Plant Identification

At the University of Aveiro, a multimedia tool was developed to help the teaching of botany [1], [2]. It includes a Dichotomous Interactive Key (DIK), for 390 taxa of vascular plants occurring at "Ria de Aveiro" lagoon system. This key is linked to two different glossaries, one descriptive, the other illustrated. At the end of the identification process, the student is conducted to a webpage including photographs, descriptions and other relevant information about the taxa. The students considered it "very useful", and the successful identifications increased in the assessment tests in the two years after the introduction of the DIK. This seem to be an effective tool in the teaching of botany, namely in plant identification at secondary and post-secondary level.

— At the University of Aveiro, a multimedia tool was developed to help the teaching of botany [1], [2]. It includes a Dichotomous Interactive Key (DIK), for 390 taxa of vascular plants occurring at “Ria de Aveiro” lagoon system. This key is linked to two different glossaries, ...

Modern scientific and technological advances allow botanists to use computer vision-based approaches for plant identification tasks. These approaches have their own challenges. Leaf classification is a computer-vision task performed for the automated identification of plant species, a serious challenge due to variations in leaf morphology, including its size, texture, shape, and venation. Researchers have recently become more inclined toward deep learning-based methods rather than conventional feature-based methods due to the popularity and successful implementation of deep learning methods in image analysis, object recognition, and speech recognition. In this paper, to have an interpretable and reliable system, a botanist's behavior is modeled in leaf identification by proposing a highly-efficient method of maximum behavioral resemblance developed through three deep learning-based models. Different layers of the three models are visualized to ensure that the botanist's behavior is modeled accurately. The first and second models are designed from scratch. Regarding the third model, the pre-trained architecture MobileNetV2 is employed along with the transfer-learning technique. The proposed method is evaluated on two well-known datasets: Flavia and MalayaKew. According to a comparative analysis, the suggested approach is more accurate than hand-crafted feature extraction methods and other deep learning techniques in terms of 99.67% and 99.81% accuracy. Unlike conventional techniques that have their own specific complexities and depend on datasets, the proposed method requires no hand-crafted feature extraction. Also, it increases accuracy as compared with other deep learning techniques. Moreover, SWP-LeafNET is distributable and considerably faster than other methods because of using shallower models with fewer parameters asynchronously.

Ayurveda is one of the oldest holistic treatment systems in the world. Finding an accurate and correct plant is the key to the working of Ayurvedic treatment. Identification of medicinal plants is a tedious job due to the look-alike feature and availability issue of medicinal plants with other plants. This paper emphases on the ideal identification and classification of plants of medicinal use using deep learning approaches. Previously researchers have used traditional machine learning techniques to identify medicinal plants, which lead to mixed results. Such results are good but not enough as the identification of medicinal plants may lead to a worsening situation for patients. This research is conducted to get results closer to an ayurvedic expert. The dataset used for this research has been taken from Mendeley Data. The dataset comprises 30 different species of medicinal plants. Hybrid transfer learning has been applied to this dataset. The model has generated a test accuracy of ...

Plant identification is most often based on visual observations by botanists and systematists. Deep learning has become a tool that provides an alternative to automatic plant identification. Our study consists in implementing a method for plant recognition from herbarium specimens using deep learning classification methods. These methods were evaluated on the dataset of ten plant families from the national herbarium of Côte d'Ivoire. The proposed work uses CNN architectures such as DensNet-121, InceptionV3, VGG19, MobileNet, and ResNet101. The dataset contains 7543 images of herbarium specimens. The database is structured in three parts: training, testing, and validation. The accuracies obtained for the first scenario without preprocessing of herbarium specimen images are 76.94% for MobileNet, 77.77% for VGG19, and 77.96% for InceptionV3, 80.41% for ResNet101, and 83.47% for DensNet-121, respectively. The best performance was obtained with DensNet-121 with 83.47%. In the second scenario with preprocessing of herbarium specimens, the accuracies obtained were 82.80% for InceptionV3, 84.40% for VGG19, 85.53% for MobileNet, and 85.80% for ResNet101. The best accuracy was obtained with ResNet121 with 85.80%. From the analysis obtained, the results show that ResNet101 gives the best accuracy compared to the other architectures. In particular, the data preprocessing improves the prediction results, of the Convolutional Neural Network algorithms.

