Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review

3.2.1 Studied Plant Organs

Identifying species requires recognizing one or more characteristics of a plant and linking them with a name, either a common or so-called scientific name. Humans typically use one or more of the following characteristics: the plant as a whole (size, shape, etc.), its flowers (color, size, growing position, inflorescence, etc.), its stem (shape, node, outer character, bark pattern, etc.), its fruits (size, color, quality, etc.), and its leaves (shape, margin, pattern, texture, vein etc.) [114].

A majority of primary studies utilizes leaves for discrimination (106 studies). In botany, a leaf is defined as a usually green, flattened, lateral structure attached to a stem and functioning as a principal organ of photosynthesis and transpiration in most plants. It is one of the parts of a plant which collectively constitutes its foliage [44, 123]. Figure 4 shows the main characteristics of leaves with their corresponding botanical terms. Typically, a leaf consists of a blade (i.e., the flat part of a leaf) supported upon a petiole (i.e., the small stalk situated at the lower part of the leaf that joins the blade to the stem), which, continued through the blade as the midrib, gives off woody ribs and veins supporting the cellular texture. A leaf is termed “simple” if its blade is undivided, otherwise it is termed “compound” (i.e., divided into two or more leaflets). Leaflets may be arranged on either side of the rachis in pinnately compound leaves and centered around the base point (the point that joins the blade to the petiole) in palmately compound leaves [44]. Most studies use simple leaves for identification, while 29 studies considered compound leaves in their experiments. The internal shape of the blade is characterized by the presence of vascular tissue called veins, while the global shape can be divided into three main parts: (1) the leaf base, usually the lower 25% of the blade; the insertion point or base point, which is the point that joins the blade to the petiole, situated at its center. (2) The leaf tip, usually the upper 25% of the blade and centered by a sharp point called the apex. (3) The margin, which is the edge of the blade [44]. These local leaf characteristics are often used by botanists in the manual identification task and could also be utilized for an automated classification. However, the majority of existing leaf classification approaches rely on global leaf characteristics, thus ignoring these local information of leaf characteristics. Only eight primary studies consider local characteristics of leaves like the petiole, blade, base, and apex for their research [19, 85, 96, 97, 99, 119, 120, 158]. The characteristics of the leave margin is studied by six primary studies [18, 21, 31, 66, 85, 93].

In contrast to studies on leaves or plant foliage, a smaller number of 13 primary studies identify species solely based on flowers [3, 29, 30, 57, 60, 64, 104, 105, 112, 117, 128, 129, 149]. Some studies did not only focus on the flower region as a whole but also on parts of the flower. Hsu et al. [60] analyzed the color and shape not only of the whole flower region but also of the pistil area. Tan et al. [128] studied the shape of blooming flowers’ petals and [3] proposed analyzing the lip (labellum) region of orchid species. Nilsback and Zisserman [104, 105] propose features, which capture color, texture, and shape of petals as well as their arrangement.

Only one study proposes a multi-organ classification approach [68]. Contrary to other approaches that analyze a single organ captured in one image, their approach analyzes up to five different plant views capturing one or more organs of a plant. These different views are: full plant, flower, leaf (and leaf scan), fruit, and bark. This approach is the only one in this review dealing with multiple images exposing different views of a plant.

Open image in new window

3.2.2 Images: Categories and Datasets

Forty-eight authors use their own, not publicly available, leaf datasets. For these leave images, typically fresh material was collected and photographed or scanned in the lab on plain background. Due to the great effort in collecting material, such datasets are limited both in the number of species and in the number of images per species. Two studies used a combination of self-collected leaf images and images from web resources [74, 138]. Most plant classification approaches only focus on intact plant organs and are not applicable to degraded organs (e.g., deformed, partial, or overlapped) largely existing in nature. Only 21 studies proposed identification approaches that can also handle damaged leaves [24, 38, 46, 48, 56, 58, 74, 93, 102, 132, 141, 143] and overlapped leaves [18, 19, 20, 38, 46, 48, 74, 85, 102, 122, 130, 137, 138, 148].

Most utilized flower images were taken by the authors themselves or acquired from web resources [3, 29, 60, 104, 105, 112]. Only one study solely used self-taken photos for flower analysis [57]. Two studies analyzed the Oxford 17 and the Oxford 102 datasets (Table 4).

A majority of primary studies only evaluated their approach on datasets containing less than a hundred species (see Fig. 5) and at most a few thousand leaf images (see Fig. 6). Only two studies used a large dataset with more than 2000 species. Joly et al. [68] used a dataset with 2258 species and 44,810 images. In 2014 this was the plant identification study considering the largest number of species so far. In 2015 [143] published a study with 23,025 species represented by 1,000,000 images in total.

Open image in new window

Open image in new window

3.3.1 Shape

3.3.2 Simple and Morphological Shape Descriptors

Across the studies we found six basic shape descriptors used for leaf analysis (see first six rows of Table 7). These refer to basic geometric properties of the leaf’s shape, i.e., diameter, major axis length, minor axis length, area, perimeter, centroid (see, e.g., [144]). On top of that, studies compute and utilize morphological descriptors based on these basic descriptors, e.g., aspect ratio, rectangularity measures, circularity measures, and the perimeter to area ratio (see Table 6). Table 6 shows that studies often employ ratios as shape descriptors. Ratios are simple to compute and naturally invariant to translation, rotation, and scaling; making them robust against different representations of the same object (aka leaf). In addition, several studies proposed more leaf-specific descriptors. For example, [58] introduce a leaf width factor (LWF), which is extracted from leaves by slicing across the major axis and parallel to the minor axis. Then, the LWF per strip is calculated as the ratio of the width of the strip to the length of the entire leaf (major axis length). Yanikoglu et al. [148] propose an area width factor (AWF) constituting a slight variation of the LWF. For AWF, the area of each strip normalized by the global area is computed. As another example, [116] used a porosity feature to explain cracks in the leaf image (Table 7).

