Plant Identification Method Research Articles

Phytochemical profiling and HPTLC studies on leaf of Chrozophora rottleri (Geiseler) Spreng

For centuries, plants have been integral to traditional medicine, food preservation, and disease prevention. Chrozophora rottleri (Geiseler) A. Juss. ex Spreng., an annual herb of the Euphorbiaceae family, is widely used across India, Myanmar, Thailand, the Andaman Islands, and Malesia in various ethnomedicinal applications. This study aims to provide a comprehensive phytochemical profile and HPTLC analysis of Chrozophora rottleri leaves to highlight its medicinal potential. The leaves were subjected to hot continuous extraction using a Soxhlet apparatus with solvents of increasing polarity to maximize the yield of bioactive compounds. Phytochemical screening identified the presence of alkaloids, flavonoids, tannins, saponins, glycosides, triterpenoids, phenols, steroids, coumarins, cardiac glycosides, and phytosterols in different extracts. Quantitative analysis revealed that alkaloids were most abundant in chloroform and ethanol extracts (500 mg/g), while flavonoids were prominent in ethyl acetate and ethanol extracts (200 mg/g). Phenols and tannins were concentrated in ethyl acetate and aqueous extracts (200 mg/g). The HPTLC analysis displayed distinct separation of phytochemical bands at varying retention factor (Rf) values, using toluene: ethyl acetate (9:1 v/v) at 254 nm and a combination of ethyl acetate: water: formic acid: glacial acetic acid (100:26:11:11 v/v/v/v) at 366 nm and 540 nm. Chromatograms revealed multiple peaks, signifying a rich diversity of bioactive compounds. These findings support the traditional uses of Chrozophora rottleri and underscore its potential for further pharmacological research. The research concluded that the leaf extract of Chrozophora rottleri is abundant in various phytochemicals, potentially contributing to its therapeutic properties. Further investigation into its individual phytochemical profile is recommended for isolating novel compounds. High-performance thin-layer chromatography (HPTLC) fingerprint analysis of Chrozophora rottleri leaf extract could serve as a reliable method for precise plant identification, acting as both a phytochemical marker and a valuable indicator of genetic variability within plant populations.

Robust deep learning method for fruit decay detection and plant identification: enhancing food security and quality control.

This paper presents a robust deep learning method for fruit decay detection and plant identification. By addressing the limitations of previous studies that primarily focused on model accuracy, our approach aims to provide a more comprehensive solution that considers the challenges of robustness and limited data scenarios. The proposed method achieves exceptional accuracy of 99.93%, surpassing established models. In addition to its exceptional accuracy, the proposed method highlights the significance of robustness and adaptability in limited data scenarios. The proposed model exhibits strong performance even under the challenging conditions, such as intense lighting variations and partial image obstructions. Extensive evaluations demonstrate its robust performance, generalization ability, and minimal misclassifications. The inclusion of Class Activation Maps enhances the model's capability to identify distinguishing features between fresh and rotten fruits. This research has significant implications for fruit quality control, economic loss reduction, and applications in agriculture, transportation, and scientific research. The proposed method serves as a valuable resource for fruit and plant-related industries. It offers precise adaptation to specific data, customization of the network architecture, and effective training even with limited data. Overall, this research contributes to fruit quality control, economic loss reduction, and waste minimization.

A Multi-Target Identification and Positioning System Method for Tomato Plants Based on VGG16-UNet Model

