What question did this study set out to answer?

To automate the detection of field tasks performed by tractors using time series data and deep learning techniques.

February 12, 2026Open Access

Time series analysis of agricultural machines for automated field task detection based on a deep-learning approach

LBLena Bunge KSKatharina Stahl AWAndreas WübbekePaderborn University

Key Points

To automate the detection of field tasks performed by tractors using time series data and deep learning techniques.
Utilized time-series data measured by tractors
Applied deep learning methods, specifically CNN and GRU
Compared results with previous approaches to evaluate performance
The deep learning approach showed promising performance in task classification
Generalization potential remains limited across different farms and machine types
Additional data from diverse sources is essential for better training and accuracy

Abstract

Documentation of field tasks for tractors is a manual, error prone task for farmers. We address this problem by exploiting the data measured by tractors and classifying the tractor’s work type with deep learning approaches: CNN and GRU. Compared to our and other previous approaches, the time-series based evaluation shows promising performance. At the same time, we show that the approach still lacks generalization potential concerning different farms and machine vendors. More data from different farms and machine types is still missing for sufficient training.

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Cite This Study

Bunge et al. (Thu,) studied this question.

synapsesocial.com/papers/698d6ebb5be6419ac0d5478e https://doi.org/https://doi.org/10.18420/giljt2026_01

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