Attention mechanism-based deep learning approach for wheat yield estimation and uncertainty analysis from remotely sensed variables

Rapid and accurate crop yield estimation is an imperative aspect of agricultural planning that is important for crop management, food security and commodity trading. There are many related factors affecting wheat yield and the relationship between them and the yield is complicated, with nonlinear spatial-temporal characteristics that are difficult to describe accurately with mathematical functions. Deep learning models can fit complex nonlinear functions efficiently and transform input data into high-dimensional features automatically. However, the feature learning process does not produce transparent information. There has been considerable evidence that the ability of attention mechanism for modeling interpretation has been demonstrated in many fields. Therefore, an attention mechanism-based multi-level crop network (AMCN) was proposed to estimate the county level wheat yield based on remote sensing data and meteorological data. To explore the difference in spatio-temporal feature extraction ability under parallel and series structures when combining CNN with LSTM, we designed the AMCN models with two forms of structure, one is a parallel module of LSTM and CNN (AMCN1) and the other is a serial connection module between LSTM and CNN (AMCN2). Our results showed that the AMCN1 model provided an improved estimation accuracy as compared to that of the AMCN2 model. We also found remote sensing data contributed significantly to crop yield estimation mainly at the late growth stages, meteorological data provided additional information mainly at the early growth stage. We assessed the estimated uncertainty using Monte Carlo dropout, and the results indicated that the uncertainty level decreased gradually as the growth stages proceeded. In addition, extreme events such as drought and uneven distribution characteristics of the samples were associated with much higher estimated uncertainties. The study highlighted that the proposed model provided more accurate yield estimations by taking advantage of multi-level crop networks while considering the uncertainty involved in model estimations.