Browsing by Person "Zimpel, Tobias"

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Predicting herbage biomass on small‐scale farms by combining sward height with different aggregations of weather data
(2024) Scheurer, Luca; Leukel, Joerg; Zimpel, Tobias; Werner, Jessica; Perdana‐Decker, Sari; Dickhoefer, Uta
Accurate predictions of herbage biomass are important for efficient grazing management. Small‐scale farms face challenges using remote sensing technologies due to insufficient resources. This limitation hinders their ability to develop machine learning‐based prediction models. An alternative is to adopt less expensive measurement methods and readily available data such as weather data. This study aimed to examine how different temporal aggregations of weather data combined with compressed sward height (CSH) affect the prediction performance. We considered weather features based on different numbers of weather variables, statistical functions, weather events, and periods. Between 2019 and 2021, data were collected from 11 organic dairy farms in Germany. Herbage biomass exhibited high variability (coefficient of variation [CV] = 0.65). Weather data were obtained from on‐farm and nearby public stations. Prediction models were learned on a training set ( n = 291) and evaluated on a test set ( n = 125). Random forest models performed better than models based on artificial neural networks and support vector regression. Representing weather data by a single feature for leaf wetness reduced the root mean square error (RMSE) by 12.1% (from 536 to 471 kg DM ha −1 , where DM is dry matter) and increased the R 2 by 0.109 (from 0.518 to 0.627). Adding features based on multiple variables, functions, events, and periods resulted in a further reduction in RMSE by 15.9% ( R 2 = 0.737). Overall, different aggregations of weather data enhanced the accuracy of CSH‐based models. These aggregations do not cause additional effort for data collection and, therefore, should be integrated into CSH‐based models for small‐scale farms.
Predicting tilling and seeding operation times in grain production: a comparison of machine learning and mechanistic models
(2025) Scheurer, Luca; Zimpel, Tobias; Leukel, Jörg
Field operations management in grain production requires accurate and timely predictions of operation times for machine tasks. While machine learning (ML) is being adopted more widely in operations management, little is known about its ability to predict tilling and seeding operation times. The aim of this study was to evaluate the prediction performance of ML models for these operation times by using readily available tractor and operations data rather than dynamic environmental data. We collected data between March 2022 and August 2023 from 70 grain fields in the southwest of Germany, including variables such as tractor speed, engine speed, fuel consumption, and field geometry. Operation times exhibited high variability (coefficient of variation [CV] = 0.88). Nine ML algorithms and two conventional mechanistic models proposed by the American Society of Agricultural and Biological Engineers (ASAE EP496.3) were evaluated in a temporal external validation. Random forest (RF) models outperformed all other models, achieving a normalized root mean square error (NRMSE) of 0.215 and a coefficient of determination (R2) of 0.910. Compared to a conventional mechanistic model, the RF model reduced the mean absolute error (MAE) by 37.8 %, and enhanced the R2 by 0.107. The study results highlight the potential of our approach to predict tilling and seeding operation times in grain production without increasing the effort for data collection, offering an accessible and cost-effective solution for resource-constrained grain farming systems that experience data shortages.