Table 7

Simple and morphological shape descriptors (SMSD)

Descriptor	Explanation	Pictogram	Formula	Studies	\(\sum\)
Diameter D	Longest distance between any two points on the margin of the organ	Open image in new window		[5, 15, 16, 89, 110, 144]	6
Major axis length L	Line segment connecting the base and the tip of the leaf	Open image in new window		[5, 16, 24, 58, 89, 144]	6
Minor axis length W	Maximum width that is perpendicular to the major axis	Open image in new window		[5, 16, 24, 58, 89, 144]	6
Area A	Number of pixels in the region of the organ	Open image in new window		[5, 15, 16, 24, 58, 89, 129, 144]	8
Perimeter P	Summation of the distances between each adjoining pair of pixels around the border of the organ	Open image in new window		[5, 16, 24, 48, 58, 89, 129, 144]	8
Centroid	Represents the coordinates of the organ’s geometric center	Open image in new window		[82, 128]	2
Aspect ratio AR (aka slimness)	Ratio of major axis length to minor axis length—explains narrow or wide leaf or flower characteristics	Open image in new window	\(AR=\frac{L}{W}\)	[1, 3, 5, 15, 16, 24, 40, 43, 48, 72, 82, 89, 110, 116, 129, 137, 141, 144, 154]	19
Roundness R (aka form factor, circularity, isoperimetric factor)	Illustrates the difference between a organ and a circle	Open image in new window	\(R = \frac{4 \pi A}{P^{2}}\)	[1, 3, 5, 16, 24, 40, 43, 48, 60, 72, 89, 110, 116, 137, 144, 146]	16
Compactness (aka perimeter ratio of area)	Ratio of the perimeter over the object’s area; provides information about the general complexity and the form factor, it is closely related to roundness	Open image in new window	\(C= \frac{P^2}{A} ; C= \frac{P}{\sqrt{A}}\)	[73, 82, 148]	3
Rectangularity N (aka extent)	Represents how rectangle a shape is, i.e., how much it fills its minimum bounding rectangle	Open image in new window	\(N= \frac{A}{LW}\)	[5, 16, 24, 40, 48, 58, 89, 144, 146]	10
Eccentricity E	Ratio of the distance between the foci of the ellipse (f) and its major axis length (a); computes to 0 for a round object and to 1 for a line		\(E = \frac{f}{a}\)	[1, 40, 43, 58, 110]	5
Narrow factor NF	Ratio of the diameter over the Major axis length	Open image in new window	\(NF= \frac{D}{L}\)	[5, 16, 89, 137, 144]	5
Perimeter ratio of diameter \(P_D\)	Ratio of the perimeter to the diameter	Open image in new window	\(P_D = \frac{P}{D}\)	[5, 89, 144]	3
Perimeter ratio of Major axis length \(P_L\)	Ratio of the perimeter to the major axis length	Open image in new window	\(P_L= \frac{P}{L}\)	[16, 24, 89]	3
Perimeter ratio of Major axis length and Minor axis length \(P_{LW}\)	Ratio of object perimeter over the sum of the major axis length and the minor axis length	Open image in new window	\(P_{LW}= \frac{P}{(L +W)}\)	[5, 16, 24, 89, 144]	5
Convex hull CH (aka Convex area)	The convex hull of a region is the smallest region that satisfies two conditions: (1) it is convex, and (2) it contains the organ’s region	Open image in new window		[1, 40, 58, 137]	4
Perimeter convexity \(P_C\)	Ratio of the convex perimeter \(P_{CH}\) to the perimeter P of the organ	Open image in new window	\(P_C = \frac{P _{CH}}{P}\)	[40, 73, 146, 148]	4
Area convexity \(A_{C1}\) (aka Entirety)	Normalized difference of the convex hull area and the organ’s area	Open image in new window	\(A_{C1} = \frac{(CH - A)}{ A}\)	[148]	1
Area ratio of convexity \(A_{C2}\) (aka Solidity)	Ratio between organ’s area and area of the organ’s convex hull	Open image in new window	\(A_{C2}= \frac{A}{CH}\)	[40, 43, 73, 110, 137, 146]	6
Sphericity S (aka Dispersion)	Ratio of the radius of the inside circle of the bounding box (\(r_i\)) and the radius of the outside circle of the bounding box (\(r_c\))	Open image in new window	\(S=\frac{r_i}{r_c}\)	[40, 72, 137, 146]	4
Equivalent diameter \(D_E\)	Diameter of a circle with the same area as the organ’s area	Open image in new window	\(D_E=\sqrt{ \frac{4*A}{\pi }}\)	[58]	1
Ellipse variance EA	Represents the mapping error of a shape to fit an ellipse with same covariance matrix as the shape	Open image in new window		[141, 146]	2
Smooth factor	Ratio between organ’s area smoothed by a 5x5 rectangular averaging filter and one smoothed by a 2x2 rectangular averaging filter	Open image in new window		[5, 144]	2
Leaf width factor LWF \(_c\)	The leaf is sliced, perpendicular to the major axis, into a number of vertical strips. Then for each strip (c), the ratio of width of each strip (\(W_c\)) and the length of the entire leaf (L) is calculated	Open image in new window	\(LWF_c=\frac{W_c}{ L}\)	[58]	1
Area width factor AWF \(_c\)	The leaf is sliced, perpendicular to the major axis, into a number of vertical strips. Then for each strip (c), the ratio of the area of each strip (\(A_c\)) and the area of the entire leaf (A) is calculated	Open image in new window	\(AWF_c=\frac{A_c}{ A}\)	[148]	1
Porosity poro	Portion of cracks in leaf image; \(A_d\) is the detected area counting the holes in the object		\(Poro={\frac{(A-A_d)}{ A}} *100\%\)	[116]	1

However, while there typically exists high morphological variation across different species’ leaves, there is also often considerable variance among leaves of the same species. Studies’ results show that SMSD are too much simplified to discriminate leaves beyond those with large differences sufficiently. Therefore, they are usually combined with other descriptors, e.g., more complex shape analysis [1, 15, 40, 72, 73, 106, 110, 137, 146], leaf texture analysis [154], vein analysis [5, 144], color analysis [16, 116], or all of them together [43, 48]. SMSD are usually employed for high-level discrimination reducing the search space to a smaller set of species without losing relevant information and allowing to perform computationally more expensive operations at a later stage on a smaller search space [15].

Similarly, SMSD play an important role for flower analysis. Tan et al. [129] propose four flower shape descriptors, namely, area, perimeter of the flower, roundness of the flower, and aspect ratio. A simple scaling and normalization procedure has been employed to make the descriptors invariant to varying capture situations. The roundness measure and aspect ratio in combination with more complex shape analysis descriptors are used by [3] for analyzing flower shape.

