National Academy of Agricultural Sciences (NAAS)
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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706 Issues : 12 per year Publisher : Excellent Publishers Email : editorijcmas@gmail.com / submit@ijcmas.com Editor-in-chief: Dr.M.Prakash Index Copernicus ICV 2018: 95.39 NAAS RATING 2020: 5.38 |
Wheat (Triticum spp.) stands as one of the most critical staple crops in the global agricultural landscape. Wheat cultivation is consistently threatened by various biotic and abiotic factors. Among these, rust diseases, caused by the fungal pathogen, poses a significant threat to wheat yields globally. In recent years, advances in artificial intelligence (AI) and machine learning (ML), particularly through the application of convolutional neural networks (CNNs), have begun to revolutionize the detection and management of disease in crop plants. Recent progress in in-depth remote sensing and learning technologies have considerably improved the capacity of convolutional neural networks (RCNN) based on the region in the detection and control of wheat rust, a critical threat to global food security. Machine learning based detection techniques, emphasizing the importance of integrating advanced methodologies such as RCNN have shown promise to improve precision in the identification of wheat rust diseases. Studies have demonstrated the effectiveness of hybrid models that combine the extraction of regions with automatic learning algorithms to classify rust diseases, resulting in better diagnostic precision. In addition, the merger of multiple techniques has shown an improvement in the performance of the classification of diseases. This convergence of technologies, including the analysis of ground imaging contributes to the development of more robust agricultural practices, ultimately supporting sustainable agriculture initiatives and furthering wheat improvement.
Adem, K., Y?lmaz, E.K., Ölmez, F., Çelik, K. and Bak?r, H., 2024. A comparative analysis of deep learning parameters for enhanced detection of yellow rust in wheat. International Journal of Engineering Research and Development, 16(2), pp.659-667.
Barbedo J G A. 2018. Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification. Computers and Electronics in Agriculture 153: 46–53. http://dx.doi.org/10.1016/j.compag.2018.08.013
Bashish A D, Braik M and Bani-Ahmad S. 2010. A framework for detection and classification of plant leaf and stem diseases. (In) Signal and Image Processing International Conference, IEEE.
Boulent J, Foucher S, Théau J and St-Charles P L. 2019. Convolutional neural networks for the automatic identification of plant diseases. Frontiers in plant science 10. https://doi.org/10.3389/fpls.2019.00941
Carbonneau P.E., B. Belletti, M. Micotti, B. Lastoria, M. Casaioli, S. Mariani, G. Marchetti, S. Bizzi (2020) UAV-based training for fully fuzzy classification of Sentinel-2 fluvial scenes. Earth Surf. Proc. Land., 45 (13) (2020), pp. 3120-3140 http://dx.doi.org/10.1002/esp.4955
Chen X (2020) Pathogens which threaten food security: Puccinia striiformis, the wheat stripe rust pathogen Food Secur, 12 (2) (2020), pp. 239-251 http://dx.doi.org/10.1007/s12571-020-01016-z
DeChant C, Wiesner-Hanks T, Chen S, Stewart E L, Yosinski J, Gore M A and Lipson H. 2017. Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology 107(11): 1426–32. https://doi.org/10.1094/phyto-11-16-0417-r
Ferentinos K P. 2018. Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture 145: 311–8. https://doi.org/10.1016/j.compag.2018.01.009
Gränzig T., F.E. Fassnacht, B. Kleinschmit, M. Förster (2021) Mapping the fractional coverage of the invasive shrub Ulex europaeus with multi-temporal Sentinel-2 imagery utilizing UAV orthoimages and a new spatial optimization approach. Int. J. Appl. Earth Obs. Geoinf., 96 (2021), p. 102281, https://doi.org/10.1016/j.jag.2020.102281
Hao Y., Y. Liu, Z. Wu, L. Han, Y. Chen, G. Chen, L. Chu, S. Tang, Z. Yu, Z. Chen (2021) Edgeflow: Achieving practical interactive segmentation with edge-guided flow. Paper presented at: Proceedings of the IEEE/CVF International Conference on Computer Vision https://doi.org/10.48550/arXiv.2109.09406
He, Kaiming, Xiangyu Zhang, Shaoqing Ren and Jian Sun. 2016. "Deep residual learning for image recognition." (In) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770–78. https://doi.org/10.1109/CVPR.2016.90
Hungilo G G, Emmanuel G and Emanuel A W. 2019. Image processing techniques for detecting and classification of plant disease: A review. (In) Proceedings of the 2019 International Conference on Intelligent Medicine and Image Processing, pp 48–52. http://dx.doi.org/10.1145/3332340.3332341
Jadhav S B, Udupi V R and Patil S B. 2020. Identification of plant diseases using convolutional neural networks. International Journal of Information Technology 1–10. http://dx.doi.org/10.1007/s41870-020-00437-5
Jain R, Minz S and Ramasubramanian V. 2009. Machine learning for forewarning crop diseases. Journal of Indian Society of Agricultural Statistics 63(1): 97–107.
