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Related Experiment Video

Updated: Nov 23, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

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Robust Species Distribution Mapping of Crop Mixtures Using Color Images and Convolutional Neural Networks.

Søren Kelstrup Skovsen¹, Morten Stigaard Laursen¹, Rebekka Kjeldgaard Kristensen²

¹Department of Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark.

Sensors (Basel, Switzerland)

|January 1, 2021

Summary

Accurately mapping crop species composition using AI-powered image analysis can optimize targeted management. This study introduces a convolutional neural network (CNN) method for precise biomass estimation in grass-clover mixtures.

Keywords:

deep learning grass clover mixtures mixed crop mapping precision agriculture proximity sensing species composition estimation targeted fertilization

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Area of Science:

Agricultural Science
Computer Science
Remote Sensing

Background:

Crop mixtures enhance resource use and yield stability but require precise species composition data for targeted management.
Current field surveys for fine-grained species mapping are costly and often unfeasible, limiting targeted management strategies.
Understanding within-field species variation is crucial for optimizing agricultural practices and crop value.

Purpose of the Study:

To develop and evaluate a novel method for determining biomass species composition from high-resolution color images.
To enable precise, large-scale mapping of within-field species distribution for improved agricultural management.
To assess the accuracy and feasibility of a deep learning approach for analyzing mixed-crop canopies.

Main Methods:

Utilized a DeepLabv3+ based convolutional neural network (CNN) to analyze high-resolution color images of crop canopies.
Collected data across four experimental sites over three growing seasons, focusing on grass-clover mixtures.
Integrated the CNN algorithm with an all-terrain vehicle (ATV)-mounted image acquisition system for field deployment.

Main Results:

Achieved state-of-the-art results with a relative biomass clover content prediction accuracy of R² = 0.91.
Demonstrated the method's robustness across diverse experimental sites and growing conditions.
Successfully mapped species distribution across 225 hectares of mixed crops at a median rate of 17 hectares per hour.

Conclusions:

The proposed CNN-based method provides a highly accurate and feasible approach for biomass species composition analysis in mixed crops.
This technology enables efficient, large-scale species distribution mapping, paving the way for advanced targeted crop management.
The integration with an ATV system highlights the practical applicability for real-world agricultural monitoring.