Temporal convolutional network based rice crop yield prediction using multispectral satellite data

Early prediction of crop yield has a significant role in ensuring food security. The crop yield depends on several parameters, such as vegetation parameters, climatic parameters, soil condition, etc. Spatial and temporal analysis of cropland is necessary for accurate prediction of yield. Usage of satellite images along with climatic data improves the prediction accuracy. This paper outlines a novel crop yield prediction model for the Paddy from Moderate Resolution Imaging Spectroradiometer (MODIS) data and climatic parameters. Various vegetation indices (VI) are collected from MODIS data for the crop’s entire life cycle. The proposed Temporal Convolutional network (TCN) with a specially designed dilated convolution module predicts the rice crop yield from vegetation indices and climatic parameters. The causal property of TCN and dilated convolution contribute to the multivariate time-based analysis of the crop and results in better performance. •Novel TCN model for rice yield prediction.•Analysis of vegetation indices and climatic parameters for precision agriculture.•Dilated convolution helps in multivariate temporal analysis.•The proposed model reduce the prediction error compared to existing models.