Plant identification based on leaf shape is becoming a popular trend, since each leaf carries substantial information that can be used to identify plant. This is difficult because the features of a leaf shape can be influenced by other leaves that have similar features but different categories. This paper presents the most popular statistical operators: mean filtering technique (MFT), median filtering technique (MDFT), Wiener filtering technique (WFT), rank order filtering technique (ROFT) and adaptive two-pass rank order filtering technique (ATRFT) for enhancing preprocessing stage. The performance of these techniques was evaluated using mean square error (MSE) and peak signal to noise ratio (PSNR). Ten features were extracted from the pre-processed leaf images and identification performance was also evaluated using precision and recall. It is found that WFT is the best filtering technique and gives the best identification accuracy of 95.1%.

Identification of right medicinal plants that goes in to the formation of a medicine is significant in ayurvedic medicinal industry. This paper focuses around the automatic identification proof of therapeutic plants that are regularly utilized in Ayurveda. The fundamental highlights required to distinguish a medicinal plant is its leaf shape, color and texture. In this paper, we propose efficient accurate classifier for ayurvedic medical plant identification (EAC-AMP) utilizing using hybrid optimal machine learning techniques. In EAC-AMP, image corners detect first and top, bottom leaf edges are computed by the improved edge detection algorithm. After preprocessing, the segmentation can achieve using spider optimization neural network (SONN), which segments leaf regions from an image. The time and frequency domain features are computed by the symbolic accurate approximation (SAX); other features shape features, color features and tooth features are computed by the two-dimensional bi...

Plant identification plays a crucial role in sustaining the balance of the environment and protecting the biodiversity of a region. Recognizing different species of plants using conventional methods for conservation purposes is a tedious task. Today there is a cumulative effort made by computer scientists and botanists to automate the entire process of plant identification with leaf being a key feature for distinguishing different species of plants. With the advancement and utilization of relevant technologies like digital cameras, mobile cameras, newer techniques in image processing, pattern recognition, machine learning, automation of this system has been a reality. In this paper we have reviewed the current status of research on computer vision methodologies for taxonomical identification of plants and have also focused on the research challenges such as the diversity of the taxa to be identified, morphological variation in plants belonging to the same species, smaller interspecies variations, the challenges in acquisition of high quality images and standard datasets. The future trends in use of new technologies, creation of standard databases and interdisciplinary aspect of research is also discussed.

Plant identification plays a crucial role in sustaining the balance of the environment and protecting the biodiversity of a region. Recognizing different species of plants using conventional methods for conservation purposes is a tedious task. Today there is a cumulative effort made by computer scientists and botanists to automate the entire process of plant identification with leaf being a key feature for distinguishing different species of plants. With the advancement and utilization of relevant technologies like digital cameras, mobile cameras, newer techniques in image processing, pattern recognition, machine learning, automation of this system has been a reality. In this paper we have reviewed the current status of research on computer vision methodologies for taxonomical identification of plants and have also focused on the research challenges such as the diversity of the taxa to be identified, morphological variation in plants belonging to the same species, smaller interspecies variations, the challenges in acquisition of high quality images and standard datasets. The future trends in use of new technologies, creation of standard databases and interdisciplinary aspect of research is also discussed.