The axillary buds that grow between the main and lateral branches of tomato plants waste nutrients and lead to a decrease in yield, necessitating regular removal. Currently, these buds are removed manually, which requires substantial manpower and incurs high production costs, particularly on a large scale. Replacing manual labor with robots can lead to cost reduction. However, a critical challenge is the accurate multi-target identification of tomato plants and precise positioning for axillary bud removal. Therefore, this paper proposes a multi-target identification and localization method for tomato plants based on the VGG16-UNet model. The average intersection and pixel accuracies of the VGG16-UNet model after introducing the pretrained weights were 85.33% and 92.47%, respectively, which were 5.02% and 4.08% higher than those of the VGG16-UNet without pretrained weights, achieving the identification of main branches, side branches, and axillary bud regions. Then, based on the multi-objective segmentation of the tomato plants in the VGG16-UNet model, the regions of the axillary buds in the tomato plants were identified by HSV color space conversion and color threshold range selection. Morphological dilation and erosion operations were used to remove noise and connect adjacent regions of the same target. The endpoints and centroids of the axillary buds were identified using the feature point extraction algorithm. The left and right positions of the axillary buds were judged by the relationship between the position of the axillary bud centroid and the position of the main branch. Finally, the coordinate parameters of the axillary bud removal points were calculated using the feature points to determine the relationship between the position of the axillary bud and the position of the branch. Experimental results showed that the average accuracy of the axillary bud pruning point recognition was 85.5%.

DNA Barcoding as a Plant Identification Method

In the last two decades, plant taxonomy has bloomed, following the development of a novel technique, namely, DNA barcoding. DNA barcodes are standardized sequences, ideally unique, coding or non-coding, either from the genome of the organism or from its organelles, that are used to identify/classify an organismal group; in short, the method includes amplification of the DNA barcode, sequencing and comparison with a reference database containing the relevant sequences from different species. In plants, the use a universal DNA barcode, such as COI, which is used in animals, has not been achieved so far. This review provides a comprehensive overview of the progress made in DNA barcoding within the field of plant taxonomy. It highlights the success of various barcode loci, the emergence of super barcodes from the chloroplast genome, and the overall impact of next-generation sequencing technologies on the field. The discussion of different approaches reflects the ongoing efforts to refine and optimize DNA barcoding techniques for plants, contributing to the advancement in our understanding of plant biodiversity.

Development of a Genus Identification Method for Poisonous Plants Using Real-Time PCR

We have developed a rapid genus identification method for poisonous plants. The real-time PCR using the TaqMan® probe method was employed for detection, with the amplified targets being the "trnL (UAA)-intron" or "trnL-trnF intergenic spacer" regions of chloroplast DNA. The targeted plants were selected six genera (Aconitum, Colchicum, Veratrum, Brugmansia, Scopolia and Narcissus), which have been implicated in many instances of food poisoning in Japan. A tissue lysis solution was used for DNA extraction, which can be completed within approximate 30 min. A master mix corresponding to the tissue lysis solution was used for real-time PCR reagents. As a result, we were able to complete the entire process from DNA extraction to genus identification in 4 to 5 hr. The detection sensitivity was estimated at approximately 1 pg of DNA for all six plant genera. Remarkably, an amplification plot was discerned even with the crude cell lysates of all samples. It was also possible to obtain amplification curves for three plant samples that had been subjected to simulated cooking (boiling). This study suggests that the developed method can rapidly identify six genera of poisonous plants.

Technical report on the classification of plant pest detection and identification methods

Technical report on the classification of plant pest detection and identification methods

Identification for the species of aquatic higher plants in the Taihu Lake basin based on hyperspectral remote sensing.

Aquatic plants are crucial for aquatic ecosystems and their species and distribution reflect aquatic ecosystem health. Remote sensing technology has been used to monitor plant distributions over large scales. However, the fine identification of the species of aquatic higher plants is challenging due to large temporal-spatial changes in optical water body properties and small spectral differences among plant species. Here, an aquatic plant identification method was developed by constructing a decision tree file in the C4.5 algorithm based on the canopy spectra of eight plants in the Changguangxi Wetland water area from hyperspectral remote sensing technology. The method was used to monitor the distribution of different plants in the Changguangxi Wetland area and two other water areas. The results showed that the spectral characteristics of plants were enhanced by calculating their spectral index, thereby improving the comparability among different species. The total recognition accuracy of the constructed decision tree file for eight types of plants was 85.02%. Nymphaea tetragona, Pontederia cordata, and Nymphoides peltatum had the highest recognition accuracy and Eichhornia crassipes was the lowest. The specific species and distributions of aquatic plants were consistent with the water quality in the area. The results can provide a reference for the accurate identification of aquatic plants in the same type of water area.

