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

Skovsen, Søren Kelstrup; Laursen, Morten Stigaard; Kristensen, Rebekka Kjeldgaard; Rasmussen, Jim; Dyrmann, Mads; Eriksen, Jørgen; Gislum, René; Jørgensen, Rasmus Nyholm; Karstoft, Henrik

doi:10.3390/s21010175

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

Søren Kelstrup Skovsen

^1,*

Morten Stigaard Laursen

Rebekka Kjeldgaard Kristensen

²,

²,

Rasmus Nyholm Jørgensen

and

Henrik Karstoft

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

Department of Agroecology, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark

School of Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark

Author to whom correspondence should be addressed.

Sensors 2021, 21(1), 175; https://doi.org/10.3390/s21010175

Received: 31 October 2020 / Revised: 1 December 2020 / Accepted: 18 December 2020 / Published: 29 December 2020

(This article belongs to the Special Issue Sensing Technologies for Agricultural Automation and Robotics)

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Abstract

Crop mixtures are often beneficial in crop rotations to enhance resource utilization and yield stability. While targeted management, dependent on the local species composition, has the potential to increase the crop value, it comes at a higher expense in terms of field surveys. As fine-grained species distribution mapping of within-field variation is typically unfeasible, the potential of targeted management remains an open research area. In this work, we propose a new method for determining the biomass species composition from high resolution color images using a DeepLabv3+ based convolutional neural network. Data collection has been performed at four separate experimental plot trial sites over three growing seasons. The method is thoroughly evaluated by predicting the biomass composition of different grass clover mixtures using only an image of the canopy. With a relative biomass clover content prediction of R² = 0.91, we present new state-of-the-art results across the largely varying sites. Combining the algorithm with an all terrain vehicle (ATV)-mounted image acquisition system, we demonstrate a feasible method for robust coverage and species distribution mapping of 225 ha of mixed crops at a median capacity of 17 ha per hour at 173 images per hectare. View Full-Text

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

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Skovsen, S.K.; Laursen, M.S.; Kristensen, R.K.; Rasmussen, J.; Dyrmann, M.; Eriksen, J.; Gislum, R.; Jørgensen, R.N.; Karstoft, H. Robust Species Distribution Mapping of Crop Mixtures Using Color Images and Convolutional Neural Networks. Sensors 2021, 21, 175. https://doi.org/10.3390/s21010175

AMA Style

Skovsen SK, Laursen MS, Kristensen RK, Rasmussen J, Dyrmann M, Eriksen J, Gislum R, Jørgensen RN, Karstoft H. Robust Species Distribution Mapping of Crop Mixtures Using Color Images and Convolutional Neural Networks. Sensors. 2021; 21(1):175. https://doi.org/10.3390/s21010175

Chicago/Turabian Style

Skovsen, Søren K., Morten S. Laursen, Rebekka K. Kristensen, Jim Rasmussen, Mads Dyrmann, Jørgen Eriksen, René Gislum, Rasmus N. Jørgensen, and Henrik Karstoft. 2021. "Robust Species Distribution Mapping of Crop Mixtures Using Color Images and Convolutional Neural Networks" Sensors 21, no. 1: 175. https://doi.org/10.3390/s21010175

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