Abstract
The use of remotely sensed images in the agriculture sector plays a vital role in knowing crop status on a higher spatial scale. Researchers have developed various indices for this purpose. The Normalized Difference Vegetation Index (NDVI) is an important indices that measure the vegetation vigor of crop. The agricultural community has shown its uses in various applications, viz. crop growth assessment, crop health monitoring, crop yield estimation, etc. The NDVI forecasting helps to make an educated guess, based on its temporal behavior in past years, on likely vegetation conditions ahead of time and thus supports decision-makers to formulate mitigation strategies. In this paper, Moderate-resolution Imaging Spectroradiometer (MODIS) satellite data has been used to calculate the NDVI, and then NDVI forecasting is performed based on 2002–2014 NDVI time-series data. The results are promising and can be used in agricultural applications.
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Acknowledgments
The authors are thankful to UGC for providing BSR fellowship and lab facilities under UGC-SAP (II) DRS Phase-I F.No.-3-42/2009, Phase-II 4-15/2015/DRS-II for this study. The authors are also thankful to the Department of Agriculture, Vaijapur Tehsil, for sharing the necessary information for this research study.
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Gaikwad, S.V., Vibhute, A.D., Kale, K.V. (2022). Development of NDVI Prediction Model Using Artificial Neural Networks. 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_32
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