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The automatic identification of plant species using unmanned aerial vehicles (UAVs) is a valuable tool for ecological research. However, challenges such as reduced spatial resolution due to high-altitude operations, image degradation from camera optics and sensor limitations, and information loss caused by terrain shadows hinder the accurate classification of plant species from UAV imagery. This study addresses these issues by proposing a novel image preprocessing pipeline and evaluating its impact on model performance. Our approach improves image quality through a multi-step pipeline that includes Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) for resolution enhancement, Contrast-Limited Adaptive Histogram Equalization (CLAHE) for contrast improvement, and white balance adjustments for accurate color representation. These preprocessing steps ensure high-quality input data, leading to better model performance. For feature extraction and classification, we employ a pre-trained VGG-16 deep convolutional neural network, followed by machine learning classifiers, including Support Vector Machine (SVM), random forest (RF), and Extreme Gradient Boosting (XGBoost). This hybrid approach, combining deep learning for feature extraction with machine learning for classification, not only enhances classification accuracy but also reduces computational resource requirements compared to relying solely on deep learning models. Notably, the VGG-16 + SVM model achieved an outstanding accuracy of 97.88% on a dataset preprocessed with ESRGAN and white balance adjustments, with a precision of 97.9%, a recall of 97.8%, and an F1 score of 0.978. Through a comprehensive comparative study, we demonstrate that the proposed framework, utilizing VGG-16 for feature extraction, SVM for classification, and preprocessed images with ESRGAN and white balance adjustments, achieves superior performance in plant species identification from UAV imagery.

Life on Earth depends heavily on plants. Various methods have been developed to extract a plant leaf’s attributes. In order to extract the characteristics, several classification techniques are used. Because there are so many different kinds of plants in the world, it may be difficult to remember their names; a method for identifying plant types was developed. The conventional way to identify a plant is by its leaves. To extract the leaf-based characteristics from the leaf picture, image processing techniques are applied. Eventually, leaf identification was accomplished using machine learning techniques. This article contrasts the methods used by other authors that have worked on various occluded leaf species identification methodologies with this recommended method for identifying leaf species.The unique method for expressing forms described in this article is termed “Tracing Leaf Outline” (TLO). The TLO approach has a low processing complexity and is also reasonably easy to apply. It is demonstrated that this technique can identify the leaf species although there is a sizable proportion of leaf occlusion (let’s say, about 50% occlusion). Standard datasets for Flavia leaf species have been used to test this method. The efficiency of the proposed technique is evaluated using accuracy, precision, recall, and F1 score. These measures have produced a result of 99.6%, which is greater than all prior findings, and have significantly exceeded the traditional methodologies for identifying plant leaf species.

The study aimed to determine the efficacy and capabilities of using high-resolution aerial imagery and a convolutional neural network (CNN) to identify plant species and monitor land cover and land change in the context of remote sensing. The full capabilities of a CNN were examined, including testing whether the platform could be used for land cover and the evaluation of land change over time. An unmanned aerial vehicle (UAV) was used to collect the aerial data of the study area. The CNN was encoded and operated in RStudio, while digitised data from the input imagery were used by the programme as training and validation data. The object in this respect was to learn about the relevant features of the landscape, and thereafter to classify the Opuntia invasive plant species. Accuracy assessments were carried out on the results to test the efficacy of the aerial imagery in terms of its accuracy and reliability. The classification achieved an overall accuracy of 93%, while the kappa coefficient score was 0.86. CNN was also able to predict the land coverage area of Opuntia to be within four percent (4%) of the ground truthing data. A change in land cover over time was detected by the programme after the manual clearing of the plant had been undertaken. This research has determined that the use of a CNN in remote sensing is a very powerful tool for supervised image classifications. It can be used for monitoring land cover in that it is able to accurately estimate the spatial distribution of plant species and to monitor the growth or decline in the species over time. As such, it is an efficient methodology and its use in remote sensing could be extended. [ABSTRACT FROM AUTHOR]