In silico analysis of potential loci for the identification of Vanda spp. in the Philippines

Difficulties in identifying Vanda species are still encountered, and the ambiguity in its taxonomy is still unresolved. To date, the advancement in molecular genetics technology has given rise to the molecular method for plant identification and elucidation. One hundred twenty-five (125) gene sequences of Vanda species from the Philippines were obtained from the NCBI GenBank. Four of the 25 loci were further examined using MEGA 11 software for multiple sequence alignment, sequence analysis, and phylogenetic reconstruction. The indel-based and tree-based methods were combined to compute the species resolution. The result showed that ITS from the nuclear region obtained the highest species resolution with 66.67%. It was then followed by psbA-trnH, matK, and trnL-trnF from the chloroplast genome with a species resolution of 60%, 40%, and 30.77%, respectively. ITS and psbA-trnH satisfied the ideal length for DNA barcoding as they have 655 bp and 701 bp, respectively. The locus psbA-trnH was also considered to have a higher potential to discriminate Vanda species since only a few sequences were tested for ITS. Furthermore, ITS and trnL-trnF have the highest variable rate, which is 2.9%, while matK and psbA-trnH have 2% and 1.3%, respectively. This showed the nature of the unique sequences of various species. In this study, the indel-based method provided better results than the tree-based method. It will help support further DNA barcoding studies and strengthen the conservation and protection of Vanda spp. in the Philippines.

Plant leaf identification based on shape and convolutional features

Plant leaf identification is an important and challenging issue in botany and image analysis. This is because leaves of the same class exhibit very large differences, and leaves of different classes have very small changes. In this article, we propose a novel plant identification method by combining shape and convolutional characteristics. First, we present an effective shape descriptor named improved multiscale triangle descriptor (IMTD) to capture the shape properties of a leaf. Then, we analyze different levels of convolutional features for plant leaf identification. Finally, we combine complementary shape and convolutional features for the task of plant leaf recognition. The proposed method has been tested on nine benchmark leaf datasets, including the Flavia, CVIP100, Swedish, MEW2012, ICL Compound, LeafSnap, Mulberry, Corn leaf disease, and Turkey-Plant dataset. Our method achieves good recognition results on nine benchmark leaf datasets for the unsupervised leaf image retrieval and supervised leaf image classification. The recognition performance of our approach is better than prior state-of-the-art plant identification approaches.

A Method of Invasive Alien Plant Identification Based on Hyperspectral Images

Invasive alien plants (IAPs) are considered to be one of the greatest threats to global biodiversity and ecosystems. Timely and accurate detection technology is needed to identify these invasive plants, helping to mitigate the damage to farmland, fruit trees and woodland. Hyperspectral technology has the potential to identify similar species. However, the challenge remains to simultaneously identify multiple invasive alien plants with similar colors based on image data. The spectral images were collected by a hyperspectral camera with a spectral range of 450–998 nm, and the raw spectra were extracted by Cubert software. First derivative (FD), Savitzky-Golay (SG) smoothing and standard normal variate (SNV) were used to preprocess the raw spectral data, respectively. Then, on the basis of preprocessing, principal component analysis (PCA) and ant colony optimization (ACO) were used for feature dimensionality reduction, and the reduced features were used as input variables for later modeling. Finally, a combination of both dimensionality reduction and non-dimensionality reduction is used for identification using support vector machines (SVM) and random forests (RF). In order to determine the optimal recognition model, a total of 18 combinations of different preprocessing methods, dimensionality reduction methods and classifiers were tested. The results showed that a combination of SG smoothing and SVM achieved a total accuracy (A) of 89.36%, an average accuracy (AA) of 89.39% and an average precision (AP) of 89.54% with a test time of 0.2639 s. In contrast, the combination of SG smoothing, the ACO, and SVM resulted in weaker performance in terms of A (86.76%), AA (86.99%) and AP (87.22%), but with less test time (0.0567 s). The SG-SVM and SG-ACO-SVM models should be selected considering accuracy and time cost, respectively, for recognition of the seven IAPs and background in the wild.