Multiword Terms (MWTs) are domain-specific Multiword Expressions (MWE) where two or more lexemes converge to form a new unit of meaning . The task of processing MWTs is crucial in many Natural Language Processing (NLP) applications, including Machine Translation (MT) and terminology extraction. However, the automatic detection of those terms is a difficult task and more research is still required to give more insightful and useful results in this field. In this study, we seek to fill this gap by using state-of-the-art transformer models. We evaluate both BERT (Devlin et al., 2019) like discriminative transformer models and generative pre-trained transformer (GPT) models on this task, and we show that discriminative models perform better than current GPT models in the identification of multiword flower and plant names for both English and Spanish. Best discriminative models perform with 94.3, 82.1 F1 scores in English and Spanish data, respectively, while ChatGPT could only return 63.3 and 47.7 F1 scores, respectively.

Venation network analysis stands as a promising direction of scientific research, providing new insights into the origins and influence of plant phenotypic traits. However, its applicability is limited by a lack of tools that would facilitate relevant data acquisition. Dicotyledons form complex reticulate networks, often elusive for regular scanning equipment, hindering the attempts to capture details of their anastomoses arrangement. Currently available professional solutions operate on high-resolution noise-free images obtained in a complex process of chemical clearing, sample staining, and computationally expensive digitizing. This work introduces a novel technique capable of detecting leaf vasculature on pixel level and extracting a graph representation of its structure while operating on lower resolution scans of unprocessed specimens. The proposed transformation pipeline is designed as an array of steps-featuring automatic leaf segmentation, machine learning-based vein recognition, a sequence of custom spatial and morphological filters, segment radii retrieval and, finally, a graph compression and denoising algorithm. Each of those stages was separately evaluated using a range of metrics, including a new one aimed at assessing the uniformity of the reconstructed network. Obtained results confirmed that the method performs well in terms of both qualitative and quantitative analysis, given the characteristic imperfections in the examined images.

Agriculture is the most important sector in developing countries, so the main source of concern for farmers is plant diseases that lead to a lack of production and a waste of money and crops. In this paper, a system using computerassisted convolutional neural networks (CNN) with camera is developed to characterize diseases of citrus trees. This proposed system can help farmers to increase and improve the quality of their agricultural productivity. In addition to reducing the spread of the disease through early detection. Citrus leaf dataset was created to train and test the model because citrus is one of the main crops in Iraq. The results of the experiment shown that the implemented CNN achieved high classification accuracy of (92%) with fewer parameters, making it flawless and promising outcomes.

Non-wood forest products (NWFPs) provide important source of income for millions of households worldwide especially for those in rural areas. Especially nonwood plants are main source of income for rural people in many parts of the world. These plants are also consumed by local people for health, nutrition and many other needs. In order to ensure benefits of NWFPs, it is important to identify nonwood plants and determine their population distribution. However, plant identification by using manual method is a complex task to fulfill especially in varying conditions in the field. The leaves are often used to identify plant species based on their visible characteristics. Image recognition systems have been developed to identify plant species by using leaf image. Comparing manual plant identification systems, image recognition systems are easier and, in most cases, more accurate alternatives. In recent years, some mobile applications have been developed to assist people to identify plants by taking pictures of their leaves using smart phones. In this study, it was aimed to investigate the capabilities of the most common mobile image recognition systems in identification of plant species based on leaf images.

Nigeria is an agricultural nation, with majority of its citizenry predominantly relied on it for survival. Recently, the sector is receiving a lot of attention due to the need to diversify and a bit away from an oil-driven economy. The sector is one among the most contributing to the nations` Gross Domestic Product (GDP), recording 21% in the previous year. However, one of the causes of low crop yield is diseases caused by agents such as fungi, bacteria, and viruses. The advent of technology has led to its influence in the agricultural sector. The recent evolution and fusion of IOT, ML and Data Analytics has brought succour for especially plant monitoring and management. This research work investigated the capability of IOT, ML and DA in tomato plant disease classification in Yauri Emirate, Northwestern Nigeria. We further conduct a survey with a view to understanding the knowledge, acceptability or otherwise of the mentioned techniques. The result of our classification using CNN, a DL model achieves a near-optimal accuracy of 98.6% with a loss of 0.03% while recording over 98.3 % for both precision and recall on the predicted labels. We also observed that our target audience for the survey lacks near total knowledge of smart farming, hence the need for the stakeholders in the domain to embark on sensitization and awareness towards reaping its numerous advantages.