Abstract Accurate species identification is essential for ecological monitoring and biodiversity conservation. Interactive plant identification keys have been considerably improved in recent years, mainly by providing iconic symbols, illustrations, or images for the users, as these keys are also commonly used by people with relatively little plant knowledge. Only a few studies have investigated how well morphological characteristics can be recognized and correctly identified by people, which is ultimately the basis of an identification key's success. This study consists of a systematic evaluation of people's abilities in identifying plant‐specific morphological characters. We conducted an online survey where 484 participants were asked to identify 25 different plant character states on six images showing a plant from different perspectives. We found that survey participants correctly identified 79% of the plant characters, with botanical novices with little or no previous experience in plant identification performing slightly worse than experienced botanists. We also found that flower characters are more often correctly identified than leaf characteristics and that characters with more states resulted in higher identification errors. Additionally, the longer the time a participant needed for answering, the higher the probability of a wrong answer. Understanding what influences users' plant character identification abilities can improve the development of interactive identification keys, for example, by designing keys that adapt to novices as well as experts. Furthermore, our study can act as a blueprint for the empirical evaluation of identifications keys. Read the free Plain Language Summary for this article on the Journal blog.

Life on Earth depends heavily on plants. Various methods have been developed to extract a plant leaf's attributes. In order to extract the characteristics, several classification techniques are used. Because there are so many different kinds of plants in the world, it may be difficult to remember their names; a method for identifying plant types was developed. The conventional way to identify a plant is by its leaves. To extract the leaf-based characteristics from the leaf picture, image processing techniques are applied. Eventually, leaf identification was accomplished using machine learning techniques. This article contrasts the methods used by other authors that have worked on various occluded leaf species identification methodologies with this recommended method for identifying leaf species. The unique method for expressing forms described in this article is termed "Tracing Leaf Outline" (TLO). The TLO approach has a low processing complexity and is also reasonably easy to apply. It is demonstrated that this technique can identify the leaf species although there is a sizable proportion of leaf occlusion (let's say, about 50% occlusion). Standard datasets for Flavia leaf species have been used to test this method. The efficiency of the proposed technique is evaluated using accuracy, precision, recall, and F1 score. These measures have produced a result of 99.6%, which is greater than all prior findings, and have significantly exceeded the traditional methodologies for identifying plant leaf species. [ABSTRACT FROM AUTHOR]

In recent works of computer science, especially in the fields of image processing and pattern recognition techniques with machine learning, considerable focus is given to plant taxonomy which enhances the abilities of people to recognize plant species. This paper presents a method that analyzes color images of leaves using a type of Convolutional Neural Network to recognize plant species. The proposed Neural Network consists of four convolutional layers followed by two Fully-Connected layers and a final soft-max layer to offer a feature representation for different plant species. Four max-pooling layers are performed over a 2 × 2 pixel window with stride 2. Results on five plant datasets viz. Leaf snap (52 plant species), UCI leaf (40 plant species), PlantVillage (38 plant species), Flavia (32 plant species) and Swedish (15 plant species) are tabulated that demonstrate the remarkable performance of the proposed deep neural network when compared to the state of art methods. [ABSTRACT FROM AUTHOR]

A critical and challenging in pattern recognition is the identification of plant species from obstructed leaf photographs. The biggest issue at that time is to accurately identify the species of leaf when all of the leaves are identical in appearance and obscured. Shape is one of the most important visual elements and is also recognized as a fundamental attribute for conveying the content of pictures. Since it can be difficult to gauge how similar distinct forms are to one another, as well as describe the content of shapes. The two main categories of shape descriptors are region-based and contour-based shape descriptors (CBSD) techniques. Region-based approaches use the complete area of an item for shape description as opposed to contour-based approaches that only use the information contained in an image's contour. In this study, we presented a shape description approach called Particular Contour-Based Shape Descriptors (PCBSD) for the identification of the plant leaves since the CBSD recovered the low level visual properties of the pictures. This method successfully captures the local and global characteristics of a leaf shape while preserving the translation, rotation, and scaling similarity transformations. This method is also quite compact and has a low processing complexity. To evaluate our experiments, we utilized Flavia datasets of typical plant leaves. We show that our technique created the best complete leaf match when high occlusion (around 50% occlusion) occurs. We may say that our method exceeds prior state-of-the-art shape-based plant leaf recognition algorithms and it generates accuracy of 76%.Picture processing methods are used to separate the leaf-based characteristics from the leaf image. Eventually, using machine learning methods, then leaf identification was accomplished. [ABSTRACT FROM AUTHOR]