In conclusion, the risk of SMSD is that any attempt to describe the shape of a leaf using only 5–10 descriptors may oversimplify matters to the extent that meaningful analysis becomes impossible, even if they seem sufficient to classify a small set of test images. Furthermore, many single-value descriptors are highly correlated with each other, making the task of choosing sufficiently independent features to distinguish categories of interest especially difficult [33].

3.3.3 Region-Based Shape Descriptors

Region-based techniques take all the pixels within a shape region into account to obtain the shape representation, rather than only using boundary information as the contour-based methods do. In this section, we discuss the most popular region-based descriptors for plant species identification: image moments and local feature techniques.

Image moments. Image moments are a widely applied category of descriptors in object classification. Image moments are statistical descriptors of a shape that are invariant to translation, rotation, and scale. Hu [61] proposes seven image moments, typically called geometric moments or Hu moments that attracted wide attention in computer vision research. Geometric moments are computationally simple, but highly sensitive to noise. Among our primary studies, geometric moments have been used for leaf analysis [22, 23, 40, 65, 72, 73, 102, 110, 137, 138, 154] as well as for flower analysis [3, 29]. Geometric moments as a standalone feature are only studied by [102]. Most studies combine geometric moments with the previously discussed SMSD [3, 23, 40, 72, 73, 110, 137, 154]. Also the more evolved Zernike moment invariant (ZMI) and Legendre moment invariant (LMI), based on an orthogonal polynomial basis, have been studied for leaf analysis [72, 138, 159]. These moments are also invariant to arbitrary rotation of the object, but in contrast to geometric moments they are not sensitive to image noise. However, their computational complexity is very high. Kadir et al. [72] found ZMI not to yield better classification accuracy than geometric moments. Zulkifli et al. [159] compare three moment invariant techniques, ZMI, LMI, and moments of discrete orthogonal basis (aka Tchebichef moment invariant (TMI)) to determine the most effective technique in extracting features from leaf images. In result, the authors identified TMI as the most effective descriptor. Also [106] report that TMI achieved the best results compared with geometric moments and ZMI and were therefore used as supplementary features with lower weight in their classification approach.

Local feature techniques. In general, the concept of local features refers to the selection of scale-invariant keypoints (aka interest points) in an image and their extraction into local descriptors per keypoint. These keypoints can then be compared with those obtained from another image. A high degree of matching keypoints among two images indicates similarity among them. The seminal Scale-invariant feature transform (SIFT) approach has been proposed by [86]. SIFT combines a feature detector and an extractor. Features detected and extracted using the SIFT algorithm are invariant to image scale, rotation, and are partially robust to changing viewpoints and changes in illumination. The invariance and robustness of the features extracted using this algorithm makes it also suitable for object recognition rather than image comparison.

SIFT has been proposed and studied for leaf analysis by [26, 27, 59, 81]. A challenge that arises for object classification rather than image comparison is the creation of a codebook with trained generic keypoints. The classification framework by [26] combines SIFT with the Bag of Words (BoW) model. The BoW model is used to reduce the high dimensionality of the data space. Hsiao et al. [59] used SIFT in combination with sparse representation (aka sparse coding) and compared their results to the BoW approach. The authors argue that in contrast to the BoW approach, their sparse coding approach has a major advantage as no re-training of the classifiers for newly added leaf image classes is necessary. In [81], SIFT is used to detect corners for classification. Wang et al. [139] propose to improve leaf image classification by utilizing shape context (see below) and SIFT descriptors in combination so that both global and local properties of a shape can be taken into account. Similarly, [74] combines SIFT with global shape descriptors (high curvature points on the contour after chain coding). The author found the SIFT method by itself not successful at all and its accuracy significantly lower compared to the results obtained by combining it with global shape features. The original SIFT approach as well as all so far discussed SIFT approaches solely operate on grayscale images. A major challenge in leaf analysis using SIFT is often a lack of characteristic keypoints due to the leaves’ rather uniform texture. Using colored SIFT (CSIFT) can address this problem and will be discussed later in the section about color descriptors.

Another substantially studied local feature approach is the histogram of oriented gradients (HOG) descriptor [41, 111, 145, 155]. The HOG descriptor, introduced by [86] is similar to SIFT, except that it uses an overlapping local contrast normalization across neighboring cells grouped into a block. Since HOG computes histograms of all image cells and there are even overlap cells between neighbor blocks, it contains much redundant information making dimensionality reduction inevitably for further extraction of discriminant features. Therefore, the main focus of studies using HOG lies on dimensionality reduction methods. Pham et al. [111], Xiao et al. [145] study the maximum margin criterion (MMC), [41] studies principle component analysis (PCA) with linear discriminant analysis (LDA), and [155] introduce attribute-reduction based on neighborhood rough sets. Pham et al. [111] compared HOG features with Hu moments and the obtained results show that HOG is more robust than Hu moments for species classification. Xiao et al. [145] found that HOG-MMC achieves a better accuracy than the inner-distance shape context (IDSC) (will be introduced in the section about contour based shape descriptors), when leaf petiole were cut off before analysis. A disadvantage of the HOG descriptor is its sensitivity to the leaf petiole orientation while the petiole’s shape actually carrying species characteristics. To address this issue, a pre-processing step can normalize petiole orientation of all images in a dataset making them accessible to HOG [41, 155].

Nguyen et al. [103] studied speeded up robust features (SURF) for leaf classification, which was first introduced by [9]. The SURF algorithm follows the same principles and procedure as SIFT. However, details per step are different. The standard version of SURF is several times faster than SIFT and claimed by its authors to be more robust against image transformations than SIFT [9]. To reduce dimensionality of extracted features, [103] apply the previously mentioned BoW model and compared their results with those of [111]. SURF was found to provide better classification results than HOG [111].

Ren et al. [121] propose a method for building leaf image descriptors by using multi-scale local binary patterns (LBP). Initially, a multi-scale pyramid is employed to improve leaf data utilization and each training image is divided into several overlapping blocks to extract LBP histograms in each scale. Then, the dimension of LBP features is reduced by a PCA. The authors found that the extracted multi-scale overlapped block LBP descriptor can provide a compact and discriminative leaf representation.

