Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping

Sadeghi‐Tehran, Pouria; Virlet, Nicolas; Sabermanesh, Kasra; Hawkesford, Malcolm J.

doi:10.1186/s13007-017-0253-8

Cited by 46 publications

(38 citation statements)

References 34 publications

(54 reference statements)

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“…In order to target the canopy objects, it is significant to separate the regions of vegetation from the background. In previous studies, colour features converted from the original R, G and B channels have been used to enhance the contrast between green canopy and the background [28, 29], and a more sophisticated method employing machine learning model could perform the segmentation with 21 colour features [30]. For UAV remote sensing, the original RGB image was normally transformed into a greyscale image of a Vegetation Index (VI) [31, 33].…”

Section: Methodsmentioning

confidence: 99%

The estimation of crop emergence in potatoes by UAV RGB imagery

Xu²,

Han

et al. 2019

Plant Methods

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BackgroundCrop emergence and canopy cover are important physiological traits for potato (Solanum tuberosum L.) cultivar evaluation and nutrients management. They play important roles in variety screening, field management and yield prediction. Traditional manual assessment of these traits is not only laborious but often subjective. ResultsIn this study, semi-automated image analysis software was developed to estimate crop emergence from high-resolution RGB ortho-images captured from an unmanned aerial vehicle (UAV). Potato plant objects were extracted from bare soil using Excess Green Index and Otsu thresholding methods. Six morphological features were calculated from the images to be variables of a Random Forest classifier for estimating the number of potato plants at emergence stage. The outputs were then used to estimate crop emergence in three field experiments that were designed to investigate the effects of cultivars, levels of potassium (K) fertiliser input, and new compound fertilisers on potato growth. The results indicated that RGB UAV image analysis can accurately estimate potato crop emergence rate in comparison to manual assessment, with correlation coefficient () of 0.96 and provide an efficient tool to evaluate emergence uniformity.ConclusionsThe proposed UAV image analysis method is a promising tool for use as a high throughput phenotyping method for assessing potato crop development at emergence stage. It can also facilitate future studies on optimizing fertiliser management and improving emergence consistency.

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Section: Methodsmentioning

confidence: 99%

The estimation of crop emergence in potatoes by UAV RGB imagery

Xu²,

Han

et al. 2019

Plant Methods

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“…To dissect green canopy from background variables, the Excess Green Red (ExGR) index was used (Equation (6)), with a threshold of > 0 to classify green vegetation [39,40]. Figure 6 shows an example of the produced mask, with reasonable agreement between visual green canopy and pixels classified as green by ExGR.…”

Section: Canopy Maskingmentioning

confidence: 99%

Radiometric Calibration of ‘Commercial off the Shelf’ Cameras for UAV-Based High-Resolution Temporal Crop Phenotyping of Reflectance and NDVI

et al. 2019

Self Cite

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Vegetation indices, such as the Normalised Difference Vegetation Index (NDVI), are common metrics used for measuring traits of interest in crop phenotyping. However, traditional measurements of these indices are often influenced by multiple confounding factors such as canopy cover and reflectance of underlying soil, visible in canopy gaps. Digital cameras mounted to Unmanned Aerial Vehicles offer the spatial resolution to investigate these confounding factors, however incomplete methods for radiometric calibration into reflectance units limits how the data can be applied to phenotyping. In this study, we assess the applicability of very high spatial resolution (1 cm) UAV-based imagery taken with commercial off the shelf (COTS) digital cameras for both deriving calibrated reflectance imagery, and isolating vegetation canopy reflectance from that of the underlying soil. We present new methods for successfully normalising COTS camera imagery for exposure and solar irradiance effects, generating multispectral (RGB-NIR) orthomosaics of our target field-based wheat crop trial. Validation against measurements from a ground spectrometer showed good results for reflectance (R2 ≥ 0.6) and NDVI (R2 ≥ 0.88). Application of imagery collected through the growing season and masked using the Excess Green Red index was used to assess the impact of canopy cover on NDVI measurements. Results showed the impact of canopy cover artificially reducing plot NDVI values in the early season, where canopy development is low.

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“…The method, based mainly on the color features of the images, achieved 90% accuracy with respect to the actual number of ears. Also for wheat, Sadeghi-Tehran et al [66] developed a deep-learning model for CC estimation using automatic segmentation of RGB images taken on wheat plots. The proposed method was more robust and accurate than other classical and machine-learning methodologies.…”

Section: Novelty Of the DL Model Against Current Approaches Used Formentioning

confidence: 99%

A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials

et al. 2020

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Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in the last few decades as the direct and indirect methods available are laborious and time-consuming. The recent emergence of high-throughput plant phenotyping platforms has increased the need to develop new phenotyping tools for better decision-making by breeders. In this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue (RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The model mixes numerical data collected in a wheat breeding field and visual features extracted from the images to make rapid and accurate LAI estimations. Model-based LAI estimations were validated against LAI measurements determined non-destructively using an allometric relationship obtained in this study. The model performance was also compared with LAI estimates obtained by other classical indirect methods based on bottom-up hemispherical images and gaps fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The model performance was slightly better than that of the hemispherical image-based method, which tended to underestimate LAI. These results show the great potential of the developed model for near real-time LAI estimation, which can be further improved in the future by increasing the dataset used to train the model.

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Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping

Cited by 46 publications

References 34 publications

The estimation of crop emergence in potatoes by UAV RGB imagery

The estimation of crop emergence in potatoes by UAV RGB imagery

Radiometric Calibration of ‘Commercial off the Shelf’ Cameras for UAV-Based High-Resolution Temporal Crop Phenotyping of Reflectance and NDVI

A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials

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