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: Digitalisation has opened a wealth of new data opportunities by revolutionizing how images are captured. Although the cost of data generation is no longer a major concern, the data management and processing have become a bottleneck. Any successful visual trait system requires automated data structuring and a data retrieval model to manage, search, and retrieve unstructured and complex image data. This paper investigates a highly scalable and computationally efﬁcient image retrieval system for real-time content-based searching through large-scale image repositories in the domain of remote sensing and plant biology. Images are processed independently without considering any relevant context between sub-sets of images. We utilize a deep Convolutional Neural Network (CNN) model as a feature extractor to derive deep feature representations from the imaging data. In addition, we propose an effective scheme to optimize data structure that can facilitate faster querying at search time based on the hierarchically nested structure and recursive similarity measurements. A thorough series of tests were carried out for plant identiﬁcation and high-resolution remote sensing data to evaluate the accuracy and the computational efﬁciency of the proposed approach against other content-based image retrieval (CBIR) techniques, such as the bag of visual words (BOVW) and multiple feature fusion techniques. The results demonstrate that the proposed scheme is effective and considerably faster than conventional indexing structures.


Background
An average 2.4% annual yield increase needs to be achieved in order to meet the required estimated doubling of crop production by the year 2050.However, the average rate of increase for the four global key crops ranges from 0.9 to 1.6% [28].To bridge this gap, the breeding process must be accelerated.High-throughput field phenotyping enables the capacity for rapid and large scale evaluation of crop performance in agriculturally relevant environments which will help to accelerate the breeding process and ultimately, the rate of genetic improvement [1].
Digital RGB cameras are the most common tool used for field phenotyping due to their high-resolution, low cost and portable size.RGB cameras constitute a simple tool that provides a non-destructive, non-invasive and generally a high-throughput approach to collect information about canopy development and health status.RGB images have been used to provide plant morphological information using stereoscopic approaches [17], and to estimate net primary production using intensity of the reflectance of each of the red, green and blue channel [11,16].In recent years, RGB images have been extensively used to estimate fractional vegetation cover to study plant responses to water stress [7,35], nitrogen nutrition [19,20,32] or disease [38], as well as for the detection of weeds [24], for plant biomass estimation [6,21] and for yield [9].
Fraction vegetation cover (FVC) is derived from images collected from the nadir position [8,10].FVC is assessed as the ratio of green vegetation pixel to the total number of pixels for a given area.Effective segmentation algorithms to extract green vegetation pixels have been implemented in numerous studies over the last decades, from using simple colour indices to machine learning approaches [2,12,13,24].
Colour is the most popular feature for visual-based plant segmentation due to low computational cost, particle occlusion, robustness, and resolution changes.Colour properties are easily extracted and relatively constant under viewpoint changes.However, colour-based techniques have a problem in maintaining colour constancy between and within images of the same object, simply due to changes in illumination conditions, inter-reflections with other objects, shadows, etc.These effects may be minimised by using colour space transformation, such as YCbCr, HSL, HSV, CIELab and CIELuv [6] or using red, green and blue band combinations to increase the contrast between background (soil) and foreground (vegetation).Indices such as excess green (ExG) or excess green minus excess red (ExGR) were used in conjunction with automatic and positive thresholds, respectively, for an automatic background/foreground segmentation [24].Alternatively, other colour indices have been developed to improve the quality of the segmentation and to handle ambient illumination [13].
As classical methods with fixed thresholds (determined manually for each image by an operator) have difficulties in segmenting vegetation from background efficiently, automated thresholding methods have been developed based on bimodal distribution of green pixel intensity (or the corresponding channel when a colour space is used).The bimodal distribution corresponds to the background and foreground pixels.A Gaussian mixture model (GMM) is used to separate the pixel distribution of the foreground pixels from the background and automatically define a threshold [8,22,29].However, when vegetation is sparse or the canopy is nearly fully closed, the bimodal distribution is not apparent [8].Thus, GMM is likely to fail to discriminate vegetation from background, especially when weeds or algae are present in the captured image.
Several machine learning methods have been proposed to address such limitations.For example, [37] used meanshift clustering to segment the green vegetation of a crop canopy.However, each image sample was manually presegmented into separate regions; thus, it is not a practical solution for automated high-throughput phenotyping applications where hundreds of images are captured each day with high temporal resolution.[25] used ExR and ExG as inputs for fuzzy clustering to classify plants, soil and crop residue regions.These approaches achieved only 69% accuracy to classify plants in bare soil and failed for plants in corn and wheat residues.[30] proposed an adaptive segmentation algorithm for outdoor image segmentation.Although their method showed some level of adaptiveness with illumination changes, it generated noise in direct sunlight and failed to segment the majority of vegetation.[12] utilised a decision tree classification method to address the specular reflection on plant leaves.Their method segmented a high level of vegetation from their digital image of a single plant growing outdoor when compared to classical methods (such as ExG or ExGR), as well as in various ambient illumination conditions.However, in order to be suitable for phenotyping field-grown crops, any developed method for image segmentation must be extensively tested at canopy-scale throughout the entire crop life cycle.[3] developed a supervised approach with morphological modelling.The method is developed further in [2] by adding clustering approach to the process.Although the results outperformed colour index-based techniques such as ExG and ExGR and learning models such as GMM, the proposed method requires too many steps to execute and is prone to error and not suitable for daily operation for highthroughput phenotyping.