Local features have also been studied for flower analysis. Nilsback and Zisserman [104], Zawbaa et al. [149] used SIFT on a regular grid to describe shapes of flowers. Nilsback and Zisserman [105] proposed to sample HOG and SIFT on both, the foreground and its boundary. The authors found SIFT descriptors extracted from the foreground to perform best, followed by HOG, and finally SIFT extracted from the boundary of a flower shape. Combining foreground SIFT with boundary SIFT descriptors further improved the classification results.

Qi et al. [117] studied dense SIFT (DSIFT) features to describe flower shape. DSIFT is another SIFT-like feature descriptor. It densely selects points evenly in the image, on each pixel or on each n-pixels, rather than performing salient point detection, which make it strong in capturing all features in an image. But DSIFT is not scale-invariant, to make it adaptable to changes in scale, local features are sampled by different scale patches within an image [84]. Unlike the work of [104, 105], [117] take the full image as input instead of a segmented image, which means that extended background greenery may affect their classification performance to some extent. However, the results of [117] are comparable to the results of [104, 105]. When considering segmentation and complexity of descriptor as factors, the authors even claim that their method facilitates more accurate classification and performs more efficiently than the previous approaches.

3.3.4 Contour-Based Shape Descriptors

Contour-based descriptors solely consider the boundary of a shape and neglect the information contained in the shape interior. A contour-based descriptor for a shape is a sequence of values calculated at points taken around an object’s outline, beginning at some starting point and tracing the outline in either a clockwise or an anti-clockwise direction. In this section, we discuss popular contour-based descriptors namely shape signatures, shape context approaches, scale space, the Fourier descriptor, and fractal dimensions.

Shape signatures. Shape signatures are frequently used contour-based shape descriptors, which represent a shape by an one dimensional function derived from shape contour points. There exists a variety of shape signatures. We found the centroid contour distance (CCD) to be the most studied shape signature for leaf analysis [10, 28, 46, 130] and flower analysis [3, 57]. The CCD descriptor consists of a sequence of distances between the center of the shape and points on the contour of a shape. Other descriptors consist of a sequence of angles to represent the shape, e.g., the centroid-angle (AC) [10, 46] and the tangential angle (AT) [6]. A comparison between CCD and AC sequences performed by [46] demonstrated that CCD sequences are more informative than AC sequences. This observation is intuitive since the CCD distance includes both global information related to the leaf area and shape as well as local information related to contour details. Therefore, when combining CCD and AC, which is expected to further improve classification performance, the CCD should be emphasized by giving it a higher classification weight [46].

Mouine et al. [92] investigate two multi-scale triangular approaches for leaf shape description: the well-known triangle area representation (TAR) and the triangle side length representation (TSL). The TAR descriptor is computed based on the area of triangles formed by points on the shape contour. TAR provides information about shape properties, such as the convexity or concavity at each contour point of the shape, and provides high discrimination capability. Although TAR is affine-invariant and robust to noise and deformation, it has a high computational cost since all the contour points are used. Moreover, TAR has two major limitations: (a) the area is not informative about the type of the considered triangle (isosceles, equilateral, etc.), which may be crucial for a local description of the contour. (b) The area is not accurate enough to represent the shape of a triangle [94]. The TSL descriptor is computed based on the side lengths rather than the area of a triangle. TSL is invariant under scale, translation, rotation, and reflection around contour points. Studies found TSL to provide yield higher classification accuracy than TAR [92, 94]. The authors argue that this result may be due to the fact that using side lengths to represent a triangle is more accurate than using its area. In addition to the two multi-scale triangular approaches, [94] also proposed two representations that they denote triangle oriented angles (TOA) and triangle side lengths and angle representation (TSLA). TOA solely uses angle values to represent a triangle. Angle orientation provides information about local concavities and convexities. In fact, an obtuse angle means convex, an acute angle means concave. TOA is not invariant under reflection around the contour point: only similar triangles having equal angles will have equal TOA values. TSLA is a multi-scale triangular contour descriptor that describes the triangles by their lengths and angle. Like TSL, the TSLA descriptor is invariant under scale and reflection around the contour points. The authors found that the angular information provides a more precise description when being jointly used with triangle side lengths (i.e., TSL) [94].

A disadvantage of shape signatures for leaf and flower analysis is the high matching cost, which is too high for online retrieval. Furthermore, shape signatures are sensitive to noise and changes in the contour. Therefore, it is undesirable to directly describe a shape using a shape signature. On the other hand, further processing can increase its robustness and reduce the matching load. For example, a shape signature can be simplified by quantizing a contour into a contour histogram, which is then rotationally invariant [151]. For example, an angle code histogram (ACH) has been used instead of AC by [148]. However, the authors did not compare AC against ACH.

Shape context approaches. Beyond the CCD and AC descriptors discussed before, there are other alternative methods that intensively elaborate a shape’s contour to extract useful information. Belongie et al. [12] proposed a shape descriptor, called shape context (SC), that represent log-polar histograms of contour distribution. A contour is resampled to a fixed number of points. In each of these points, a histogram is computed such that each bin counts the number of sampled contour points that fall into its space. In other words, each contour point is described by a histogram in the context of the entire shape. Descriptors computed in similar points on similar shapes will provide close histograms. However, articulation (e.g., relative pose of the petiole or the position of the blade) results in significant variation of the calculated SC. In order to obtain articulation invariance, [83] replaced the Euclidean distance and relative angles by inner-distances and inner-angles. The resulting 2D histogram, called inner-distance shape context (IDSC), was reported to perform better than many other descriptors in leaf analysis [11]. It is robust to the orientation of the footstalk, but at the cost of being a shape descriptor that is extensive in size and expensive in computational cost. For example, [139] does not employ IDSC due to its expensive computational cost.

Hu et al. [62] propose a contour-based shape descriptor named multi-scale distance matrix (MDM) to capture the geometric structure of a shape, while being invariant to translation, rotation, scaling, and bilateral symmetry. The approach can use Euclidean distances as well as inner distances. MDM is considered a most effective method since it avoids the use of dynamic programming for building the point-wise correspondence. Compared to other contour-based approaches, such as SC and IDSC, MDM can achieve comparable recognition performance while being more computationally efficient [62]. Although MDM effectively describes the broad shape of a leaf, it fails in capturing details, such as leaf margin. Therefore, [73] proposed a method that combines contour (MDM), margin (average margin distance (AMD), margin statistics (MS)), SMSD and Hu moments and demonstrated higher classification accuracy than reached by using MDM and SMSD with Hu moments alone.

