Prediction of paddy cultivation using deep learning on land cover variation for sustainable agriculture
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Geospatial analytics is a promising method of spatial data processing and analysis. This study presents a deep learning-based geospatial analytics model to classify the satellite images and geographical information system (GIS) data to estimate the agricultural land area under paddy cultivation. The fine-tuned predictive model is validated against GIS data, followed by an evaluation scenario of a selected paddy cultivation area. Deep learning-based geospatial land usage monitoring can be both conceptually and practically appealing to efficiently identify and respond to the diverse needs of agents in the paddy supply chain. The information with a fine-tuned predictive analysis model can lead to possible policy implications for sustainable agriculture directives of the Sri Lankan government.
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