Challenges
To reliably classify vegetation from background (e.g.soil, rock) within an image, several challenges must be overcome when implementing a modelling algorithm.A key challenge when acquiring a time series of digital images in the field is the wide range of colour temperatures of ambient light.Changes in colour temperature depend on the sun position during the day and amount of cloud cover.[33] showed that colour temperature changes from 3400 (sunset) to 9500 K (north sky light) in daylight.Furthermore, the colour temperature at midday can fluctuate between 6000 and 9300 K as result of sunny or overcast skies.Such variation can result in poor performance of colour threshold-based and colour index-based approaches in segmenting vegetation from images captured in the field.The other issues are shadows and/or distribution of illumination variations.Illumination levels may vary between 102,000 lux (maximum sunlight) to 10,000 lux (shadows in sunny day) [33].In general, modern cameras with automatic exposure times and ISO values can handle the illumination distribution in an image to some extent; however, too much variation in the camera parameters (e.g.exposure) leads to false colour identification of objects.Apart from extremely dark or light conditions, humans are able to recognise non-uniformly illuminated scenes, due to their capability of thresholding each individual part locally.However, most of the aforementioned image processing methods failed to cope with this variation.
Colour transformation provides an ideal solution to minimise the shadow effect.Colours in an image are split into brightness and chromaticity.Then, an assumption is made that the chromaticity remains almost constant when the brightness changes if a pixel is part of a shadow.Moreover, in some scenarios, for example, after a rainy day, the sun on the wet surface (e.g.leaf ) may contribute to increase reflection and cause difficulty in segmenting plants precisely.The main reason is that reflections retain colour, texture, and edge information that are missing in shadows.Thus, most algorithms that rely on colour or texture will most likely fail to distinguish the plant's reflected surface from the background.
Similarly, detection of specular reflective regions is hardly achievable using classical segmentation based on colour properties, as those regions display a saturated signal (white spot) in all the RGB channels.Ideally, data must be captured in optimal light conditions.However, as illumination conditions under temperate climates may be uncertain over a day and will inevitably change through the season, segmentation algorithms must be robust enough to cope with dynamic illuminations.In recent years, intense efforts have been driven by the scientific community of crop and computer scientists to develop new techniques to process field phenotyping data.Machine learning is a promising multidisciplinary approach to data processing as it combines statistics, optimisation and modelling techniques.
This paper describes a machine learning technique to analyse field data (digital images) for the new generation of phenotyping platforms.The described supervised method is capable of learning from environmental conditions with a high level of adaptiveness and is suitable for high-throughput analysis in terms of processing time.

Methods
The entire process of the proposed method includes the following steps (Fig. 1 The digital camera (colour 12 bit Prosilica GT3300) on the Field Scanalyzer phenotyping platform (Lem-naTec GmbH; Virlet et al. [31]) was used to acquire all digital images (Fig. 2).The camera was perpendicular to the ground and positioned to maintain a 2.5 m distance On the 21st June 2016 (245 DAS), leaf area index was measured with a ceptometer, (LAI-2200C Plant Canopy Analyser Licor ® ) on each of the 54 plots of the experi- ment.Three measurements were taken at each plot with a 90 • view-restricting cap, one above the vegetation and two below.The above vegetation reading was performed to correct the below vegetation reading for light intensity.The correction is automatically performed by the ceptometer and the Leaf Area Index (LAI) was extracted using FV2200 software (Licor Bioscience, v2.1.1).For each plot, two LAI values were averaged before comparing with canopy closure data obtained from RGB images collected the same day.