Seedling maize counting method in complex backgrounds based on YOLOV5 and Kalman filter tracking algorithm.

Maize population density is one of the most essential factors in agricultural production systems and has a significant impact on maize yield and quality. Therefore, it is essential to estimate maize population density timely and accurately. In order to address the problems of the low efficiency of the manual counting method and the stability problem of traditional image processing methods in the field complex background environment, a deep-learning-based method for counting maize plants was proposed. Image datasets of the maize field were collected by a low-altitude UAV with a camera onboard firstly. Then a real-time detection model of maize plants was trained based on the object detection model YOLOV5. Finally, the tracking and counting method of maize plants was realized through Hungarian matching and Kalman filtering algorithms. The detection model developed in this study had an average precision mAP@0.5 of 90.66% on the test dataset, demonstrating the effectiveness of the SE-YOLOV5m model for maize plant detection. Application of the model to maize plant count trials showed that maize plant count results from test videos collected at multiple locations were highly correlated with manual count results (R2 = 0.92), illustrating the accuracy and validity of the counting method. Therefore, the maize plant identification and counting method proposed in this study can better achieve the detection and counting of maize plants in complex backgrounds and provides a research basis and theoretical basis for the rapid acquisition of maize plant population density.

Developing an Algorithm for Robotic Precision Application of Crop Protection Products

The existing range of plant identification methods and tools is considered limited in real agrotechnical tasks. The image parameters tend to differ significantly in applied solutions. (Research purpose) To develop an algorithm for crop plant recognition by a robotic device using a state-of-the-art convolutional neural network (R-CNN) and deep learning technology. (Materials and methods) A robotic device has been developed for variable rate application of plant protection products able to recognize both useful crops and weeds, determine the area of processing, namely the coordinates of the processing center and the processing radius. Mask R-CNN and Deeplabv3 plus segmenting neural networks were chosen for crop (white head cabbage) detection. The network-based algorithm detects, segments, and positions plants based on a dataset collected in the image-mask and COCO dataset formats. The data set was formed by aerial photography using an unmanned aircraft. The original images are taken by Xiaovv HD Web USB 150 degree Full HD 1080P webcam and Logitech C270 HD 720p webcam. The trained neural network for the robotic device was installed on the Nvidia Jetson AGX Xavier platform. (Results and discussion) As a result of assessing the accuracy of the model on the test data, the following values were obtained: the number of plants detected is 98 percent, the accuracy of contour detection is 94 percent. (Conclusions) It is proved that the trained neural network can be applied to any cultivated crops, taking into account the heterogeneity of their location in the field, soil types, and the percentage of weeds. As a result, the model is trained to extract the bounding box coordinates and the object (cabbage) location by pixels with the required accuracy for both synthetic and real data.

Use of rbcL and ITS2 for DNA barcoding and identification of Solanaceae plants in hilly state of Mizoram, India

Solanaceae is an important angiosperm family having high economic and commercial importance being used as sources of food, spice, and medicine. The traditional method of plant identification using morphological characters often leads to inaccurate and unreliable results because of genetic and environmental factors. In Solanaceae family, highly similar morphological traits among the species also make the identification difficult. The present study evaluated the viability of employing DNA barcodes, ITS2 and rbcL regions to identify Solanaceae species. DNA barcoding is a rapid and reliable tool to identify and study the genetic relationship among plants and animals. The present study for the first time was conducted during 2017 to 2019 at the Department of Botany, Mizoram University to analyze the applicability of two DNA regions-rbcL (Ribulosebisphosphate Carboxylases) and ITS2 (Internal Transcribed Spacer gene), as the barcodes for the identification of Solanaceae species of Mizoram, India. Fifteen Solanaceae species were analyzed at ITS2 and rbcL regions. The success rates of PCR amplification and sequencing of ITS2 and rbcL were 100% and MEGA 7.0 was used to align the sequences and to compute genetic distances. The results from BLAST and nearest-distance methods revealed that ITS2 and rbcL can be used to identify all the studied species and high divergences at taxonomic levels were also found using K2P (Kimura 2-parameter) model. Hence, our study reveals that rbcL and ITS2 regions are suitable DNA barcodes for the identification of Solanaceae species and therefore can be successfully used to monitor the adulteration of plants.