Zhao et al. [158] made two observations concerning shape context approaches. First, IDSC cannot model local details of leaf shapes sufficiently, because it is calculated based on all contour points in a hybrid way so that global information dominates the calculation. As a result, two different leaves with similar global shape but different local details tend to be misclassified as the same species. Second, the point matching framework of generic shape classification methods does not work well for compound leaves since their local details are hard to be matched in pairs. To solve this problem, [158] proposed an independent-IDSC (I-IDSC) feature. Instead of calculating global and local information in a hybrid way, I-IDSC calculates them independently so that different aspects of a leaf shape can be examined individually. The authors argue that compared to IDSC [11, 83] and MDM [62], the advantage of I-IDSC is threefold: (1) it discriminates leaves with similar overall shape but different margins and vice versa; (2) it accurately classifies both simple and compound leaves; and (3) it only keeps the most discriminative information and can thus be more efficiently computed [158].

Wang et al. [134, 135] developed a multi scale-arch-height descriptor (MARCH), which is constructed based on the concave and convex measures of arches of various levels. This method extracts hierarchical arch height features at different chord spans from each contour point to provide a compact, multi-scale shape descriptor. The authors claim that MARCH has the following properties: invariant to image scale and rotation, compactness, low computational complexity, and coarse-to-fine representation structure. The performance of the proposed method has been evaluated and demonstrated to be superior to IDSC and TAR [134, 135].

Scale space analysis. A rich representation of a shape’s contour is the curvature-scale space (CSS). It piles up curvature measures at each point of the contour over successive smoothing scales, summing up the information into a map where concavities and convexities clearly appear, as well as the relative scale up to which they persist [151]. Florindo et al. [45] propose an approach to leaf shape identification based on curvature complexity analysis (fractal dimension based on curvature). By using CSS, a curve describing the complexity of the shape can be computed and theoretically be used as descriptor. Studies found the technique to be superior to traditional shape analysis methods like FD, Zernike moments, and multi-scale fractal dimension [45]. However, while CSS is a powerful description it is too informative to be used as a descriptor. The implementation and matching of CSS is very complex. Curvature has also been used to detect dominant points (points of interest or characteristic points) on the contour, and provides a compact description of a contour by its curvature optima. Studies select this characteristic or the most prominent points based on the graph of curvature values of the contour as descriptor [15, 18]. Lavania and Matey [81] use mean projection transform (MPT) to extract corner candidates by selecting only candidates that have high curvature (contour-based edge detection). Kebapci et al. [74] extract high curvature points on the contour by analyzing direction changes in the chain code. They represent the contour as a chain code, which is a series of enumerated direction codes. These points (aka codes) are labeled as convex or concave depending on their position and direction (or curvature of the contour). Kumar et al. [76] suggest a leaf classification method using so-called histograms of curvature over scale (HoCS). HoCS are built from CSS by creating histograms of curvature values over different scales. One limitation of the HoCS method is that it is not articulation-invariant, i.e., that a change caused by the articulation either between the blade and petiole of a simple leaf, or among the leaflets of a compound leaf can cause significant changes to the calculated HoCS feature. Therefore, it needs special treatment of leaf petioles and the authors suggest to detect and remove the petiole before classification. Chen et al. [28] used a simplified curvature of the leaf contour, called velocity. The results showed that the velocity algorithms were faster at finding contour shape characteristics and more reasonable in their characteristic matching than CSS. Laga et al. [77] study the performance of the squared root velocity function (SRVF) representation of closed planar curves for the analysis of leaf shapes and compared it to IDSC, SC, and MDM. SRVF significantly outperformed the previous shape-based techniques. Among the lower performing techniques in this study, SC and MDM performed equally, IDCS achieved the lowest performance.

Fourier descriptors. Fourier descriptors (FD) are a classical method for shape recognition and have grown into a general method to encode various shape signatures. By applying a Fourier transform, a leaf shape can be analyzed in the frequency domain, rather than the spatial domain as done with shape signatures. A set number of Fourier harmonics are calculated for the outline of an object, each consisting of only four coefficients. These Fourier descriptors capture global shape features in the low frequency terms (low number of harmonics) and finer features of the shape in the higher frequency terms (higher numbers of harmonics). The advantages of this method are that it is easy to implement and that it is based on the well-known theory of Fourier analysis [33]. FD can easily be normalized to represent shapes independently of their orientation, size, and location; thus easing comparison between shapes. However, a disadvantage of FDs is that they do not provide local shape information since this information is distributed across all coefficients after the transformation [151]. A number of studies focused on FD, e.g., [147] use FD computed on distances of contour points from the centroid, which is advantageous for smaller datasets. Kadir et al. [72] propose a descriptor based on polar Fourier transform (PFT) to extract the shape of leaves and compared it with SMSD, Hu, and Zernike moments. Among those methods, PFT achieved the most prospective classification result. Aakif and Khan [1], Yanikoglu et al. [148] used FD in combination with SMSD. The authors obtained more accurate classification results by using FD than with SMSD alone. However, they achieved the best result by combining all descriptors. Novotny and Suk [106] used FD in combination with TMI and major axis length. Several studies that propose novel methods for leaf shape analysis benchmark their descriptor against FD in order to prove effectiveness [45, 62, 134, 147, 158].

Fractal dimension. The fractal dimension (FracDim) of an object is a real number used to represent how completely a shape fills the dimensional space to which it belongs. The FracDim descriptor can provide a useful measure of a leaf shape’s complexity. In theory, measuring the fractal dimension of leaves or flowers can quantitatively describe and classify even morphologically complex plants. Only a few studies used FracDim for leaf analysis [14, 65, 67] and flower analysis [3]. Bruno et al. [14] compare box-counting and multi-scale Minkowski estimates of fractal dimension. Although the box-counting method provided satisfactory results, Minkowski’s multi-scale approach proved superior in terms of characterizing plant species. Given the wide variety of leaf and flower shapes, characterizing their shape by a single value descriptor of complexity likely discards useful information, suggesting that the FracDim descriptors may only be useful in combination with other descriptors. For example, [65] demonstrated that leaf analysis with FracDim descriptors are effective and yield higher classification rates than Hu moments. When combining both, even better results were achieved. One step further, [14, 65, 67] proposed methods for combining the FD descriptor of a leaf’s shape with a FracDim descriptor computed on the venation of the leaf to rise classification performance (further details in the section about vein feature).