Accurate species identification is essential for ecological monitoring and biodiversity conservation. Interactive plant identification keys have been considerably improved in recent years, mainly by providing iconic symbols, illustrations, or images for the users, as these keys are also commonly used by people with relatively little plant knowledge. Only a few studies have investigated how well morphological characteristics can be recognized and correctly identified by people, which is ultimately the basis of an identification key's success.This study consists of a systematic evaluation of people's abilities in identifying plant‐specific morphological characters. We conducted an online survey where 484 participants were asked to identify 25 different plant character states on six images showing a plant from different perspectives.We found that survey participants correctly identified 79% of the plant characters, with botanical novices with little or no previous experience in plant identification performing slightly worse than experienced botanists. We also found that flower characters are more often correctly identified than leaf characteristics and that characters with more states resulted in higher identification errors. Additionally, the longer the time a participant needed for answering, the higher the probability of a wrong answer.Understanding what influences users' plant character identification abilities can improve the development of interactive identification keys, for example, by designing keys that adapt to novices as well as experts. Furthermore, our study can act as a blueprint for the empirical evaluation of identifications keys. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Plant species identification represents an extraordinary challenge for machine learning due to visual interspecies similarities and large intraspecies variations. Furthermore, research literature reports that plant species identification usually lacks sufficiently large datasets for training classification models. In this paper, we address this problem with a model that simplifies the VGG-16 architecture, the N-VGG model. The idea behind N-VGG is to reduce experimentally observed overfitting on VGG-16 by using as few trainable parameters as possible. To do this, we substitute the flattening layer on the VGG architecture with a global average pooling layer. This reduces the size of the feature vector. In addition, we eliminate one of the two fully-connected layers and use a new hyper-parameter, N, to indicate the number of nodes on the remaining layer. To show the robustness of the N-VGG model, we conducted extensive experimentation. We trained N-VGG on five datasets for plant species identification. Four of these datasets are publicly available and have been widely used as benchmarks for plant identification models. For all datasets, we compare the accuracy of N-VGG to that of the VGG-16, Inception-v4, and EfficienNet-B3 models. The experimental results show that the N-VGG model achieved the best classification performance for all but one datasets, whereas all the models showed a remarkable performance for the remaining dataset. This evidence supports our initial idea that, for plant species classification, some accuracy might be lost due to overfitting and that having fewer trainable parameters helps in producing a more robust model. [ABSTRACT FROM AUTHOR]

In this paper, we propose an effective composite descriptor combining contour and appearance features for plant species identification. The composite descriptor uses two different fusion schemes: the first is a method based on the fusion of low-level local contour and appearance features; that is, we use local triangle features and local speeded-up robust features (SURF) to describe leaf images, respectively. Then, we utilize the Hausdorff distance to measure the leaf discrepancy to complete the plant identification task. The second is a method of global feature fusion based on high-level shape and appearance features. Specifically, the Fisher vector encodes the local triangle, and SURF features into high-level shape and appearance features. Then, the Euclidean distance is used to compute the dissimilarity of leaves to realize plant leaf recognition. The composite descriptor based on these two fusion schemes can well describe the characteristics of leaf images and improve the ability to identify plant leaves. This composite descriptor is extensively tested on several standard plant databases, including simple, compound, and natural scene plant leaf datasets. Experimental results demonstrate that the composite descriptor achieves high recognition accuracy, outperforming existing state-of-the-art benchmarks. In addition, we further applied our method directly to shape recognition and achieved good recognition results, demonstrating the wide range of applications of our method. • We present an effective contour representation named local triangle feature. • We propose two fusion schemes to effectively combine the contour and appearance characteristics for plant identification. • The experimental results show that our method outperforms the state-of-the-art plant identification approaches. [ABSTRACT FROM AUTHOR]

