Predicting Maize Theoretical Methane Yield in Combination with Ground and UAV Remote Data Using Machine Learning.

Abstract

The accurate, timely, and non-destructive estimation of maize total-above ground biomass (TAB) and theoretical biochemical methane potential (TBMP) under different phenological stages is a substantial part of agricultural remote sensing. The assimilation of UAV and machine learning (ML) data may be successfully applied in predicting maize TAB and TBMP; however, in the Nordic-Baltic region, these technologies are not fully exploited. Therefore, in this study, during the maize growing period, we tracked unmanned aerial vehicle (UAV) based multispectral bands (blue, red, green, red edge, and infrared) at the main phenological stages. In the next step, we calculated UAV-based vegetation indices, which were combined with field measurements and different ML models, including generalized linear, random forest, as well as support vector machines. The results showed that the best ML predictions were obtained during the maize blister (R2)-Dough (R4) growth period when the prediction models managed to explain 88-95% of TAB and 88-97% TBMP variation. However, for the practical usage of farmers, the earliest suitable timing for adequate TAB and TBMP prediction in the Nordic-Baltic area is stage V7-V10. We conclude that UAV techniques in combination with ML models were successfully applied for maize TAB and TBMP estimation, but similar research should be continued for further improvements.

Plant phenotyping relevance

UAVマルチスペクトル計測と機械学習を組み合わせ、トウモロコシの地上部バイオマスという植物形質を非破壊推定する手法が研究の中心である。

Code and data availability

The supplied article blocks describe UAV multispectral data collection, vegetation indices, and ML models (GLM, SVM, RF) for maize TAB and TBMP prediction, but contain no data availability statement, no public dataset deposit, no author code repository, and no supplement with phenotyping data or models. The only URLs (

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