3.3.5 Color

Color is an important feature of images. Color properties are defined within a particular color space. A number of color spaces have been applied across the primary studies, such as red-green-blue (RGB), hue-saturation-value (HSV), hue-saturation-intensity (HSI), hue-max-min-diff (HMMD), LUV (aka CIELUV), and more recently Lab (aka CIELAB). Once a color space is specified, color properties can be extracted from images or regions. A number of general color descriptors have been proposed in the field of image recognition, e.g., color moments (CM), color histograms (CH), color coherence vector, and color correlogram [153]. CM are a rather simple descriptor, the common moments being mean, standard deviation, skewness, and kurtosis. CM are used for characterizing planar color patterns, irrespective of viewpoint or illumination conditions and without the need for object contour detection. CM is known for its low dimension and low computational complexity, thus, making it convenient for real-time applications. CH describes the color distribution of an image. It quantizes a color space into different bins and counts the frequency of pixels belonging to each color bin. This descriptor is robust to translation and rotation. However, CH does not encode spatial information about the color distribution. Therefore, visually different images can have similar CH. In addition, a histogram is usually of high dimensionality [153]. A major challenge for color analysis is light variations due to different intensity and color of the light falling from different angles. These changes in illumination can cause shadowing effects and intensity changes. For species classification the most studied descriptors are CM [16, 43, 48, 87, 116, 148] and CH [3, 16, 29, 57, 74, 87, 104, 105, 112, 148]. An overview of all primary studies that analyze color is shown in Table 8.

Leaf analysis. Only 8 of 106 studies applying leaf analysis also study color descriptors. We always found color descriptors being jointly studied together with leaf shape descriptors. Kebapci et al. [74] use three different color spaces to produce CH and color co-occurrence matrices (CCM) for assessing the similarity between two images; namely RGB, normalized RGB (nRGB), and HSI, where nRGB-CH facilitated the best results. Yanikoglu et al. [148] studied the effectiveness of color descriptors, specifically the RGB histogram and CM. The authors found CM to provide the most accurate results. However, the authors also found that color information did not contribute to the classification accuracy when combined with shape and texture descriptors. Caglayan et al. [16] defined different sets of color features. The first set consisted of mean and standard deviation of intensity values of the red, the green, and the blue channel and an average of these channels. The second set consisted of CH in red, green, and blue channels. The authors found the first four CM to be an efficient and effective way for representing color distribution of leaf images [43, 48, 116, 148]. Che Hussin et al. [27] proposed a grid-based CM as descriptor. Each image is divided into a 3x3 grid, then each cell is described by mean, standard deviation, and the third root of the skewness. In contrast, [87] evaluated the first three central moments, which they found to be not discriminative according their experimental results.

3.3.6 Texture

Texture is the term used to characterize the surface of a given object or phenomenon and is undoubtedly a main feature used in computer vision and pattern recognition [142, 153]. Generally, texture is associated to the feel of different materials to human touch. Texture image analysis is based on visual interpretation of this feeling. Compared to color, which is usually a pixel property, texture can only be assessed for a group of pixels [153]. Grayscale texture analysis methods are generally grouped into four categories: signal processing methods based on a spectral transform, such as, Fourier descriptors (FD) and Gabor filters (GF); statistical methods that explore the spatial distribution of pixels, e.g., co-occurrence matrices; structural methods that represent texture by primitives and rules; and model-based methods based on fractal and stochastic models. However, some recently proposed methods cannot be classified into these four categories. For instance, methods based on deterministic walks, fractal dimension, complex networks, and gravitational models [37]. An overview of all primary studies that analyze texture is shown in Table 9.

Leaf analysis. For leaf analysis, twelve studies analyzed texture solely and another twelve studies combined texture with other features, i.e., shape, color, and vein. The most frequently studied texture descriptors for leaf analysis are Gabor filter (GF) [17, 23, 32, 74, 132, 150], fractal dimensions (FracDim) [7, 8, 36, 37], and gray level co-occurrence matrix (GLCM) [23, 32, 43, 48].

GF are a group of wavelets, with each wavelet capturing energy at a specific frequency and in a specific direction. Expanding a signal provides a localized frequency description, thereby capturing the local features and energy of the signal. Texture features can then be extracted from this group of energy distributions. GF has been widely adopted to extract texture features from images and has been demonstrated to be very efficient in doing so [152]. Casanova et al. [17] applied GF on sample windows of leaf lamina without main venation and leaf margins. They observed a higher performance of GF than other traditional texture analysis methods such as FD and GLCM. Chaki et al. [23], Cope et al. [32] combined banks of GF and computed a series of GLCM based on individual results. The authors found the performance of their approach to be superior to standalone GF and GLCM. Yanikoglu et al. [148] used GF and HOG for texture analysis and found GF to have a higher discriminatory power. Venkatesh and Raghavendra [132] proposed a new feature extraction scheme termed local Gabor phase quantization (LGPQ), which can be viewed as the combination of GF with a local phase quantization scheme. In a comparative analysis the proposed method outperformed GF as well as the local binary pattern (LBP) descriptor.

Natural textures like leaf surfaces do not show detectable quasi-periodic structures but rather have random persistent patterns [63]. Therefore, several authors claim fractal theory to be better suited than statistical, spectral, and structural approaches for describing these natural textures. Authors found the volumetric fractal dimension (FracDim) to be very discriminative for the classification of leaf textures [8, 122]. Backes and Bruno [7] applied multi-scale volumetric FracDim for leaf texture analysis. de M Sa Junior et al. [36, 37] propose a method combining gravitational models with FracDim and lacunarity (counterpart to the FracDim that describes the texture of a fractal) and found it to outperform FD, GLCM, and GF.

Surface gradients and venation have also been exploited using the edge orientation histogram descriptor (EOH) [10, 10, 91, 148]. Here the orientations of edge gradients are used to analyze the macro-texture of the leaf. In order to exploit the venation structure, [25] propose the EAGLE descriptor for characterizing leaf edge patterns within a spatial context. EAGLE exploits the vascular structure of a leaf within a spatial context, where the edge patterns among neighboring regions characterize the overall venation structure and are represented in a histogram of angular relationships. In combination with SURF, the studied descriptors are able to characterize both local gradient and venation patterns formed by surrounding edges.

