Systems (ICCES). Coimbatore, India. https://doi.org/10.1109/icces57224.2023.10192615
   Google Scholar
• Hassan, F., Qayyum, A., Malik, W., Maqbool, A., Rehman, A. M., Shoaib, M., & Arshad, M. (2016). Cotton leaf curl virus (CLCuV) disease in Pakistan: A critical review. Applied Sciences and Business Economics, 3, 8–14. https://www.researchgate.net/publication/305994089_cotton_leaf_curl_virus_clcuv_disease_in_pakistan_a_critical_review
   Google Scholar
• Huang, K., & Aviyente, S. (2008). Wavelet feature selection for image classification. IEEE Transactions on Image Processing: A Publication of the Ieee Signal Processing Society, 17(9), 1709–1720. https://doi.org/10.1109/TIP.2008.2001050
   PubMed Web of Science ®Google Scholar
• Ilic, M., Ilic, S., Jovic, S., & Panic, S. (2018). Early cherry fruit pathogen disease detection based on data mining prediction. Computers and Electronics in Agriculture, 150, 418–425. https://doi.org/10.1016/j.compag.2018.05.008
   Web of Science ®Google Scholar
• Jiang, F., Lu, Y., Chen, Y., Cai, D., & Li, G. (2020). Image recognition of four rice leaf diseases based on deep learning and support vector machine. Computers and Electronics in Agriculture, 179, 105824. https://doi.org/10.1016/j.compag.2020.105824
   Web of Science ®Google Scholar
• Jovic, A., Brkic, K., & Bogunovic, N. (2015). A review of feature selection methods with applications. IEEE 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia. (pp. 1200–1205). https://doi.org/10.1109/mipro.2015.7160458
   Google Scholar
• Kerkech, M., Hafiane, A., & Canals, R. (2018). Deep learning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images. Computers and Electronics in Agriculture, 155, 237–243. https://doi.org/10.1016/j.compag.2018.10.006
   Web of Science ®Google Scholar
• Khaled, A. Y., Abd Aziz, S., Bejo, S. K., Nawi, N. M., & Abu Seman, I. (2018). Spectral features selection and classification of oil palm leaves infected by Basal stem rot (BSR) disease using dielectric spectroscopy. Computers and Electronics in Agriculture, 144, 297–309. https://doi.org/10.1016/j.compag.2017.11.012
   Web of Science ®Google Scholar
• Kotian, S., Ettan, P., Kharche, S., Saravanan, K., & Kumar, K. A. (2022). Cotton leaf disease detection using machine learning. 2nd International Conference on Advancement in Electronics & Communication Engineering, Ghaziabad, India. https://doi.org/10.2139/ssrn.4159108
   Google Scholar
• Kumar, G., & Bhatia, P. K. (2014). A detailed review of feature extraction in image processing systems. International Conference on Advanced Computing & Communication Technologies (ACCT). Rohtak, India. (pp. 5–12.). https://doi.org/10.1109/acct.2014.74
   Google Scholar
• Lever, J., Krzywinski, M., & Altman, N. (2016). Classification evaluation. Nature Methods, 13(8), 603–604. https://doi.org/10.1038/nmeth.3945
   Web of Science ®Google Scholar
• Lutu, P. E., & Engelbrecht, A. P. (2010). A decision rule-based method for feature selection in predictive data mining. Expert Systems with Applications, 37(1), 602–609. https://doi.org/10.1016/j.eswa.2009.06.031
   Web of Science ®Google Scholar
• Ma, J., Du, K., Zhen, F., Zhang, L., Gong, Z., & Sun, Z. (2018). A recognition method for cucumber diseases using leaf symptom images base on deep convolutional nerual network. Computers and Electronics in Agriculture, 154, 18–24. https://doi.org/10.1016/j.compag.2018.08.048
   Web of Science ®Google Scholar
• Nilsson, H. E. (1995). Remote sensing and image analysis in plant pathology. Annual Review of Phytopathology, 33(1), 489–528. https://doi.org/10.1146/annurev.py.33.090195.002421
   PubMedGoogle Scholar
• Ouhami, M., Hafiane, A., Es-Saad, Y., Hajji, M. E., & Canals, R. (2021). Computer vision, IoT and data fusion for crop disease detection using machine learning: A survey and ongoing research. Remote Sensing, 13(13), 2486. https://doi.org/10.3390/rs13132486
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Patil, R. R., & Kumar, S. (2022). Rice-fusion: A multimodality data fusion framework for rice disease diagnosis. IEEE Access, 10, 5207–5222. https://doi.org/10.1109/ACCESS.2022.3140815
   Web of Science ®Google Scholar
• Pechuho, N., Khan, Q., & Kalwar, S. (2020). Cotton crop disease detection using machine learning via tensor flow. Pakistan Journal of Engineering and Technology, 3(2), 126–130. https://doi.org/10.51846/vol3iss2pp126-130
