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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adede, C., Oboko, R., Wagacha, P.W., Atzberger, C.: A mixed model approach to vegetation condition prediction using artificial neural networks (ANN): case of Kenya’s operational drought monitoring. Remote Sens. 11(9), 1099 (2019)
Dempewolf, J., et al.: Wheat yield forecasting for Punjab province from vegetation index time series and historic crop statistics. Remote Sens. 6(10), 9653–9675 (2014)
Dhumal, R.K., et al.: A spatial and spectral feature based approach for classification of crops using techniques based on GLCM and SVM. In: Panda, G., Satapathy, S.C., Biswal, B., Bansal, R. (eds.) Microelectronics, Electromagnetics and Telecommunications. LNEE, vol. 521, pp. 45–53. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1906-8_5
Dutta, D., Kundu, A., Patel, N.: Predicting agricultural drought in eastern Rajasthan of India using NDVI and standardized precipitation index. Geocarto Int. 28(3), 192–209 (2013)
Gaikwad, S.V., Vibhute, A.D., Kale, K.V.: Design and implementation of a Web-GIS platform for monitoring of vegetation status. ICTACT J. Image Video Process. 11(3), 2373–2377 (2021)
Gaikwad, S.V., et al.: Drought severity identification and classification of the land pattern using Landsat 8 data based on spectral indices and maximum likelihood algorithm. In: Panda, G., Satapathy, S.C., Biswal, B., Bansal, R. (eds.) Microelectronics, Electromagnetics and Telecommunications. LNEE, vol. 521, pp. 517–524. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1906-8_53
Gaikwad, S.V., et al.: Identification and classification of water stressed crops using hyperspectral data: a case study of Paithan tehsil. In: Krishna, C.R., Dutta, M., Kumar, R. (eds.) Proceedings of 2nd International Conference on Communication, Computing and Networking. LNNS, vol. 46, pp. 911–919. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1217-5_89
Jalili, M., Gharibshah, J., Ghavami, S.M., Beheshtifar, M., Farshi, R.: Nationwide prediction of drought conditions in Iran based on remote sensing data. IEEE Trans. Comput. 63(1), 90–101 (2013)
Liu, X., Zhu, X., Pan, Y., Li, S., Liu, Y., Ma, Y.: Agricultural drought monitoring: progress, challenges, and prospects. J. Geog. Sci. 26(6), 750–767 (2016). https://doi.org/10.1007/s11442-016-1297-9
Mishra, N., Soni, H.K., Sharma, S., Upadhyay, A.: Development and analysis of artificial neural network models for rainfall prediction by using time-series data. Int. J. Intell. Syst. Appl. 10(1) (2018)
Morid, S., Smakhtin, V., Bagherzadeh, K.: Drought forecasting using artificial neural networks and time series of drought indices. Int. J. Climatol. J. R. Meteorol. Soc. 27(15), 2103–2111 (2007)
Reddy, D.S., Prasad, P.R.C.: Prediction of vegetation dynamics using NDVI time series data and LSTM. Model. Earth Syst. Environ. 4(1), 409–419 (2018). https://doi.org/10.1007/s40808-018-0431-3
Vibhute, A.D., Gawali, B.W.: Analysis and modeling of agricultural land use using remote sensing and geographic information system: a review. Int. J. Eng. Res. Appl. 3(3), 081–091 (2013)
Vibhute, A.D., Kale, K., Dhumal, R.K., Mehrotra, S.: Hyperspectral imaging data atmospheric correction challenges and solutions using QUAC and FLAASH algorithms. In: 2015 International Conference on Man and Machine Interfacing (MAMI), pp. 1–6. IEEE (2015)
Zambrano, F., Vrieling, A., Nelson, A., Meroni, M., Tadesse, T.: Prediction of drought-induced reduction of agricultural productivity in Chile from MODIS, rainfall estimates, and climate oscillation indices. Remote Sens. Environ. 219, 15–30 (2018)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-07005-1_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-07004-4
Online ISBN: 978-3-031-07005-1
eBook Packages: Computer ScienceComputer Science (R0)