Volume 15 , Issue 2 , PP: 65-77, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Daniel Arockiam 1 * , Azween Abdullah 2 , Valliappan Raju 3
Doi: https://doi.org/10.54216/JCIM.150206
At present, the application of remote sensing (RS) data achieved from satellite imagery or unmanned aerial vehicles (UAV) has become common for crop classification procedures, i.e. crop mapping, soil classification, or prediction of yield. The classification of food crop utilizing RS images (RSI) is one of the major applications of RS in farming. It contains the usage of aerial or satellite images for classifying and identifying dissimilar kinds of food crops developed in an exact region. This data is beneficial for estimation of yield, crop monitoring, and land management. Meeting the conditions for examining these data needs more refined techniques and artificial intelligence (AI) technologies, which deliver essential support. Recently, the usage of deep learning (DL) for crop type classification with RS images could help sustainable farming practices by providing appropriate and precise data on the kinds and features of crops. In this study, we offer an Automated Agricultural Crop Type Mapping Utilizing Fusion of Transfer Learning and Tasmanian Devil Optimization (AACTM-FTLTDO) algorithm on Remote Sensing Imagery. The primary goal of the AACTM-FTLTDO methodology is to accurately detect and classify crop types for more precise agricultural monitoring using remote sensing technologies. To accomplish that, the AACTM-FTLTDO model employs a fusion of transfer learning techniques involving three models such as SqueezeNet, CapsNet, and ShuffleNetV2 to capture diverse, multi-scale spatial and spectral features. For the crop type classification and detection process, the auto-encoder (AE) classifier can be employed. Eventually, the tasmanian devil optimization (TDO) technique was deployed to modify the hyperparameter of the AE technique for ensuring optimal model configurations and reducing computational complexity. A wide range of experimentation studies is made and the results are examined under numerous measures. The comparative study shows that the AACTM-FTLTDO technique performs better than existing approaches
Crop Type Mapping , Remote Sensing Imagery , Tasmanian devil Optimization , Transfer Learning , Agricultural
[1] U. Patel, M. Pathan, P. Kathiria, and V. Patel, ‘‘Crop type classification with hyperspectral images using deep learning: A transfer learning approach,’’ Model. Earth Syst. Environ., vol. 9, no. 2, pp. 1977–1987, Jun. 2023.
[2] Li, H., Dou, X., Tao, C., Wu, Z., Chen, J., Peng, J., Deng, M. and Zhao, L., 2020. RSI-CB: A large-scale remote sensing image classification benchmark using crowdsourced data. Sensors, 20(6), p.1594.
[3] K. Bhosle and B. Ahirwadkar, ‘‘Deep learning convolutional neural network (CNN) for cotton, mulberry and sugarcane classification using hyperspectral remote sensing data,’’ J. Integr. Sci. Technol., vol. 9, no. 2, pp. 70–74, 2021.
[4] M. A. Duhayyim, H. Alsolai, S. B. H. Hassine, J. S. Alzahrani, A. S. Salama, A. Motwakel, I. Yaseen, and A. S. Zamani, ‘‘Automated deep learning driven crop classification on hyperspectral remote sensing images,’’ Comput., Mater. Continua, vol. 74, no. 2, pp. 3167–3181, 2023.
[5] P. Sadeghi-Tehran, N. Virlet, and M. J. Hawkesford, ‘‘A neural network method for classification of sunlit and shaded components of wheat canopies in the field using high-resolution hyperspectral imagery,’’ Remote Sens., vol. 13, no. 5, p. 898, Feb. 2021.
[6] Wei, W., Polap, D., Li, X., Woźniak, M. and Liu, J., 2018, November. Study on remote sensing image vegetation classification method based on decision tree classifier. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 2292-2297). IEEE.
[7] K. Bhosle and V. Musande, ‘‘Evaluation of deep learning CNN model for land use land cover classification and crop identification using hyperspectral remote sensing images,’’ J. Indian Soc. Remote Sens., vol. 47, no. 11, pp. 1949–1958, Nov. 2019.