Multi-feature supervised machine learning approach
Low-level cognitive functions are important in computational intelligence, which also involves discovery of structures in data analysis, object recognition and segmentation.The techniques introduced to address the problem can be divided into two main groups of supervised and unsupervised learning.They include Bayesian networks, statistical and kernel methods as well as evolutionary, fuzzy and neural approaches.In supervised techniques, the information is supplied by pre-defined class labels and pre-trained samples.Conversely, the unsupervised pattern representations do not require any pre-trained samples.A supervised multi-feature model is developed which is capable of training a model in different field conditions and labelling each image pixel as background or vegetation regardless of environmental conditions in the field.

Feature extraction
Visual features are fundamental in processing digital images to represent an image content.A set of good features should contain sufficient discrimination power to discriminate image contents.In this paper, colour properties are used as the main features to segment plants and monitor canopy coverage.Colour properties are extracted directly from pixel densities over the whole image and carry enough information about an image to discriminate plants from the background.In addition, colour features are sufficiently robust to handle background complications and invariants to the size, orientation and partial occlusion of the canopy image.In any colour-based method, having a colour consistency is an important factor.To maintain this consistency in a colour space, illumination conditions should not be changed; however, this is not achievable in the field with illumination changes, shading and a cluttered background (e.g.soil).In order to achieve an efficient system, a multidimensional feature is used to describe different properties of an image which also gives the ability to resist noise induced variations.Six colour spaces known as L*u*v*, L*a*b*, HSV, HSI, YCbCr, and YUV are used to extract the colour properties of each pixel; thus, the final size of the feature vector is equal to 21 elements: (1)

Noise reduction
A median filter is applied to minimise the noise and remove the result of misclassification over the binary image [15].In this work, a window size of seven pixels slides over the entire image, pixel by pixel.Then, the pixel values from the window are sorted numerically and replaced with a median value of neighbouring pixels.

Experimental results
The performance of the developed machine learning method was evaluated through several experiments under changing light conditions in a day, by comparing to manual measurement as well as throughout the canopy lifecycle.The algorithms were developed in MAT-LAB (Mathworks Inc.) as well as python using OpenCV [5] and scikit-learn [27]  The introduced techniques were also compared with the three well-known colour-index methods, ExG, ExGR [24], and CIVE [18], as well as two unsupervised learning methods known as ACE [8] and K-means clustering techniques.ExG (Excess Green Index) was originally proposed by [34] to provide a clear contrast between plant and soil: ExG = 2 × G − R − B. [24] used an automatic thresholding method known as the Otsu method [26], which enabled background and foreground segmentation based on the bimodal distribution of the pixel.ExGR combines ExG and ExR (Excess Red Index) to improve performance of ExG: [23,24].The authors added a positive threshold to the index to remove residual background pixels and achieved a higher performance compared to the ExG method with an Otsu threshold.Colour Index of Vegetation Extraction (CIVE) was proposed by [18] to evaluate crop growing status by providing a greater emphasis on the green area: CIVE = 0.441R − 0.811G + 0.385B + 18.78745.Similar to ExG, the Otsu method is used for automatic segmentation of vegetation from a soil background.When ExG, CIVE and ExGR are subsequently mentioned in this paper, they will be referred to their respective thresholds, Otsu and positive threshold respectively.In addition to the three colour index-based approaches mentioned earlier, the performance of two learning based models are compared with the proposed approach.Automated canopy estimator (ACE) is used an unsupervised segmentation process to produce accurate estimate of fractional vegetation cover using GMM [8].It should be noted that the ACE results presented in this paper are based on the free software provided by the authors at http://173.230.158.211.Another unsupervised learning model evaluated in this paper is K-means clustering developed to group pixels in digital images under a transformed L*u*v* colour space.A canopy is partitioned into k clusters (k = 20).The − u* and + v* axis indicate where green colour falls; thus, green pixels distributed close to the negative value of u* and positive value of v*, contain vegetation.Therefore, the pixels which do not satisfy the following condition (Eq.2) are considered as background.
where p i is an image pixel of i th cluster; c i is a cluster centre