   Google Scholar
• Prajapati, B. S., Dabhi, V. K., & Prajapati, H. B. (2016). A survey on detection and classification of cotton leaf diseases. IEEE International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). Chennai, India. (pp. 2499–2506). https://doi.org/10.1109/iceeot.2016.7755143
   Google Scholar
• Qadri, S., Khan, D. M., Ahmad, F., Qadri, S. F., Babar, M. E., Shahid, M., Ul-Rehman, M., Razzaq, A., Shah Muhammad, S., Fahad, M., Ahmad, S., Pervez, M. T., Naveed, N., Aslam, N., Jamil, M., Rehmani, E. A., Ahmad, N., & Akhtar Khan, N. (2016). A comparative study of land cover classification by using multispectral and texture data. BioMed Research International, 2016, 8797438–12. https://doi.org/10.1155/2016/8797438
   PubMed Web of Science ®Google Scholar
• Qadri, S., Khan, D. M., Qadri, S. F., Razzaq, A., Ahmad, N., Jamil, M., Nawaz Shah, A., Shah Muhammad, S., Saleem, K., & Awan, S. A. (2017). Multisource data fusion framework for land use/land cover classification using machine vision. Journal of Sensors, 2017, 1–8. https://doi.org/10.1155/2017/3515418
   Web of Science ®Google Scholar
• Radhakrishnan, M. (2012). Application of computer vision technique on sorting and grading of fruits and vegetables. Food Processing & Technology, 10, 2157–7110. https://doi.org/10.4172/2157-7110.s1-001
   Google Scholar
• Rana, A. W., Ejaz, A., & Shikoh, S. H. (2020). Cotton crop: A situational analysis of Pakistan. International Food Policy Research Institute. https://doi.org/10.2499/p15738coll2.133702
   Google Scholar
• Ray, S. (2019). A quick review of machine learning algorithms. International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). Faridabad, India. https://doi.org/10.1109/comitcon.2019.8862451
   Google Scholar
• Saeed, H., Ehetisham-Ul-Haq, M., Atiq, M., Kamran, M., Idrees, M., Ali, S., Burhan, M., Mohsan, M., Iqbal, M., Nazir, S., & Il Yasin, S. (2018). Prediction of cotton leaf curl virus disease and its management through resistant germplasm and bio-products. Archives of Phytopathology and Plant Protection, 51(3–4), 170–186. https://doi.org/10.1080/03235408.2018.1443602
   Google Scholar
• Schmitt, M., & Zhu, X. X. (2016). Data fusion and remote sensing: An ever-growing relationship. IEEE Geoscience and Remote Sensing Magazine, 4(4), 6–23. https://doi.org/10.1109/MGRS.2016.2561021
   Web of Science ®Google Scholar
• Shahi, T. B., Xu, C. Y., Neupane, A., & Guo, W. (2023). Recent advances in crop disease detection using UAV and deep learning techniques. Remote Sensing, 15(9), 2450. https://doi.org/10.3390/rs15092450
   Web of Science ®Google Scholar
• Szczypiński, P. M., Strzelecki, M., Materka, A., & Klepaczko, A. (2009). MaZda- A software package for image texture analysis. Computer Methods and Programs in Biomedicine, 94(1), 66–76. https://doi.org/10.1016/j.cmpb.2008.08.005
   PubMed Web of Science ®Google Scholar
• Tariq, M.,Akhtar, M. S., &Anjum, M. S. (2020). Identification of Rice Quality Through Pattern Classification Using Computer Vision Image Processing. Computer Engineering and Intelligent Systems, 11(2), 1–10. 2222–2863.
   Google Scholar
• Wang, Z., Hu, M., & Zhai, G. (2018). Application of deep learning architectures for accurate and rapid detection of internal mechanical damage of blueberry using ­hyperspectral transmittance data. Sensors (Basel), 18(4), 1–17. https://doi.org/10.3390/s18041126
   Web of Science ®Google Scholar
• Yan, J. J., Kuo, H. H., Lin, Y. F., & Liao, T. L. (2016). Real-time driver drowsiness detection system based on PERCLOS and grayscale image processing. International Symposium on Computer, Consumer and Control.Xi’an, China. (pp. 243–246). https://doi.org/10.1109/is3c.2016.72
   Google Scholar
• Zhang, N., Yang, G., Pan, Y., Yang, X., Chen, L., & Zhao, C. (2020). A review of advanced technologies and development for hyperspectral-based plant disease detection in the past three decades. Remote Sensing, 12(19), 3188. https://doi.org/10.3390/rs12193188
   Web of Science ®Google Scholar
• Zubair, M., Zaidi, S. S. A., Shakir, S., Amin, I., & Mansoor, S. (2017). An insight into cotton leaf curl Multan betasatellite, the most important component of cotton leaf curl disease complex. Viruses, 9(10), 280. https://doi.org/10.3390/v9100280
   PubMed Web of Science ®Google Scholar