###### Abstract  
As the second largest rice producer, India contributes about 20% of the world's rice production. Timely, accurate, and reliable rice yield prediction in India is crucial for global food security and health issues. Deep learning models have achieved excellent performances in predicting crop yield. However, the interpretation of deep learning models is still rare. In this study, we proposed a transformer-based model, Informer, to predict rice yield across the Indian Indo-Gangetic Plains by integrating time-series satellite data, environmental variables, and rice yield records from 2001 to 2016. The results showed that Informer had better performance (\(R^{2}=0.81\), \(RMSE=0.41\) t/ha) than four other machine learning and deep learning models for end-of-season prediction. For within-season prediction, the Informer model could achieve stable performances (\(R^{2}\approx 0.78\)) after late September, which indicated that the optimal prediction could be achieved 2 months before rice maturity. In addition, we interpreted the prediction models by evaluating the input feature importance and analyzing hidden features. The evaluation of feature importance indicated that NIRv was the most critical factor, while intervals 6 (mid-August) and 12 (mid-November) were the key periods for rice yield prediction. The hidden feature analysis demonstrated that the attention-based long short-term memory (AtLSTM) model accumulated the information of each growth period, while the Informer model focused on the information around intervals 5 to 6 (August) and 11 to 12 (November). Our findings provided a reliable and simple framework for crop yield prediction and a new perspective for explaining the internal mechanism of deep learning models. crop yield prediction; remote sensing; deep learning; feature importance; attention +
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In recent decades, popular crop yield prediction methods include field surveys, crop growth models, and statistical machine learning models [9; 10; 11]. Traditionally, field surveys are used to estimate crop yield by collecting information from farmers. Field surveys can capture the actual growth of crops, but this method is time-consuming, laborious, and difficult to be applied on large scales [12; 13]. Due to these limitations, crop growth models and statistical machine learning models are more reliable and convenient for large-scale predictions. Crop growth models can simulate crop growth, plant carbon assimilation, and phenology using the information of meteorology, soil, phenology, and management [14]. However, these models require sufficient field data to adjust the specific parameters [15], which restricts their performances at the regional-scale application [16]. Compared with crop growth models, machine learning methods can reduce dependence on crop-specific parameters as remote sensing technology advances and describe nonlinear relationships between input features and crop yield. Therefore, many machine learning approaches have been applied to predict crop yield at the regional scales based on climate and remote sensing data [17; 10; 18; 19]. For instance, [PERSON] et al. [17] applied four machine learning methods, including least absolute shrinkage and selection operator (LASSO), ridge, random forest (RF), extreme gradient boosting (XGBoost), and their ensembles as metamodels, to predict maize yield and found that XGBoost had the best performance with a relative mean square error (_RMSE_) of 13.5%. [PERSON] et al. [18] used RF, multiple linear regression (MLR), and artificial neural network (ANN) to estimate the sugarcane yield, and the results showed that the prediction of RF was the closest to the observed yield. In addition, deep learning methods can capture complex relationships from high-dimensional data and recognize sequential patterns of time-series data [20; 21].