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J. Imaging 2017, 3(4), 59;

Preliminary Results of Clover and Grass Coverage and Total Dry Matter Estimation in Clover-Grass Crops Using Image Analysis

Department of Agroecology, Aarhus University, Forsøgsvej 1, Flakkebjerg, 4200 Slagese, Denmark
Department of Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark
Department of Agroecology, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
Author to whom correspondence should be addressed.
Received: 22 October 2017 / Revised: 21 November 2017 / Accepted: 30 November 2017 / Published: 6 December 2017
(This article belongs to the Special Issue Remote and Proximal Sensing Applications in Agriculture)
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The clover-grass ratio is an important factor in composing feed ratios for livestock. Cameras in the field allow the user to estimate the clover-grass ratio using image analysis; however, current methods assume the total dry matter is known. This paper presents the preliminary results of an image analysis method for non-destructively estimating the total dry matter of clover-grass. The presented method includes three steps: (1) classification of image illumination using a histogram of the difference in excess green and excess red; (2) segmentation of clover and grass using edge detection and morphology; and (3) estimation of total dry matter using grass coverage derived from the segmentation and climate parameters. The method was developed and evaluated on images captured in a clover-grass plot experiment during the spring growing season. The preliminary results are promising and show a high correlation between the image-based total dry matter estimate and the harvested dry matter ( R 2 = 0.93 ) with an RMSE of 210 kg ha 1 . View Full-Text
Keywords: image processing; biomass; yield; mixed-crops; non-destructive image processing; biomass; yield; mixed-crops; non-destructive

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Mortensen, A.K.; Karstoft, H.; Søegaard, K.; Gislum, R.; Jørgensen, R.N. Preliminary Results of Clover and Grass Coverage and Total Dry Matter Estimation in Clover-Grass Crops Using Image Analysis. J. Imaging 2017, 3, 59.

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