Elhariri et al. [43] studied first and second order statistical properties of texture. First order statistical properties are: average intensity, average contrast, smoothness, intensity histogram’s skewness, uniformity, and entropy of grayscale intensity histograms (GIH). Second order statistics (aka statistics from GLCM) are well known for texture analysis and are defined over an image to be the distribution of co-occurring values at a given offset [55]. The authors found that the use of first and second order statistical properties of texture improved classification accuracy compared to using first order statistical properties of texture alone. Ghasab et al. [48] derive statistics from GLCM, named contrast, correlation, energy, homogeneity, and entropy and combined them with shape, color, and vein features. Wang et al. [136] used dual-scale decomposition and local binary descriptors (DS-LBP). DS-LBP descriptors effectively combine texture and contour of a leaf and are invariant to translation and rotation.

3.3.7 Leaf-Specific Features

Leaf venation. Veins provide leaves with structure and a transport mechanism for water, minerals, sugars, and other substances. Leaf veins can be, e.g., parallel, palmate, or pinnate. The vein structure of a leaf is unique to a species. Due to a high contrast compared to the rest of the leaf blade, veins are often clearly visible. Analyzing leaf vein structure, also referred to as leaf venation, has been proposed in 16 studies (see Table 10).

Only four studies solely analyzed venation as a feature discarding any other leaf features, like, shape, size, color, and texture [53, 78, 79, 80]. Larese et al. [78, 79, 80] introduced a framework for identifying three legumes species on the basis of leaf vein features. The authors computed 52 measures per leaf patch (e.g., the total number of edges, the total number of nodes, the total network length, median/min/max vein length, median/min/max vein width). Larese et al. [80] defines and discusses each measure. The author [80] performed an experiment using images that were cleared using a chemical process (enhancing high contrast leaf veins and higher orders of visible veins), which increased their accuracy from 84.1 to 88.4% compared to uncleared images at the expense of time and cost for clearing. Gu et al. [53] processed the vein structure using a series of wavelet transforms and Gaussian interpolation to extract a leaf skeleton that was then used to calculate a number of run-length features. A run-length feature is a set of consecutive pixels with the same gray level, collinear in a given direction, and constituting a gray level run. The run length is the number of pixels in the run and the run length value is the number of times such a run occurs in an image. The authors obtained a classification accuracy of 91.2% on a 20 species dataset.

Twelve studies analyzed venation in combination with the shape of leaves [4, 5, 14, 65, 67, 101, 107, 108, 139, 144] and two studies analyzed venation in combination with shape, texture, and color [43, 48]. Nam et al. [101], Park et al. [107, 108] extract structure features in order to categorize venation patterns. Park et al. [107, 108] propose a leaf image retrieval scheme, which analyzes the venation of a leaf sketch drawn by the user. Using the curvature scale scope corner detection method on the venation drawing they categorize the density of feature points (end points and branch points) by using non-parametric estimation density. By extracting and representing these venation types, they could improve the classification accuracy from 25 to 50%. Nam et al. [101] performed classification on graph representations of veins and combined it with modified minimum perimeter polygons as shape descriptor. The authors found their method to yield better results than CSS, CCD, and FD. Four groups of researchers [5, 43, 48, 144] studied the ratio of vein-area (number of pixels that represent venation) and leaf-area (\(A_{vein}/ A_{leaf}\)) after morphological opening. Elhariri et al. [43], Ghasab et al. [48] found that using a combination of all features (vein, shape, color, and texture) yielded the highest classification accuracy. Wang et al. [139] used SC and SIFT extracted from contour and vein sample points. They noticed that vein patterns are not always helpful for SC based classification. Since in their experiments, vein extraction based on simple Canny edge detection generated noisy outputs utilizing the resulting vein patterns in shape context led to unstable classification performance. The authors claim that this problem can be remedied with advanced vein extraction algorithms [139]. Bruno et al. [14], Ji-Xiang et al. [65] and Jobin et al. [67] studied FracDim extracted from the venation and the outline of leafs and obtained promising results. Bruno et al. [14] argues that the segmentation of a leaf venation system is a complex task, mainly due to low contrast between the venation and the rest of the leaf blade structure. The authors propose a methodology divided into two stages: (i) chemical leaf clarification, and (ii) segmentation by computer vision techniques. Initially, the fresh leaf collected in the herbarium, underwent a chemical process of clarification. The purpose was removing the genuine leaf pigmentation. Then, the fresh leaves were digitalized by a scanner. Ji-Xiang et al. [65], Jobin et al. [67] did not use any chemical or biological procedure to physically enhance the leaf veins. They obtained a classification accuracy of 87% on a 30 species dataset and 84% on a 50 species dataset, respectively.

Leaf margin. All leaves exhibit margins (leaf blade edges) that are either serrated or unserrated. Serrated leaves have teeth, while unserrated leaves have no teeth and are described as being smooth. These margin features are very useful for botanists when describing leaves, with typical descriptions including details such as the tooth spacing, number per centimeter, and qualitative descriptions of their flanks (e.g., convex or concave). Leaf margin has seen little use in automated species identification with 8 out of 106 studies focusing on it (see Table 10). Studies usually combine margin analysis with shape analyses [18, 20, 21, 73, 85, 93]. Two studies used margin as sole feature for analysis [31, 66].

3.4.1 Swedish Leaf Dataset

Classifiers. For the Swedish leaf dataset, nearly all authors apply a k-nearest neighbor (k-NN) classifier [62, 62, 83, 94, 136, 147], occasionally in the simple 1-NN form [134, 135, 145, 158], to perform classification and to evaluate their approaches (see Table 11). k-NN is a non-parametric classification algorithm that classifies unknown samples based to their k nearest neighbors among the training samples. The most frequent class among these k neighbors is chosen as the class for the sample to be classified. A challenge of k-NN is to select an appropriate value of k, typically based on error rates [16]. In order to improve robustness and discriminability of classification, a fuzzy k-nearest neighbors classifier was proposed [136]. Unlike the conventional k-NN, which only considers the congeneric number of k-nearest neighbors, fuzzy k-NN synthetically considers the congeneric number and the similarity between the k-nearest neighbors and the unknown sample. Only one study used support vector machines (SVM) as classifier on this dataset. A Radial basis function (RBF) kernel for the SVM was used [121], which can handle a high dimensional space of data points that are not linearly separable. SVM are known as classifiers with simple structure and comparatively fast training phase and are easy to implement.