Evaluating the segmentation accuracy
The accuracy of all methods were evaluated with the reference images in which the vegetation was manually segmented using Photoshop (Adobe Systems Incorporated, San Jose, CA, USA).Due to the complexity of the tested images, special care was put into segmenting vegetation appropriately as a fully manual manipulation.Figure 4 shows three examples of test images and manually segmented reference images for ground truthing.Three quality factors known as Q seg , S r and an error factor E s [24,36] are used to assess the segmentation accuracy using the following Eqs.3, 4, and 5.
where S is the segmented plant (p = 255) or background pixels (p = 0).R is the reference image manually seg- mented.Indices i, j are the pixels coordinate, and h, w are the height and width of the image, respectively.The accuracy is based on logical operations, logical and (∩), logical or (∪) and logical not (!), compared on a pixel-by-pixel basis of the reference image R and segmented image S.
Q seg is based on both plants and background regions within the range of values 0 and 1.It illustrates the consistency between the segmented image S and the reference image R on pixel-by-pixel basis where value 1 represents a perfect outcome.Similarly, S r measures the consistency within the image region of plant pixels and E s (2) represents the portion of misclassified plant pixels relative to true total plant pixels.

Single colour space versus multiple colour spaces as input(s) for the learning model
In this section, the performance of the learning model using single colour space with three features is compared to multiple colour spaces with 21 features as described in the feature extraction section.Table 1 illustrates the performance comparison between the two methods.It should be noted that L*a*b* colour space is used as a single input to meet the requirement of uniformity of distribution of colour [4].It is device-independent and proved to perform well in segmenting vegetation under uncontrolled outdoor illumination conditions [3,8].
As shown in Table 1, using multiple colour spaces is more robust to background noise and outdoor illumination changes.While both approaches displayed similar S r values (1.029 +/− 0.047 and 1.025 +/− 0.042 in multi and single colour space, respectively), the multicolour spaces had higher mean quality factor of 0.922 with lower standard deviation 0.019 as opposed to single colour space (0.885 +/− 0.087).In addition, multi-colour spaces had the lower rate of misclassified plant pixels (0.088 vs. 0.143) with lower standard deviation (0.022 vs.   0.131).As a result, multi-feature colour spaces (MFL) was selected as the optimum technique and compared with the state-of-the-art vegetation segmentation described in this work (Fig. 5).

Comparison of image segmentation techniques with the ground truth segmentation
Comparison of the accuracy rate of segmentation (Q seg , S r , E r ) of the proposed method with five other methods is presented in Fig. 6.Ten images were randomly selected under varying conditions of spectral reflections and background noise on different days (Figs. 7, 8).As shown in Fig. 6a, ExG, CIVE and ACE had the lowest Q seg val- ues (0.617, 0.65 and 0.645, respectively) with the highest standard deviation (0.299, 0.314 and 0.327, respectively).ExGR and K-means came second and third with average values of 0.776 and 0.77 and standard deviation 0.169 and 0.19, respectively.Nevertheless, MFL had the highest Q seg mean value of 0.898 and the lowest standard deviation, 0.07.MFL also had the highest S r mean value and low- est standard deviation along with ExGR with 1.014 and 0.965, respectively (Fig. 6b).ExG and CIVE had the highest misclassified pixels (E s ) while K-means and ExGR came second and third with 0.245 and 0.353, respectively.MFL performed the best with the lowest misclassified segmented pixels values of 0.123 (Fig. 6c).

Vegetation segmentation over illumination changes during a day
The performance of all methods in various ambient illuminations was assessed (Table 2).A screenshot of a Soissons plot was captured at different times of the day 165 DAS (April 2016).CIVE, ExG and ACE showed the highest coefficient of variations (CVs) with 58, 56 and 18%, respectively, while K-means, MFL and ExGR had the lowest coefficient of variation, below 5% over the day (5, 4 and 2% respectively).

Automatic vegetation segmentation versus hand held measurements of leaf area index
FVC was computed from all methods and compared to LAI of 54 plots at 245 DAS.As shown in Fig. 9, ExG and CIVE had the lowest coefficient of determination with R 2 = 0.02 as on Fig. 9 and 0.02 as in Fig. 9. ACE, K-means, ExGR and MFL showed a linear increase with LAI and had the highest coefficient of determination, 0.82, 0.88, 0.91 and 0.92, respectively.