Plant species identification based on plant leaves is important for biological science, ecological science, and agricultural digitization. Because of the complexity and variation of the plant leaves, many classical plant species identification algorithms using plant leaf images are not enough for practical application. A modified local discriminant projection (MLDP) algorithm is proposed for plant species identification. MLDP aims to extract discriminant features for plant species identification by taking class label information into account based on the property of locality preserving. The MLDP can preserve the local geometrical structure of leaves and extract the strong discriminative ability. The experimental results on the public ICL leaf image database show the effectiveness and feasibleness of the proposed method. [ABSTRACT FROM AUTHOR]

While plant species identification in forensics can be useful in cases involving poisonous, psychoactive, or endangered plant species, it can also become quite challenging, especially, when dealing with processed, decaying, colonized or infected material of plant origin. The Animal Plant and Soil Traces expert working group of the European Network of Forensic Science Institutes in their best practice manual has recommended several markers for plant species identification. Current study is a part of implementation of method in a forensic laboratory and its aim is to evaluate four of the recommended markers (ITS , matK , rbcL , and trnH-psbA) for species identification of forensically important plant species including medicinal, poisonous, psychoactive, and other plants. Such parameters as PCR and sequencing success, sequence length, species resolution rate and species cover in GenBank were analysed. Blind testing was performed to evaluate use of the markers for identification of forensically more complicated samples. According to results, a combination of ITS , matK and trnH-psbA is the best choice for plant species identification. The best results with fresh plant material can be achieved with ITS, trnH-psbA, and matK , while ITS and matK are the best choice when working with low quality plant material. rbcL due to its low species discrimination rate can be used only as an indicative marker. • Markers ITS , matK , rbcL , and trnH-psbA were tested for plant species identification. • With fresh plant material best results were gained by combination of ITS , trnH-psbA and matK. • In blind test with old and dried plant material best results were achieved with ITS and matK. • rbcL has low species resolution rate and can be used only as an indicative marker. [ABSTRACT FROM AUTHOR]

With the increase in the digitization efforts of herbarium collections worldwide, dataset repositories such as iDigBio and GBIF now have hundreds of thousands of herbarium sheet images ready for exploration. Although this serves as a new source of plant leaves data, herbarium datasets have an inherent challenge to deal with the sheets containing other non-plant objects such as color charts, barcodes, and labels. Even for the plant part itself, a combination of different overlapping, damaged, and intact individual leaves exist together with other plant organs such as stems and fruits, which increases the complexity of leaf trait extraction and analysis. Focusing on segmentation and trait extraction on individual intact herbarium leaves, this study proposes a pipeline consisting of deep learning semantic segmentation model (DeepLabv3+), connected component analysis, and a single-leaf classifier trained on binary images to automate the extraction of an intact individual leaf with phenotypic traits. The proposed method achieved a higher F1-score for both the in-house dataset (96%) and on a publicly available herbarium dataset (93%) compared to object detection-based approaches including Faster R-CNN and YOLOv5. Furthermore, using the proposed approach, the phenotypic measurements extracted from the segmented individual leaves were closer to the ground truth measurements, which suggests the importance of the segmentation process in handling background noise. Compared to the object detection-based approaches, the proposed method showed a promising direction toward an autonomous tool for the extraction of individual leaves together with their trait data directly from herbarium specimen images.

