Enhancing rice yield prediction: a deep fusion model integrating ResNet50-LSTM with multi source data

Abstract

Rice production is pivotal for ensuring global food security. In Pakistan, rice is not only the dominant Kharif crop but also a significant export commodity that significantly impacts the state’s economy. However, Pakistan faces challenges such as abrupt climate change and the COVID-19 pandemic, which affect rice production and underscore the need for predictive models for informed decisions aimed at improving productivity and ultimately the state’s economy. This article presents an innovative deep learning-based hybrid predictive model, ResNet50-LSTM, designed to forecast rice yields in the Gujranwala district, Pakistan, utilizing multi-modal data. The model incorporates MODIS satellite imagery capturing EVI, LAI, and FPAR indices along with meteorological and soil data. Google Earth Engine is used for the collection and preprocessing of satellite imagery, where the preprocessing steps involve data filtering, applying region geometry, interpolation, and aggregation. These preprocessing steps were applied manually on meteorological and soil data. Following feature extraction from the imagery data using ResNet50, the three LSTM model configurations are presented with distinct layer architectures. The findings of this study exhibit that the model configuration featuring two LSTM layers with interconnected cells outperforms other proposed configurations in terms of prediction performance. Analysis of various feature combinations reveals that the selected feature set (EVI, FPAR, climate, and soil variables) yields highly accurate results with an R2 = 0.9903, RMSE = 0.1854, MAPE = 0.62%, MAE = 0.1384, MRE = 0.0062, and Willmott’s index of agreement = 0.9536. Moreover, the combination of EVI and FPAR is identified as particularly effective. Our findings revealed the potential of our framework for globally estimating crop yields through the utilization of publicly available multi-source data.

Keywords

• Rice yield prediction
• Google Earth Engine
• NASA POWER project
• Deep learning
• Remote sensing
• ResNet-LSTM