Assimilation of Deep Learning and Machine Learning Schemes into a Remote Sensing-Incorporated Crop Model to Simulate Barley and Wheat Productivities

Abstract

Deep learning (DL) and machine learning (ML) procedures are prevailing data-driven schemes capable of advancing crop-modelling practices that assimilate these techniques into a mathematical crop model. A DL or ML modelling scheme can effectively represent complicated algorithms. This study reports on an advanced fusion methodology for evaluating the leaf area index (LAI) of barley and wheat that employs remotely sensed information based on deep neural network (DNN) and ML regression approaches. We investigated the most appropriate ML regressors for exploring LAI estimations of barley and wheat through the relationships between the LAI values and four vegetation indices. After analysing ten ML regression models, we concluded that the gradient boost (GB) regressor most effectively estimated the LAI for both barley and wheat. Furthermore, the GB regressor outperformed the DNN regressor, with model efficiencies of 0.89 for barley and 0.45 for wheat. Additionally, we verified that it would be possible to simulate LAI using proximal and remote sensing data based on assimilating the DNN and ML regressors into a process-based mathematical crop model. In summary, we have demonstrated that if DNN and ML schemes are integrated into a crop model, they can facilitate crop growth and boost productivity monitoring.

Combining machine learning and remote sensing-integrated crop modeling for rice and soybean crop simulation

“…This is similar to a recent report on the relationship between VI and aboveground biomass by Liu et al (2023b). These findings corroborate recent studies (Jeong et al, 2022a;Shin et al, 2022) but contradict earlier research suggesting the superiority of DNN techniques (Bui et al, 2020;Sahoo et al, 2020). This discrepancy may highlight the limitations of our dataset's scope and specific characteristics in determining simulation effectiveness.…”

“…These include operational optical satellite sensors, e.g., Jeong et al. (2022a) and remote-controlled aerial systems, e.g., Shin et al. (2022) .…”

“…This has the effect of simplifying the data acquisition process, making it more manageable and less resource-intensive since the current methodology can be directly applied to those using the other RS platforms. These include operational optical satellite sensors, e.g., Jeong et al (2022a) and remote-controlled aerial systems, e.g., Shin et al (2022). Secondly, integrating RS data directly translates to enhanced simulation performance in the RSCM system.…”