The accurate identification of plant species is crucial for the conservation of biodiversity. However, traditional methods for identifying plant species are often complicated, time-consuming, and prone to errors. Therefore, it is essential to address these challenges and develop automated identification methods to enhance the efficiency and accuracy of plant species identification. In this study, a step-by-step method was utilized to identify and classify plant species. The dataset was first loaded, and then preprocessing was performed to remove noisy data. Following that, data augmentation was carried out to improve model accuracy. The deep convolutional neural network (CNN) and visual geometry group-16 (VGG-16) were then employed to extract only the relevant features, owing to their efficient learning capabilities. Feature-level fusion was accomplished by utilizing dimensionality reduction, and enhanced Spearman's principal component analysis (ESPCA) was employed to address the overfitting problem, eliminate redundant data, and reduce storage space and training time requirements. For classification, the hyperparameter-tuned batch-updated stochastic gradient descent (HP-BSGD) method was utilized. The Flavia and Swedish datasets were utilized in the experiments. The proposed hybrid classifier yielded excellent results due to its high convergence speed, good computational effectiveness, and high flexibility. To validate the experimental results, performance and comparative analyses were carried out using standard metrics. The analytical results demonstrated the superior efficiency and suitability of the proposed method in the classification of plant species over existing methods. The hybrid method achieved approximately 97% and 98.85% accuracy in the Flavia and Swedish datasets, respectively, when considering combined features. The performance of the proposed method was further enhanced by considering leaves at different stages, such as seedlings, tiny, mature, and dried leaves. • The system enhanced identification accuracy via multi-feature fusion extraction. • CNN and VGG-16 were used along with HPBSGD and ESPCA for the plant species identification. • Multi-feature fusion on leaf stages boosted system performance. • Decision support system has been improved with the combination of shallow and deep features. • Performance enhanced by considering leaves at different stages - seedlings, tiny, mature, and dried leaves. [ABSTRACT FROM AUTHOR]

Dried roots of Dipsacus asper (Caprifoliaceae) are used as important traditional herbal medicines in Korea. However, the roots are often used as a mixture or contaminated with Dipsacus japonicus in Korean herbal markets. Furthermore, the dried roots of Phlomoides umbrosa (Lamiaceae) are used indiscriminately with those of D. asper, with the confusing Korean names of Sok-Dan and Han-Sok-Dan for D. asper and P. umbrosa, respectively. Although D. asper and P. umbrosa are important herbal medicines, the molecular marker and genomic information available for these species are limited. In this study, we analysed DNA barcodes to distinguish among D. asper, D. japonicus, and P. umbrosa and sequenced the chloroplast (CP) genomes of D. asper and D. japonicus. The CP genomes of D. asper and D. japonicus were 160,530 and 160,371 bp in length, respectively, and were highly divergent from those of the other Caprifoliaceae species. Phylogenetic analysis revealed a monophyletic group within Caprifoliaceae. We also developed a novel sequence characterised amplified region (SCAR) markers to distinguish among D. asper, D. japonicus, and P. umbrosa. Our results provide important taxonomic, phylogenetic, and evolutionary information on the Dipsacus species. The SCAR markers developed here will be useful for the authentication of herbal medicines.

Background: DNA barcoding is widely used and most efficient approach that facilitates rapid and accurate identification of plant species based on the short standardized segment of the genome. The nucleotide sequences of maturaseK (matK) and ribulose-1, 5-bisphosphate carboxylase (rbcL) marker loci are commonly used in plant species identification. Here, we present a new and highly efficient approach for identifying a unique set of discriminating nucleotide patterns to generate a signature (i.e. regular expression) for plant species identification. Methods: In order to generate molecular signatures, we used matK and rbcL loci datasets, which encompass 125 plant species in 52 genera reported by the CBOL plant working group. Initially, we performed Multiple Sequence Alignment (MSA) of all species followed by Position Specific Scoring Matrix (PSSM) for both loci to achieve a percentage of discrimination among species. Further, we detected Discriminating Patterns (DP) at genus and species level using PSSM for the matK dataset. Combining DP and consecutive pattern distances, we generated molecular signatures for each species. Finally, we performed a comparative assessment of these signatures with the existing methods including BLASTn, Support Vector Machines (SVM), Jrip-RIPPER, J48 (C4.5 algorithm), and the Naïve Bayes (NB) methods against NCBI-GenBank matK dataset. Results: Due to the higher discrimination success obtained with the matK as compared to the rbcL, we selected matK gene for signature generation. We generated signatures for 60 species based on identified discriminating patterns at genus and species level. Our comparative assessment results suggest that a total of 46 out of 60 species could be correctly identified using generated signatures, followed by BLASTn (34 species), SVM (18 species), C4.5 (7 species), NB (4 species) and RIPPER (3 species) methods As a final outcome of this study, we converted signatures into QR codes and developed a software matK-QR Classifier (http://www.neeri.res.in/matk_classifier/index.htm), which search signatures in the query matK gene sequences and predict corresponding plant species. Conclusions: This novel approach of employing pattern-based signatures opens new avenues for the classification of species. In addition to existing methods, we believe that matK-QR Classifier would be a valuable tool for molecular taxonomists enabling precise identification of plant species. [ABSTRACT FROM AUTHOR]

