Estimation of aboveground biomass of Alfalfa using field robotics

Abstract

Alfalfa is a high-yielding forage crop that is widely grown in the United States for grazing, hay and silage making. A proper maintenance of these grasslands is necessary to ensure optimum productivity and profits. The pre-harvest estimation of biomass yield helps in quantifying the profits and optimizing the forage allocation in advance. Most traditional methods of forage estimation are relatively laborious and time-consuming. Recent developments in contact and remote sensing technologies opened numerous paths for performing aboveground biomass estimation tasks with flexibility and easiness. This study focused on the development of crop height measurement systems for estimating the aboveground biomass yield of Alfalfa (Medicago sativa L.). Five different systems for measuring crop height were evaluated on their ability to estimate aboveground wet and dry biomass. The crop height measurement systems used in this study were Structure-from-Motion, Ultrasound Sensor and Ski, Inertial Measurement Unit and Ski, Inertial Measurement Units and Roller, and a Depth Camera. The results indicated that the system using the Inertial Measurement Unit sensor and ski (IMU-Ski) performed the best among ground-based methods (R2= 0.79; SeY= 3166 kg-wet/ha). The Structure-from-Motion (SfM) method using UAV also provided satisfactory results for biomass predictions (R2= 0.74; SeY= 2543 kg-wet/ha). The models based on IMU-Ski and UAV-based SfM methods were facilitated with vegetation coverage as an additional independent variable to evaluate their effect on biomass predictions. The results indicated that the vegetation coverage did not improve the predictions in any of these systems. Thus, the models based on only the crop height (IMU-Ski and UAV-SfM) were the recommended approaches for Alfalfa biomass estimations. The addition of data points for wide ranges of crop height and vegetation coverage is recommended for future studies to improve the results and ensure the adaptability of these systems in varying environmental conditions.

Plant phenotyping relevance

アルファルファの草丈から地上部バイオマスを推定する複数のフィールドロボティクス・センシング手法を開発・比較評価しており、植物形質の取得と推定が研究の中心である。

Code and data availability

公開論文であることは確認できましたが、現在の公式API・許可済み取得経路では本文を自動取得できませんでした。

No evidence-backed public reproduction asset is currently recorded.