Crop Yield Prediction at Gram Panchayat Scale using Deep Learning Framework
Lokehwari M
, Girish Kumar Jha , Sunil Kumar Dubey , Rajeev Ranjan Kumar , P. Venkatesh
Paddy yield prediction, deep learning, LSTM, NDVI, Crop Insurance
Crop yield prediction is crucial for assurance of food security, implementation of policies and the evaluation of crop insurance losses from biotic and abiotic stress. This paper aims to explore the strength of spectral vegetation indices, specifically Normalized Difference Vegetation Index (NDVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data accessible through the google earth engine platform for predicting crop yields using deep learning framework. We proposed a long short-term memory neural network model, which captures the temporal dependencies within historically satellite-derived observations and weather patterns. The proposed model is developed for kharif paddy in the Krishna district of Andhra Pradesh state during 2013-2020. The result indicates that, in predicting paddy yield, the proposed model showed considerable superiority over other baseline models such as random forest regression and shallow neural network in terms of root mean square error (88.01 Kg/ha) and R-square value (91.76%). The findings also revealed that NDVI has significant impact on predicting crop yield compared to weather variables. Our study highlights that the proposed deep learning framework offers a simple, scalable, and cost-effective method for reliably predicting paddy yield based on NDVI before harvest. In addition, it is the first attempt to enhance the paddy yield prediction at gram panchayat level in India.
"Crop Yield Prediction at Gram Panchayat Scale using Deep Learning Framework", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 1, page no.577 - 583, January-2024, Available :https://ijsdr.org/papers/IJSDR2401083.pdf
Volume 9
Issue 1,
January-2024
Pages : 577 - 583
Paper Reg. ID: IJSDR_209939
Published Paper Id: IJSDR2401083
Downloads: 000347354
Research Area: Social Science and Humanities
Country: Thirupathur, Tamil Nadu, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
