Integrating Argonomic Method and Yield Crop Modelling with ML Baseline

Abstract

Predicting agricultural yields using machine learning has been the subject of several research, most of which have concentrated on individual cases. It is possible that their data and techniques won't work with other crops or in different places. Contrarily, machine learning is not used by operating large-scale structures like MARS Crops Harvest Foretelling Systems (MCYFS). When there is a flood of newly disclosed data, ML becomes a potentially useful tool. In order to construct a machine learning foundation for huge-scale crop yield prediction, researchers integrated agronomic concepts of crop modelling with machine learning. A methodology that focusses on accuracy, modularity, and reusability forms the basis. Researchers made sure everything was accurate by using machine learning without leaking any information and by creating characteristics or predictors that could be explained in connection to the development and growth of crops. The characteristics were developed by researchers using information from the MCYFS database, which included meteorological, remote sensing, soil, and crop simulation results.

The researcher focused on a reusable and modular procedure that can accommodate various crops and nations with little configuration adjustments. Using standard input data, the method may be utilised to conduct repeated tests and produce repeatable outcomes. The outcomes act as a springboard for more improvements. In order to compare the performance, the researchers used a straightforward strategy that required no prediction skills and either projected the training set's average for 3 countries like India (IND), Spain (SP) and Ireland (IL) and 5 crops like springs barley, wheat, sunflower, potatoes and sugar beetroot). The researcher forecasted yield at the regional level in the case studies. Additionally, the researchers compared the projections with previous MCYFS forecasts and aggregated them at the national level. Across nations, the normalised RMSE (NRMSE) for the beginning season forecasts was similar. Wheat had an NRMSE of 7.88 for wheat (IND), whereas sweet beetroot had an NRMSE of 8.22 (SP). NRMSEs for potatoes, sugar beetroot and wheat, on the other hand, were double those of MCYFS. At the conclusion of the season, NRMSEs were still similar to MCYFS. Adding more information foundations, creating high analytical characteristics & testing various ML algorithms may all help to enhance the baseline. Large-scale agricultural harvest prediction using ML will be encouraged by the baseline.