Visual Reference Guide for Estimating Legume Content in Pastures

Rayburn, Edward B.; Green, James T.

doi:10.2134/fg-2011-0176-dg

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…Visual estimation methods have been used historically as the principal method. Rayburn and Green (2014)developed a visual reference guide for mixed stands of clover 93 and grass to help calibrate the eye for human field estimation. Rayburn (2014) tested a manual 94 point count method by iteratively superimposing a randomly placed virtual point count grid on 95 mixed stand images, counting the number of points touching grass, legumes, forbs, bare ground, 96 and dark shadows, and quantifying the points.…”

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confidence: 99%

Application of local binary patterns in digital images to estimate botanical composition in mixed alfalfa–grass fields

McRoberts

Benson²,

Mudrak

et al. 2016

Computers and Electronics in Agriculture

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Botanical composition in mixed stands of alfalfa and grass is a critical parameter in equations estimating harvest fiber concentration for dairy rations. Composition is difficult to estimate by 26 visual observation. Digital image analysis in mixed stands could reduce botanical composition 27 uncertainty and improve spring harvest management decisions. Mixed stands were sampled 28 (n=168) in farmers' fields in Tompkins County, New York in May 2011. A digital image was 29 taken of standing samples at 5-Megapixels resolution using a Canon PowerShot A3100IS, and 30 alfalfa and grass height relationships were recorded. After clipping representative samples at 10-31 cm above ground level, samples were manually separated into alfalfa (Medicago sativa L.) and timothy grass (Phleum pratense L.), and dried to calculate fractions on a dry matter basis. Uniform rotation invariant local binary patterns (LBP) were extracted from whole images and 64 x 64 pixel tiles, and were used to develop regression equations estimating grass fraction. Tiles were manually classified as alfalfa (0), grass (1) or unclassifiable. An iterative process selected most accurate local binary pattern operator settings. Grass fraction was estimated in three regression model development approaches: (1) using average tile LBP histogram bins from whole images and botanical height relationships, (2) developing a binary tile classification model from tile LBP histogram bins, and using tile model-predicted grass probability averaged for tiles in whole images (grass coverage estimate) and botanical height relationships as inputs in whole image models, and (3) using LBP histogram bins extracted directly from whole images (1024 by 1024 pixel square) and height relationships. Predictive accuracy in whole image models using tile LBP histogram averages was highest for models generated from LBP tile histogram bin means (R 2 pred up to 0.847), followed closely by combined tile models and whole image models (R 2 pred up to 0.807), with pairwise correlations between tile model-generated grass coverage 46 3 estimates and sample grass fraction up to 0.895. Local binary patterns are effective in differentiating alfalfa and grass under field conditions, because the method is robust to changes in color and illumination. Furthermore, key LBP histogram bins (e.g., symmetric edges) strongly differentiate alfalfa and grass in tiles. The LBP method is promising based on this study, but further evaluation under diverse field conditions, including different cameras and grass species, is necessary to assess usefulness.

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mentioning

confidence: 99%

Application of local binary patterns in digital images to estimate botanical composition in mixed alfalfa–grass fields

McRoberts

Benson²,

Mudrak

et al. 2016

Computers and Electronics in Agriculture

View full text Add to dashboard Cite

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Predicting Forage Quality

Cherney

Parsons

2020

Forages

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

Skovsen

Kristensen

Rasmussen

et al. 2020

Sensors

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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.

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Visual Reference Guide for Estimating Legume Content in Pastures

Cited by 3 publications

References 3 publications

Application of local binary patterns in digital images to estimate botanical composition in mixed alfalfa–grass fields

Application of local binary patterns in digital images to estimate botanical composition in mixed alfalfa–grass fields

Predicting Forage Quality

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

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