Vegetation segmentation of tested methods over a full growing season
Figure 10 presents the fractional vegetation cover of all methods over 33 time points throughout the life-cycle of Crusoe, Gatsby and Widgeon cultivars.ExG and CIVE behaved similarly throughout the season in all three tested varieties with substantial day to day variation.Both tended to underestimate the FVC over the rapid canopy expansion period (197-240 DAS) and over-estimate canopy cover during the senescence period (245-284 DAS).
Moreover, examples taken from different timestamps in Fig. 11b, c, clearly illustrated that both methods failed to segment vegetation properly .
ACE showed daily inconsistency over the plant life cycle (Fig. 10).As illustrated in Fig. 11  (Fig. 11a) or failed to segment vegetation adequately, specially when part of the image was in shade (Fig. 11c).
In comparison, ExGR, K-means and MFL presented a similar pattern throughout the time series (Fig. 10).From emergence to 185 DAS, ExGR and K-means showed more day to day variation compared to MFL for all three tested varieties.From 185 to 284 DAS, ExGR and MFL had similar FVC values in the Gatsby and Maris Widgeon varieties, while K-means showed more fluctuation in the same period (Fig. 10b, c).Although the three methods in Fig. 10a, gave similar patterns from 185 to 284 DAS, ExGR and K-means had higher values of FVC than MFL.The example taken at 213 DAS (Fig. 11a) showed ExGR and K-means segmented background noise as vegetation, which may justify the higher value of FVC.

Discussion
Analysis of images acquired outdoors is a challenging task, as ambient illumination changes throughout a growing season.Unlike single plants grown in pots within greenhouse facilities, segmenting the vegetation from a field-grown plot is complex due to overlapping leaves, as well as portions of the canopy that are shadowed or displaying high specular reflectance; each of which contribute to an underestimation of vegetation pixels within an image.To be relevant for high-throughput phenotyping in field conditions, vegetation segmentation algorithms must be robust enough to handle dynamic illumination conditions and complex canopy architecture throughout the entire crop life cycle.

Colour index-based techniques (ExG, ExGR, CIVE)
It has been shown that single colour with automatic thresholding cannot adequately segment vegetation from a complex canopy in dynamic outdoor environments.As presented in all four experiments, although ExG and CIVE are easy to implement and require low computational complexity, they performed poorly, particularly at canopy expansion stage, 197 DAS (Fig. 10).When the contrast between foreground/vegetation and background is high, ExG and CIVE performed similarly to the other methods during the winter period until 185 DAS.As shown in Fig. 10, during canopy expansion when background soil is hardly visible, bimodal pixel distribution  However, ExGR demonstrated a high level of performance unlike results presented elsewhere [3,12].It showed a high correlation with LAI and performed consistently over illumination changes during a day (Table 2), as well as in time series (Fig. 10b, c).However, ExGR performed poorly with noisy backgrounds, which led to a high rate of vegetation misclassification (Figs.6c, 11a) and in high lightness intensity/spectral reflectance.

Unsupervised learning-based techniques (K-means, ACE)
In addition to colour index-based methods, two unsupervised machine learning techniques known as ACE [8] and K-means clustering were tested and compared with the proposed model.Although ACE showed a high correlation with LAI, it performed poorly in dynamic outdoor environments in general.In the experiments reported here, ACE computed a low value of Q seg and a high rate of misclassification of vegetation (Fig. 6).ACE also, performed inconsistently in terms of extracting FVC throughout the plant life cycle.
K-means achieved good segmentation performance across all conditions with a high rate of Q seg and a low level of misclassification error (Fig. 6).It also performed well in the time series experiment; however, it appeared to over-estimate FVC in certain conditions as illustrated in Crusoe from 197 to 240 DAS due to residual noise (Figs.10a, 11a).It also showed higher fluctuation in FVC compared to ExGR and MFL during the same periods.Nevertheless, the main drawback of using K-means is the iterative process which is computationally expensive.Moreover, the performance of K-means clustering depends on the selected number of clusters.The poor choice of cluster numbers may affect the performance of segmenting vegetation considerably.Throughout the conditions tested within this study, the MFL method achieved the best performance.It demonstrated the highest quality of segmentation indices with the lowest variation compared to the five other methods and had the lowest misclassification rate.It performed consistently throughout the growth cycle (Fig. 10) under different natural light conditions (Fig. 7) as well as with various backgrounds without pre-defining parameters.
Conversely, the proposed machine learning approach holds the advantage of versatility and could be applied to extract more than just green vegetation, such as yellow/brown organs appearing during senescence, or even for the detection of disease and/or pest symptoms with an adequate training dataset.As already mentioned, the performance of any supervised learning model strongly depends on training datasets.Therefore, in order to have a good model, a substantial set of training data plays an important role.Acquiring a training data is time-consuming and can be subjective.An aim is to expand this study by integrating a semiadaptive approach to generate bigger and more reliable training datasets semi-automatically; in addition, testing the model on more varieties and different crops is required.