An Editing-Site-Specific PCR Method for Detection and Quantification of CAO1-Edited Rice.

Genome-edited plants created by genome editing technology have been approved for commercialization. Due to molecular characteristics that differ from classic genetically modified organisms (GMOs), establishing regulation-compliant analytical methods for identification and quantification of genome-edited plants has always been regarded as a challenging task. An editing-site-specific PCR method was developed based on the unique edited sequence in CAO1-edited rice plants. Test results of seven primer/probe sets indicated that this method can identify specific CAO1-edited rice from other CAO1-edited rice and wild types of rice with high specificity and sensitivity. The use of LNA (locked nucleic acid) in a probe can efficiently increase the specificity of the editing-site-specific PCR method at increased annealing temperature which can eliminate non-specific amplification of the non-target. The genome-edited ingredient content in blinded samples at the level of 0.1% to 5.0% was accurately quantified by this method on the ddPCR platform with RSD of <15% and bias in the range of ±17%, meeting the performance requirements for GMO detection method. The developed editing-site-specific PCR method presents a promising detection and quantification technique for genome-edited plants with known edited sequence.

Методичні підходи щодо ідентифікації рослин на знімках високого розрізнення за мультиспектрального моніторингу за допомогою БПЛА

Crop management used in these technologies is one of the main trends in the modernization of agricultural technologies. To implement crop management, growers need accessible and effective information about the state of crops. The aim of the work is to develop a method of plant identification on multispectral images of high resolution for crops of continuous sowing on the example of winter wheat. The research was conducted on 03/17/2019 on winter wheat crops in the tillering vegetation phase, Mukan variety in production fields near the village of Horodyshche, Kyiv region. Aerial monitoring from a height of 100 meters was carried out using a spectral complex Slantrange 3p, mounted on a UAV UAV DJI Matrice 600. To extract the reference graphics data from Slantview made a copy of the screen in full screen mode of the image window. Statistical processing of graphical data of spectral monitoring results was performed in MathCad. It was found that the reliable establishment of the spectral portrait of the soil for its pixel-by-pixel filtering from multispectral images is a difficult task because its color significantly depends on the state of moisture, which may differ in open and shaded by plants. A more promising way to eliminate random inclusions is to use a spectral portrait of plants based on the intensity ratios of its spectral components. A promising parameter for assessing the condition of crops is to assess their area of heir horizontal surface, which can be determined by pixel analysis of the image. A filtering option is proposed, which, as in the solutions implemented in Slantview software, needs to be debugged. In further researches it is expedient to consider questions of methodical maintenance of an estimation of quality of a filtration of data of spectral monitoring of vegetation.

Detecting Ingested Host Plant DNA in Potato Leafhopper (Hemiptera: Cicadellidae): Potential Use of Molecular Markers for Gut Content Analysis.

