Remote sensing and machine learning for yield prediction of lowland paddy crops

Abstract

Background: Paddy is one of the crops with the largest production worldwide, after corn and wheat. In Indonesia, paddy crops play a role as one of the main boosters of national economic growth based on their contribution to Indonesia's gross domestic product (GDP). Therefore, it is imperative to do research aimed at predicting the yield of paddy crops. Methods: : This research exploits the technology of remote sensing and machine learning methods (i.e. Gradient Boosting Regressor) to predict the yield of lowland paddy crops. Remote sensing with a Landsat 8 satellite was used to obtain the input data in the form of the vegetation index (i.e. NDVI) value, surface temperature, and total pixels of the observed area. Afterward, the input data was arranged into training data by combining paddy yield data and the paddy harvest period. Results: : The obtained training data was modelled to predict the yield of paddy crops using a Gradient Boosting Regressor. The results obtained from experiments conducted in Bandung, Indonesia, showed the scenario with the best parameter combination is an estimator of 2000, a learning rate of 0.001, minimum samples split of 2, and a maximum depth of 4, which has RMSE of 9766.72. Conclusions: : This research succeeded in designing a computational model to predict the yield of lowland paddy crops by involving remote sensing and Gradient Boosting Regressor.

Plant phenotyping relevance

衛星リモートセンシングと機械学習を用いて水稲収量を推定する手法が研究の中心であり、収量という植物・作物形質を直接推定している。

Code and data availability

The paper publicly deposits its underlying phenotyping data (NDVI vegetation index data, Indonesian topographical map, and final combined dataset) on OSF under CC0, and its analysis source code on GitHub with a Zenodo archive. Both are paper-specific, public, and actionable.