Kamboj D, S Kumar, CN Mishra, P Srivastav, G Singh and GP Singh. 2020. Marker assisted breeding in cereals: Progress made and challenges in India. Journal of Cereal Research 12(2): 85-102. https://doi.org/10.25174/2582-2675/2020/104208
Kamilaris A and Boldú P F X. 2018. Deep learning in agriculture: A survey. Computers and Electronics in Agriculture 147: 70–90. https://doi.org/10.1016/j.compag.2018.02.016
Khalid, H., 2024, May. Portable Edge Computing System Analysis for Real-time Detection and Classification of Wheat Yellow Rust Infection Types Using Embedded AI. In 2024 International Conference on Engineering & Computing Technologies (ICECT) (pp. 01-04). IEEE. http://dx.doi.org/10.1109/ICECT61618.2024.10581008
Kumar S, G Singroha, SC Bhardwaj, R Bala, MS Saharan, V Gupta, A Khan, S Mahapatra, M Sivasamy, V Rana, CN Mishra, P Sharma, O Prakash, A Verma, I Sharma, R Chatrath and GP Singh. 2019. Multienvironmental evaluation of wheat germplasm identifies donors with multiple fungal disease resistance. Genetic Resources and Crop Evolution 66: 797-808. https://link.springer.com/article/10.1007/s10722-019-00751-3
Kumar, D. and Kukreja, V., 2024. Image segmentation, classification, and recognition methods for wheat diseases: Two Decades’ systematic literature review. Computers and Electronics in Agriculture, 221, p.109005. http://dx.doi.org/10.1016/j.compag.2024.109005
Kumar, D., Kukreja, V. and Singh, A., 2024. A novel hybrid segmentation technique for identification of wheat rust diseases. Multimedia Tools and Applications, 83(29), pp.72221-72251.
LeCun Y, Bengio Y and Hinton G. 2015. Deep learning. Nature 521(7553): 436-44. http://dx.doi.org/10.1038/nature14539
Lee S H, Chan C S, Wilkin P and Remagnino P. 2015. Deep plant: Plant identification with convolutional neural networks. (In) International Conference on Image Processing. IEEE, pp 452–56. https://doi.org/10.48550/arXiv.1506.08425
Lu Y, Yi S, Zeng N, Liu Y and Zhang Y. 2017. Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267: 378–84. http://dx.doi.org/10.1016/j.neucom.2017.06.023
Mandava, M., Vinta, S.R., Ghosh, H. and Rahat, I.S., 2024. Identification and Categorization of Yellow Rust Infection in Wheat through Deep Learning Techniques. EAI Endorsed Transactions on Internet of Things, 10.
Mishra, K. and Kaur, K., 2024, October. Advanced Image Processing and Feature Extraction Approaches For Detecting Wheat Yellow Rust Disease. In 2024 First International Conference on Innovations in Communications, Electrical and Computer Engineering (ICICEC) (pp. 1-7). IEEE.
Nigam, S., Jain, R., Singh, V.K., Jain, S., Marwaha, S. and Arora, A., 2024. Implementing Artificial Intelligence in Wheat Disease Identification: A Mobile Application Approach. In Diseases of Field Crops: Diagnostics and Management (pp. 169-185). Singapore: Springer Nature Singapore. http://dx.doi.org/10.1007/978-981-97-6160-9_8
Trethowan R, R Chatrath, R Tiwari, S Kumar, MS Saharan, NS Bains, VS Sohu, P Srivastava, A Sharma, N De, S Prakash, GP Singh, I Sharma, H Eagles, S Diffey, U Bansal, H Bariana. 2018. An analysis of wheat yield and adaptation in India. Field Crops Research 219: 192-213.
Vimala, G.A.G., Raj, A.A. and Bharathi, C.R., 2024, November. Deep Learning-based Image Processing for Early Detection of Stripe Rust in Wheat Crops using CNN Model. In 2024 8th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 162-166). IEEE.
Xie E., W. Wang, Z. Yu, A. Anandkumar, J.M. Alvarez, P. Luo (2021) SegFormer: Simple and efficient design for semantic segmentation with transformers Advances in Neural Information Processing Systems 34
Zhang T., Z. Xu, J. Su, Z. Yang, C. Liu, W.-H. Chen, J. Li (2021) Ir-UNet: Irregular Segmentation U-Shape Network for Wheat Yellow Rust Detection by UAV Multispectral Imagery Rem. Sens., 13 (19) (2021), p. 3892, https://doi.org/10.3390/rs13193892
Zheng S., J. Lu, H. Zhao, X. Zhu, Z. Luo, Y. Wang, Y. Fu, J. Feng, T. Xiang, P.H. Torr (2021) Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. Paper presented at: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
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