[8] Shi, J., Zhang, H., Jiang, Z. and Meng, G., 2019, October. Crop classification based on lightened convolutional neural networks in multispectral images. In Image and Signal Processing for Remote Sensing XXV (Vol. 11155, pp. 660-667). SPIE.
[9] Y. Zhang, D. Wang, and Q. Zhou, ‘‘Advances in crop fine classification based on hyperspectral remote sensing,’’ in Proc. 8th Int. Conf. AgroGeoinformatics (Agro-Geoinformatics), Jul. 2019, pp. 1–6.
[10] Elngar, A.A., Arafa, M., Fathy, A., Moustafa, B., Mahmoud, O., Shaban, M. and Fawzy, N., 2021. Image classification based on CNN: a survey. Journal of Cybersecurity and Information Management, 6(1), pp.18-50.
[11] Bouacida, I., Farou, B., Djakhdjakha, L., Seridi, H. and Kurulay, M., 2024. Innovative deep learning approach for cross-crop plant disease detection: A generalized method for identifying unhealthy leaves. Information Processing in Agriculture.
[12] Alajmi, M., Mengash, H.A., Eltahir, M.M., Assiri, M., Ibrahim, S.S. and Salama, A.S., 2023. Exploiting hyperspectral imaging and optimal deep learning for crop type detection and classification. IEEE Access, 11, pp.124985-124995.
[13] Amri, E., Gulzar, Y., Yeafi, A., Jendoubi, S., Dhawi, F. and Mir, M.S., 2024. Advancing automatic plant classification system in Saudi Arabia: introducing a novel dataset and ensemble deep learning approach. Modeling Earth Systems and Environment, 10(2), pp.2693-2709.
[14] Alahmari, S., Yonbawi, S., Racharla, S., Lydia, E.L., Ishak, M.K., Alkahtani, H.K., Aljarbouh, A. and Mostafa, S.M., 2023. Hybrid Multi-Strategy Aquila Optimization with Deep Learning Driven Crop Type Classification on Hyperspectral Images. Comput. Syst. Sci. Eng., 47(1), pp.375-391.
[15] Goyal, P., Sharda, R., Saini, M. and Siag, M., 2024. A deep learning approach for early detection of drought stress in maize using proximal scale digital images. Neural Computing and Applications, 36(4), pp.1899-1913.
[16] Haridasan, A., Thomas, J. and Raj, E.D., 2023. Deep learning system for paddy plant disease detection and classification. Environmental monitoring and assessment, 195(1), p.120.
[17] Kathole, A.B., Vhatkar, K.N. and Patil, S.D., 2024. IoT-enabled pest identification and classification with new meta-heuristic-based deep learning framework. Cybernetics and Systems, 55(2), pp.380-408.
[18] Duan, R., Lin, D. and Fathi, G., 2024. PEMFC model identification using a squeezenet developed by modified transient search optimization algorithm. Heliyon, 10(6).
[19] Zhai, H. and Zhao, J., 2024. Two-Stream spectral-spatial convolutional capsule network for Hyperspectral image classification. International Journal of Applied Earth Observation and Geoinformation, 127, p.103614
[20] Yu, Y.N., Xiong, C.L., Yan, J.C., Mo, Y.B., Dou, S.Q., Wu, Z.H. and Yang, R.F., 2024. Citrus Pest Identification Model Based on Improved ShuffleNet. Applied Sciences, 14(11), p.4437.
[21] Boukhris, A., Jilali, A. and Asri, H., 2024. Deep Learning and Machine Learning Based Method for Crop Disease Detection and Identification Using Autoencoder and Neural Network. Revue d'Intelligence Artificielle, 38(2)
[22] Almotairi, S., Addula, S.R., Alharbi, O., Alzaid, Z., Hausawi, Y.M. and Almutairi, J., Personal Data Protection Model in IOMT-Blockchain on Secured Bit-Count Transmutation Data Encryption Approach.
[23] http://rsidea.whu.edu.cn/resource_WHUHi_sharing.htm
[24] Y. Zhong, X. Hu, C. Luo, X. Wang, J. Zhao, and L. Zhang, “WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF”, Remote Sens. Environ., vol. 250, pp. 112012, 2020.