Nowadays, global warming and its resulting environmental changes is a hot topic in different biology research area. Phenology is one effective way of tracking such environmental changes through the study of plant’s periodic events and their relationship to climate. One promising research direction in this area relies on the use of vegetation images to track phenology changes over time. In this scenario, the creation of effective image-based plant identification systems is of paramount importance. In this paper, we propose the use of a new representation of time series to improve plants recognition rates. This representation, called recurrence plot (RP), is a technique for nonlinear data analysis, which represents repeated events on time series into a two-dimensional representation (an image). Therefore, image descriptors can be used to characterize visual properties from this RP images so that these features can be used as input of a classifier. To the best of our knowledge, this is the first work that uses recurrence plot for plant recognition task. Performed experiments show that RP can be a good solution to describe time series. In addition, in a comparison with visual rhythms (VR), another technique used for time series representation, RP shows a better performance to describe texture properties than VR. On the other hand, a correlation analysis and the adoption of a well successful classifier fusion framework show that both representations provide complementary information that is useful for improving classification accuracies. [ABSTRACT FROM AUTHOR]

Global warming and its resulting environmental changes surely are ubiquitous subjects nowadays and undisputedly important research topics. One way of tracking such environmental changes is by means of phenology, which studies natural periodic events and their relationship to climate. Phenology is seen as the simplest and most reliable indicator of the effects of climate change on plants and animals. The search for phenological information and monitoring systems has stimulated many research centers worldwide to pursue the development of effective and innovative solutions in this direction. One fundamental requirement for phenological systems is concerned with achieving fine-grained recognition of plants. In this sense, the present work seeks to understand specific properties of each target plant species and to provide the solutions for gathering specific knowledge of such plants for further levels of recognition and exploration in related tasks. In this work, we address some important questions such as: (i) how species from the same leaf functional group differ from each other; (ii) how different pattern classifiers might be combined to improve the effectiveness results in target species identification; and (iii) whether it is possible to achieve good classification results with fewer classifiers for fine-grained plant species identification. In this sense, we perform different analysis considering RGB color information channels from a digital hemispherical lens camera in different hours of day and plant species. A study about the correlation of classifiers associated with time series extracted from digital images is also performed. We adopt a successful selection and fusion framework to combine the most suitable classifiers and features improving the plant identification decision-making task as it is nearly impossible to develop just a single “silver bullet” image descriptor that would capture all subtle discriminatory features of plants within the same functional group. This adopted framework turns out to be an effective solution in the target task, achieving better results than well-known approaches in the literature. [ABSTRACT FROM AUTHOR]

Herbal medicines are vigorously marketed, but poorly regulated. Analysis methodology for this field is still forming. One particular analytical task is confirmation of plant species identity for medicinal plants used as ingredients. In this work, machine learning approach has been implemented for LC–MS plant species identification. Samples for 36 plant species have been analyzed. Peak data ( m / z , abundance) from respective samples have been used for development of classification algorithms. Namely, logistic regression (LR), support vector machine (SVM) and random forest (RF) techniques were used. For most of used machine learning algorithms, classification accuracy of 95 % higher were obtained on cross-validation dataset. Now, massive training datasets are needed for full-scale application of this approach. [ABSTRACT FROM AUTHOR]