Classification accuracies. Table 11 shows classification accuracies achieved on the Swedish leaf dataset with the different methods proposed in the primary studies. The four lowest classification rates are obtained with Gabor Filter (GF) (85.75%), Shape Context (SC) (88.12%), and Fourier descriptor (FD) (87.54 and 89.60%) classified using fuzzy-k-NN, k-NN, and 1-NN. As discussed in the feature section, [94] found TSLA to give better identification scores than TAR, TOA, and TSL. Xiao et al. [145] noticed that the IDSC descriptor performs better than HOG on the original Swedish leaf dataset. Ren et al. [121] used multi-scale overlapped block local binary pattern (LBP) with a SVM classifier and obtained the fourth best classification performance on this dataset. Zhao et al. [158] introduced I-IDSC and obtained with 97.07% the third best result. The multi-scale-arch-height descriptor (MARCH) method [135] achieved the second best classification rate (97.33%). The best result with 99.25% was obtained by [136]. They used dual-scale decomposition and local binary descriptors (DS-LPB). DS-LBP combines textures and contour information of a leaf and is invariant to translation and rotation.

Images of the Swedish leaf dataset contain leafstalks. The benefit of leafstalks is controversially debated by authors. On one hand, they can provide discriminant information for classification, but on the other hand length and orientation of leafstalks depends on the collection and imaging process and is therefore considered unreliable. Table 11 shows that for all with and without leafstalks studied descriptors the classifications accuracy dropped when removing leafstalks, e.g., the performance of IDSC decreased from 93.73 to 85.07%. This result indicates that leafstalks indeed provide useful information for recognition.

3.4.2 ICL Dataset

Table 12 shows classification accuracies on the ICL leaf dataset using the methods proposed in the primary studies. The upper part of the table shows results gained on the whole dataset containing 220 species. Several studies do not use the whole dataset, but merely evaluate their approaches on two subsets of the ICL leaf dataset (subset A and B). Subset A includes 50 species sharing the characteristic that the contained species’ shapes can be distinguished easily by humans. Subset B also includes 50 species with shapes that are very similar but still distinguishable [62, 121, 145, 158]. Furthermore, [147, 156, 157] also used a subset of the ICL dataset but without specifying the selected species. Their results are not considered for comparison here.

Classifier. The set of utilized classification methods (k-NN, 1-NN, fuzzy k-NN, and SVM) is the same as for the Swedish leaf dataset.

3.4.3 Flavia Dataset

The Flavia dataset is a benchmark used by researchers to compare and evaluate methods across studies and publications. The dataset contains leaf images of 32 different species. Table 13 shows a comparison of different methods applied by the primary studies on the Flavia dataset.

Classifier. Primary studies used a richer set of classification methods for their experiments on the Flavia dataset compared to the Swedish leaf dataset and the ICL dataset. In addition to the previously mentioned k-NN and SVM classifiers, also the following methods were used: Naive Bayes (NB) [16], decision tree (DT) [116], random forest (RF) [16], neuro fuzzy classifier (NFC) [23, 24], multi-layered perceptron (MLP) [23], Riemannian metrics [77], artificial neural network (ANN) with back-propagation (BPNN) [1], and probabilistic neural networks (PNN) [58, 144]. Bayesian classifiers are statistical models able to predict the probability for an unknown sample to belong to a specific class. They are a practical learning approach based on Bayes’ Theorem. A disadvantage of Bayesian classifiers is that conditional independence may decrease accuracy thereby imposing a constraint over attributes that may not be dependent. A Decision Tree is a classifier that uses a tree-like graph to represent decisions and their possible consequences. A decision tree consists of three types of nodes: decision nodes, which evaluate each feature at a time according their relevance; chance nodes, which choose between possible values of features; and end nodes, which represent the final decision, i.e., the wing label. The Random Forest classifier is based on the classification tree approach. It aggregates predictions of multiple classification trees for a dataset. Each tree in the forest is grown using bootstrap samples. At prediction time, classification results are taken from each tree in the forest. The class with the most votes among the separate trees is selected by the forest. Random forests are efficient on large datasets with high accuracy. Random forests also allow to estimate the importance of input variables (in their original dimensional space). However, they have constraints on memory and computing time. Finally, an artificial neural network (ANN) is an interconnected group of artificial neurons simulating the thinking process of the human brain. One can consider an ANN as a “magical” black box trained to achieve an expected intelligent process, against the input and output information stream [144].

Classification accuracies. The lowest classification rates with 25.30% were obtained with Hu moments [111] and Hu moments in combination with curvelet transform 41.6% [23]. The results demonstrate that the Hu descriptor is not robust when working with leaf shape and should be combined with other features like vein, margin, color, or texture [23, 111]. Prasad et al. [116] study shape and color information of leaves using SMSD and FD to represent the shape. Once the initial classification is calculated solely based on these shape descriptors using k-NN, the two classes with the highest probability are selected. Then, color is analyzed and a binary decision tree is used to decide between these two classes. Prasad et al. [116] found that color information of leaves increased accuracy from 84.45% (shape only) to 91.30% (shape + color). Arun Priya et al. [5] compared SVM with RBF kernel and k-NN classification based on shape and vein features and found that SVM with 94.5% outperformed k-NN with only 78%. Caglayan et al. [16] compared four classification algorithms: k-NN, SVM with linear kernel function, Naive Bayes, and Random Forest based on shape and color features. Across all their experiments, Random Forest yielded the best classification results. The lowest accuracy was achieved with SVM based on shape features. Combining shape and color increased classification accuracy significantly. The greatest increase was demonstrated with SVM using SMSD, color moments, and color histograms improving accuracy about 15% compared to a Naive Bayes classifier using the same features. Wang et al. [140] obtained the highest accuracy on the Flavia dataset with 97.80% by combining EnS representing texture features with CDS representing shape features and utilized SVM with RBF kernel for classification (see results of ICL dataset).