Abstract
In this paper, we have proposed a novel clustering-based approach for the prediction of the quality index of agricultural land. This paper aims to develop a system that can predict the quality index of the land based on the hyperspectral data acquired for the specified region over the period of 6 years. Dataset collected for this research consists of hyperspectral images of agricultural lands belonging to different geographical regions in India. Google earth engine platform has been used to collect the hyperspectral data of the Landsat 8 satellite. Autoencoder and k-means clustering algorithms are used for high-performance dimensionality reduction and clustering of the data respectively. Insurance agencies, NGOs, government bodies can get the benefits from the proposed methodology for client claim verification or for conducting agricultural surveys on large scale. As the data can be collected from a remote location, experts won’t need to visit the field each time.
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References
Singh, P., et al.: Hyperspectral remote sensing in precision agriculture: present status, challenges, and future trends. In: Hyperspectral Remote Sensing, pp. 121–146 (2020)
Cetin, H., Pafford, J.T., Mueller, T.G.: Precision agriculture using hyperspectral remote sensing and GIS. In: Proceedings of 2nd International Conference on Recent Advances in Space Technologies, RAST 2005 (2005)
Shwetank, J. K., Bhatia, K.: Hyperspectral data compression model using SPCA (segmented principal component analysis) and classification of rice crop varieties. In: Ranka, S., et al. (eds.) IC3 2010. CCIS, vol. 94, pp. 360–372. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14834-7_34
Stroppiana, D., et al.: Rice yield estimation using multispectral data from UAV: A preliminary experiment in northern Italy. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (2015)
Wijitdechakul, J., Sasaki, S., Kiyoki, Y., Koopipat, C.: UAV-based multispectral image analysis system with semantic computing for agricultural health conditions monitoring and real-time management. In. 2016 International Electronics Symposium (IES) (2016)
Wyawahare, M., Kulkarni, P., Kulkarni, A., Lad, A., Majji, J., Mehta, A.: Agricultural field analysis using satellite surface reflectance data and machine learning technique. In: Singh, M., Gupta, P.K., Tyagi, V., Flusser, J., Ören, T., Valentino, G. (eds.) ICACDS 2020. CCIS, vol. 1244, pp. 439–448. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-6634-9_40
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R.: Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017)
Arabameri, A., Pourghasemi, H.R.: Spatial modeling of gully erosion using linear and quadratic discriminant analyses in GIS and R. In: Spatial Modeling in GIS and R for Earth and Environmental Sciences, pp. 299–321 (2019)
Alkhayrat, M., Aljnidi, M., Aljoumaa, K.: A comparative dimensionality reduction study in telecom customer segmentation using deep learning and PCA. Journal of Big Data 7(1), 1–23 (2020). https://doi.org/10.1186/s40537-020-0286-0
Alla, S., Adari, S.K.: Autoencoders. In: Beginning Anomaly Detection Using Python-Based Deep Learning, pp. 123–178 (2019)
Sakketou, F., Ampazis, N.: On the invariance of the SELU activation function on algorithm and hyperparameter selection in neural network recommenders. In: MacIntyre, J., Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds.) AIAI 2019. IAICT, vol. 559, pp. 673–685. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-19823-7_56
Subasi, A.: Clustering examples. In: Practical Machine Learning for Data Analysis Using Python, pp. 465–511 (2020)
Marutho, D., Hendra Handaka, S., Wijaya, E., Muljono: The determination of cluster number at k-mean using elbow method and purity evaluation on headline news. In: International Seminar on Application for Technology of Information and Communication (2018)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Pal, K., Patel, B.V.: Data classification with k-fold cross validation and holdout accuracy estimation methods with 5 different machine learning techniques. In: Fourth International Conference on Computing Methodologies and Communication (ICCMC) (2020)
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Kulkarni, P., Wyawahare, M., Karwande, A., Kolhe, T., Kamble, S., Joshi, A. (2022). Agricultural Field Analysis Using Satellite Hyperspectral Data and Autoencoder. In: Santosh, K., Hegadi, R., Pal, U. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2021. Communications in Computer and Information Science, vol 1576. Springer, Cham. https://doi.org/10.1007/978-3-031-07005-1_31
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