Leaf is one of the most important organs of plant. Leaf contour or outline, usually a closed curve, is a fundamental morphological feature of leaf in botanical research. In this paper, a novel shape descriptor based on periodic wavelet series and leaf contour is presented, which we name as Periodic Wavelet Descriptor (PWD). The PWD of a leaf actually expresses the leaf contour in a vector form. Consequently, the PWD of a leaf has a wide range in practical applications, such as leaf modeling, plant species identification and classification, etc. In this work, the plant species identification and the leaf contour reconstruction, as two practical applications, are discussed to elaborate how to employ the PWD of a plant leaf in botanical research. [ABSTRACT FROM AUTHOR]

Herbaria contain the treasure of millions of specimens that have been preserved for several years for scientific studies. To increase the rate of scientific discoveries, digitization of these specimens is currently ongoing to facilitate the easy access and sharing of data to a wider scientific community. Online digital repositories such as Integrated Digitized Biocollection and the Global Biodiversity Information Facility have already accumulated millions of specimen images yet to be explored. This presents the perfect time to take advantage of the opportunity to automate the identification process and increase the rate of novel discoveries using computer vision (CV) and machine learning (ML) techniques. In this study, a systematic literature review of more than 70 peer-reviewed publications was conducted focusing on the application of computer vision and machine learning techniques to digitized herbarium specimens. The study categorizes the different techniques and applications that are commonly used for digitized herbarium specimens and highlights existing challenges together with their potential solutions. We hope this study will serve as a firm foundation for new researchers in the relevant disciplines and will also be enlightening to both computer science and ecology experts. • Difficulty in fair comparison and evaluation of plant identification methods. • Deep learning with transfer learning as the common approach for most of the studies. • Higher success for the plant identification task compared to phenological studies. • Biology related journals and conferences as the main platforms for publications. • Strong collaboration exists between computer scientists and biologists. [ABSTRACT FROM AUTHOR]

description is given of interactive multiple-entry polytomous computer key designed to identify 102 Siberia's Astragalus species and subspecies. At each identification step the program reduces the species list, shortens the lists of qualitative character states, recalculates the diagnostic value of characters, and rank the characters according to their diagnostic value. In the process of identification, a user can return to previous identification steps, to get text descriptions and drawing of species and photos of herbarium specimens, to use an interactive single-entry dichotomous key, to change and input new data to the data table. The program is local; it has been made in the Microsoft Access 2002. [ABSTRACT FROM AUTHOR]

In this paper, a novel heuristic structure optimization methodology for radial basis probabilistic neural networks (RBPNNs) is proposed. First, a minimum volume covering hyperspheres (MVCH) algorithm is proposed to select the initial hidden-layer centers of the RBPNN, and then the recursive orthogonal least square algorithm (ROLSA) combined with the particle swarm optimization (PSO) algorithm is adopted to further optimize the initial structure of the RBPNN. The proposed algorithms are evaluated through eight benchmark classification problems and two real-world application problems, a plant species identification task involving 50 plant species and a palmprint recognition task. Experimental results show that our proposed algorithm is feasible and efficient for the structure optimization of the RBPNN. The RBPNN achieves higher recognition rates and better classification efficiency than multilayer perceptron networks (MLPNs) and radial basis function neural networks (RBFNNs) in both tasks. Moreover, the experimental results illustrated that the generalization performance of the optimized RBPNN in the plant species identification task was markedly better than that of the optimized RBFNN. [ABSTRACT FROM AUTHOR]

In this paper, an efficient computer-aided plant species identification (CAPSI) approach is proposed, which is based on plant leaf images using a shape matching technique. Firstly, a Douglas–Peucker approximation algorithm is adopted to the original leaf shapes and a new shape representation is used to form the sequence of invariant attributes. Then a modified dynamic programming (MDP) algorithm for shape matching is proposed for the plant leaf recognition. Finally, the superiority of our proposed method over traditional approaches to plant species identification is demonstrated by experiment. The experimental result showed that our proposed algorithm for leaf shape matching is very suitable for the recognition of not only intact but also partial, distorted and overlapped plant leaves due to its robustness. [ABSTRACT FROM AUTHOR]