Conclusion
This study shows that the proposed machine learning approach can be an essential tool for the development of data analysis pipelines in high-throughput field phenotyping.The learning model has shown a great capability to segment vegetation in various environments with various illumination conditions from "simple" to "complex" images.This study also highlighted that the classical colour index-based methods, ExG, CIVE with a single colour thresholding or unsupervised learning models like ACE may not be relevant, when it comes to dynamic illumination conditions.
For the first time, the robustness of vegetation segmentation algorithms (classical and machine learning) were tested along the whole crop life cycle, with increasing canopy complexity within images, as well as under dynamic illumination conditions experienced over multiple seasons.This study highlights that the proposed MFL approach is a relevant tool for time series analysis of field grown crops.The proposed method has a clear advantage over other colour index-based and unsupervised learning approaches, as it can be applied to other types of applications and is not limited to segmenting green vegetation only.

•
): Acquisition of digital images in time series • Extraction of multi-feature colour transformation • A supervised classification model to label pixels as foreground or background • Noise reduction using median filtering Field experiment and image acquisition Six wheat cultivars (Triticum aestivum L. cv.Avalon, Cadenza, Crusoe, Gatsby, Soissons and Maris Widgeon) were grown in the field at Rothamsted Research, Harpenden, UK.All cultivars were sown at a planting density of 350 seeds/m 2 on 20 October 2015 (Autumn) and harvested on 27 August 2016 (Summer).Nitrogen (N) treatments were applied as ammonium nitrate in the spring, at rates of 0 kg ha −1 (residual soil N; N1) 100 kg ha −1 (N2) and 200 kg ha −1 (N3).

Fig. 1
Fig. 1 Schematic representation of the method

Fig. 2 Fig. 3
Fig. 2 Field Scanalyzer.(Left) The Field Scanalyzer phenotyping platform at Rothamsted Research showing (Right) the cameras within the camera bay directed down, perpendicular to the ground

Fig. 4
Fig. 4 Examples of test images and their corresponding ground truths.The test images randomly selected from the image dataset in different illumination conditions.a original image, b reference image segmented manually, c binary image of the reference image

Fig. 5
Fig. 5 Multi-feature versus single feature.Comparison of segmented images of supervised learning model with single colour space versus multiple colour spaces

Fig. 8
Fig. 8 Digital images of a single section of a wheat plot (Triticum aesvtivum L. cv.Soissons) and the vegetation extracted using various image segmentation methods.Images were captured 165 DAS at a 10:16 AM, b 12:44 PM, c 3:36 PM, d 5:03 PM

Fig. 9
Fig. 9 Comparison of manual canopy cover estimates of 54 wheat plots determined using leaf area index (LAI) with the automatic methods, ExG, ExGR, CIVE, ACE, K-means, and MFL

Fig. 11
Fig. 11 Segmentation results of six methods.The columns from the first to sixth demonstrate the segmentation results by ExG, ExGR, CIVE, K-means, ACE, and the proposed MFL method respectively.a Crusoe 213 DAS, b Gatsby 262 DAS, c Widgeon 230 DAS python packages.The process of segmenting an image with an original resolution of 3298 × 2474 only takes 3.4 s on a Windows 10 PC with 6-core Intel Xeon processor (3.60 GHz) with 32 GB RAM.Although the outcomes from MATLAB and python are almost identical, all the results and comparisons presented in this paper are under python development.

Table 1 Comparison of the mean accuracy rate (Q seg , S r , E s ) between multi-colour spaces and single colour space Multi colour spaces Single colour space
, ACE either generated noise which led to over-segmenting vegetation Comparison of the mean accuracy rate (Q seg , S r , and E s ).Comparison of different approaches by segmentation quality for ExG, ExGR, CIVE, ACE, K-means, and the proposed method, MFL.The bar indicates the standard deviations