Abstract
Research into agriculture has been gaining steam and displaying signals of significant expansion over the last several years. The most recent to emerge, using a variety of computer technologies in deep learning and remote sensing are simplifying agricultural tasks. The classification of agricultural land cover by humans necessitates a large team of experts and is time-consuming when dealing with vast areas. To implement this project, we have utilized the machine learning and deep learning algorithms to classify the land cover using the RGB version of the EuroSat dataset. This helps to differentiate the agricultural land from the other distinct landscapes aiding the farmers to determine the fine land for cultivation of crops. In this study, we examined the competence between four classifiers namely K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and the pretrained ResNet50 model, which is a convolutional neural network (CNN) technique, are all machine learning classifiers. However, few research have examined the performances of these classifiers with different training sample sizes for the same remote sensing photos, with a particular focus on Sentinel-2 Multispectral Imager images (MSI). Finally, the accuracy rates of the machine learning methods were only fair, with the ResNet50 model producing the best results with a 97.46% accuracy rate.
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References
Helber, P., Bischke, B., Dengel, A., Borth, D.: EuroSAT: a novel dataset and deep learning benchmark for land use and land cover classification. IEEE J. Selected Topics Appl. Earth Observations Remote Sens. (2019)
Li, J., Lin, D., Wang, Y., Xu, G., Zhang, Y., Ding, C., Zhou, Y.: Deep discriminative representation learning with attention map for scene classification. In: Remote Sensing (MDIP) (2020)
Oo, T. K., Arunrat, N., Sereenonchai, S., Ussawarujikulchai, A., Chareonwong, U., Nutmagul, W.: Comparing four machine learning algorithms for land cover classification in gold mining: a case study of Kyaukpahto Gold Mine, Northern Myanmar. In: Sustainability (MDIP) (2022)
Yassine, H., Tout, K., Jaber, M.: Improving lulc classification from satellite imagery using deep learning—eurosat dataset. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2021)
Naushad, R., Kaur, T., Ebrahim, G.: Deep transfer learning for land use and land cover classification: a comparative study. In: Sensors (2021)
Asawa, T.S., Balaji, V., Helwatkar, T.: Deep ensemble learning for agricultural land mapping and classification from satellite images. Int. J. Eng. Res. Technol. (2021)
Goldblatt, R., Stuhlmacher, M.F., Tellman, B., Clinton, N., Hanson, G., Georgescu, M., Wang, C., Serrano- Candela, F., Khandelwal, A.K., Cheng, W.H., Balling, R.C.: Using Eurosat and nighttime lights for supervised pixel-based image classification of urban land cover. Remote Sens. Environ. 205, 253–275 (2018)
Samaniego, L., Schulz, K.: Supervised Classification of Agricultural Land Cover Using a Modified k-NN Technique (MNN) and Landsat Remote Sensing Imagery (2009)
Gharbia, R., Hassanien, A.E., El-Baz, A.H., Elhoseny, M., Gunasekaran, M.: Multi-spectral and panchromatic image fusion approach using stationary wavelet transform and swarm flower pollination optimization for remote sensing applications. In: Future Generation Computer Systems (2018)
Patel, N.N., Angiuli, E., Gamba, P., Gaughan, A., Lisini, G., Stevens, F.R., Tatem, A.J., Trianni, G.: Multitemporal settlement and population mapping from Eurosat using Google Earth Engine. Int. J. Appl. Earth Obs. Geoinf. 35, 199–208 (2015)
Pelletier, C., Webb, G. I., Petitjean, F.: Deep Learning for the Classification of Sentinel-2 Image Time Series (2019)
Gharbia, R., El-Baz, A.H., Hassanien, A.E.: An adaptive image fusion rule for remote sensing images based on the particle swarm optimization. In: 2016 International Conference on Computing, Communication, and Automation (ICCCA), pp. 1080–1085. IEEE (2016)
Ban, Y., Jacob, A., Gamba, P.: Spaceborne SAR data for global urban mapping at 30 m resolution using a robust urban extractor. ISPRS J. Photogramm. Remote Sens. 103, 28–37 (2015)
Ndikumana, E., Minh, D.H.T., Baghdadi, N., Courault, D., Hossard, L.: Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, France (2018)
Wu, S.S., Qiu, X., Usery, E.L., Wang, L.: Using geometrical, textural, and contextual information of land parcels for the classification of detailed urban land use. Ann. Assoc. Am. Geogr. 99(1), 76–98 (2009)
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)
Vibhute, A.D., Gawali, B.W.: Analysis and Modeling of Agricultural Land use using Remote Sensing and Geographic Information System (2013)
Khalifa, N.E.M., Taha, M.H.N., Hassanien, A.E.: Aquarium family fish species identification system using deep neural networks. In: International Conference on Advanced Intelligent Systems and Informatics, pp. 347–356. Springer, Cham (2018)
Wong, S.C., Gatt, A., Stamatescu, V., McDonnell, M.D.: Understanding Data Augmentation for Classification: When to Warp? (2016).arXiv preprint arXiv:1609.08764
Pavan Kumar, C.S., Dhinesh Babu, L.D.: Novel text preprocessing framework for sentiment analysis. In: Satapathy, S., Bhateja, V., Das, S. (eds.) Smart Intelligent Computing and Applications. Smart Innovation, Systems and Technologies, vol. 105. Springer (2019)
Pavan Kumar, C.S., Dhinesh Babu, L.D.: Fuzzy Based Feature Engineering Architecture for Sentiment Analysis of Medical Discussion over Online Social Networks, pp. 11749–11761 (2021)
Varun, P.S., Manohar, G.L., Kumar, T.S., Pavan Kumar, C.S.: Novel sentiment analysis model with modern bio-NLP techniques over chronic diseases. In: Satapathy, S.C., Peer, P., Tang, J., Bhateja, V., Ghosh, A. (eds.) Intelligent Data Engineering and Analytics. Smart Innovation, Systems and Technologies, vol. 266. Springer (2022)
Kumar, C.S.P., Babu, L.D.D.: Evolving dictionary based sentiment scoring framework for patient authored text. Evol. Intel. 14, 657–667 (2021)
Agarwal, R., Jain, R., Regunathan, R., Pavan Kumar, C.S.: Automatic attendance system using face recognition technique. In: Kulkarni, A., Satapathy, S., Kang, T., Kashan, A. (eds.) Proceedings of the 2nd International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol. 828 (2019)
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Bharghavi, Y.M., Pavan Kumar, C.S., Lakshmi, Y.H., Sri Vyshnavi, K.P. (2023). Agriculture Land Image Classification Using Machine Learning Algorithms and Deep Learning Techniques. In: Bhateja, V., Yang, XS., Ferreira, M.C., Sengar, S.S., Travieso-Gonzalez, C.M. (eds) Evolution in Computational Intelligence. FICTA 2023. Smart Innovation, Systems and Technologies, vol 370. Springer, Singapore. https://doi.org/10.1007/978-981-99-6702-5_19
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