Detection of host plant DNA from sap-feeding insects can be challenging due to potential low concentration of ingested plant DNA. Although a few previous studies have demonstrated the possibility of detecting various fragments of plant DNA from some sap-feeders, there are no protocols available for potato leafhopper, Empoasca fabae (Harris) (Hemiptera: Cicadellidae), a significant agricultural pest. In this study we focused on optimizing a DNA-based method for host plant identification of E. fabae and investigating the longevity of the ingested plant DNA as one of the potential applications of the protocol. We largely utilized and modified our previously developed PCR-based method for detecting host plant DNA from grasshopper and the spotted lanternfly gut contents. We have demonstrated that the trnL (UAA) gene can be successfully utilized for detecting ingested host plant DNA from E. fabae and determining plant DNA longevity. The developed protocol is a relatively quick and low-cost method for detecting plant DNA from E. fabae. It has a number of important applications-from determining host plants and dispersal of E. fabae to developing effective pest management strategies.

A novel approach for unsupervised image segmentation fusion of plant leaves based on G-mutual information

Plant leaf segmentation has a very important role in most plant identification methods. Tree leaves segmentation in images with complex background is very difficult when there is no prior information about the leaves and backgrounds. In practice, the parameters of unsupervised image segmentation algorithms must be set for each image to get the best results. In this paper, to overcome this problem, fusion of the results of five leaf segmentation algorithms (fuzzy c-means, SOM and k-means in various color spaces or different parameters) is applied. To fuse the results of these segmentations, new equations for mutual information (g-mutual information equations) based on the g-calculus are introduced to find the best consensus segmentation. The results of the mentioned primary clustering algorithms are considered as a new feature vector for each pixel. To reduce the time complexity, a fast method is employed using truth table containing different feature vectors. To evaluate this new approach, a leaf image database with natural scenes, taken from Pl@ntLeaves database, is generated to have different positions and orientations. In addition, a widely used database is used to compare the proposed method with other methods. The experimental results presented in this paper show that the use of g-calculus in fusion of image segmentations improves the evaluation parameters.

Plant Classification in Images of Natural Scenes Using Segmentations Fusion

This paper presents a novel approach to automatic classifying and identifying of tree leaves using image segmentation fusion. With the development of mobile devices and remote access, automatic plant identification in images taken in natural scenes has received much attention. Image segmentation plays a key role in most plant identification methods, especially in complex background images. Where there are no presumptions about leaf and background, segmentation of leaves in images with complex background is very difficult. In addition, each image has special conditions, so parameters of the algorithm must be set for each image. In this paper, image segmentation fusion is used to overcome this problem. A fast method based on maximum mutual information is used to fuse the results of leaf segmentation algorithms with different parameters. Applying Tsallis entropy and g-calculus, generalized mutual information equations are derived and used to obtain the best consensus segmentation. To evaluate the proposed methods, a dataset with tree leaf images in natural scenes and complex backgrounds is used. These images were taken from Pl@ntLeaves dataset and modified so that they do not have a presumption. The experimental results show the use of Tsallis entropy and g-calculus in image segmentation fusion, improves plant species identification.

Eflorapantnagar: Database of Local Flora of Pantnagar

Image comparison is a convenient and quick method of plant identification, however, essentially requires databases of authentically identified images of plants. Eflorapantnagar is an open online database of flowering plants of G.B. Pant University Pantnagar campus area containing identified images of 428 flowering plant species belonging to 308 genera under 88 families, 36 orders and 13 informal groups. This virtual flora may help in identification of local flora being a user friendly tool, thus initially attracting more students in plant taxonomy.

An overview on Taxonomic Keys and Automated Species Identification (ASI)

A core goal of taxonomy and systematics, entomology, field botany, horticulture, zoology and many agriculture courses involves learning to identify plants and animals. But current syllabi and time constraint allow students to see only a limited amount of taxonomic variability. Usually only experts such as taxonomists and trained technicians can identify taxa accurately because it requires special skills acquired through extensive experience. Taxonomic keys are essential tools for species identification, used by students and professionals. The development of computer-based, multi-media keys provides one means of addressing this critical identification and diagnostic function. Automated species identification (ASI) is a method of making the expertise of taxonomists available to ecologists, parataxonomists and others via digital technology and artificial intelligence. Today, most automated identification systems rely on images depicting the species for the identification. Although computer programs will not replace classical plant identification methods, they have the potential to make these methods more effective.