In recent studies, many researchers have tested different kind of techniques focusing on flowers, barks and leaf for plant recognition such as extraction of shape factors and comparison of color histogram. Both have provided good results but has many restrictions. This study on leaf shape recognition using Fourier descriptors and Hough transform with probabilistic neural network has shown great potential as an improvement for recognition of plant species. It is a combination between shape factors, Fourier descriptors, and vein features using Hough transform. All image processing techniques are done using MATLAB. The hardware implementation emulated wireless connection between a thin client and a server with the help of Mono, OpenCV, and EmguCV. Hardware peripherals are composed of a miniature studio, Raspberry Pi processor as well as its Pi Cam and LCD modules. The mini studio was used to provide clean background and correct lighting. Results show an accuracy of 87 percent on Flavia dataset, and 99 percent on the local dataset.

Since several active pharmaceutical ingredients are sourced from medicinal plants, identifying and classifying these plants are generally a valuable and essential task during the drug manufacturing process. For many years, identifying and classifying those plants have been exclusively done by experts in the domain, such as botanists and herbarium curators. Recently, powerful computer vision technologies, using deep learning or deep artificial neural networks, have been developed for classifying or identifying objects using images. A convolutional neural network is a deep learning architecture that outperforms previous state-of-the-art approaches in image classification and object detection based on its efficient feature extraction of images. This study investigated several pre-trained convolutional neural networks for identifying and classifying leaves of three species of the genus Brachylaena. The three species considered were Brachylaena discolor, Brachylaena ilicifolia, and Brachylaena elliptica. All three species are used medicinally by people in South Africa. We trained and evaluated different deep convolutional neural networks from 1259 labeled images of those plant species (at least 400 for each species) split into training, evaluation, and test sets. The best model provided a 98.26% accuracy using cross-validation with a confidence interval of ±2.16%.

In most of classic plant identification methods a dichotomous or multi-access key is used to compare characteristics of leaves. Some questions about if the analyzed leaves are lobed, unlobed, simple or compound need to be answered to identify plants successfully. However, very little attention has been paid to make an automatic distinction of leaves using such features. In this paper we first explore if incorporating prior knowledge about leaves (categorizing between lobed simple leaves, and the unlobed simple ones) has an effect on the performance of six classification methods. According to the results of experiments with more than 1,900 images of leaves from Flavia data set, we found that it is statically significant the relationship between such categorization and the improvement of the performances of the classifiers tested. Therefore, we propose two novel methods to automatically differentiate between lobed simple leaves, and the unlobed simple ones. The proposals are invariant to rotation, and achieve correct prediction rates greater than 98%.

Automatic plant identification has been an important issue in the last years. Most of the state-of-the-art methods for this purpose use leaf features to predict the species. Despite there are many methods to extract different leaf features, just few of them are focused on discriminating between simple and compound leaves. In this work, we introduce a method to detect compound leaves. Our method uses concentric circles to explore the surface of the leaf to count the changes of color in binary images, then, the changes are analyzed to detect compound leaves. The method predicts correctly more than 96% of the leaves in the Flavia data set. We also tested the method with some images of leaves available on the Internet, with 100% of correctness. The information on whether a leaf is or not compound, was used on experiments to observe whether this improves the performance of classifiers.

Automatic digital plant classification and retrieval can be achieved by extracting features from its leaves. There are various opportunities to improve plant species identification due to computerization through the designing of a convenient automatic plant recognition system. Many different approaches consist of some major parts. First, images of leaf are acquired with digital camera or scanners. Then the user can selects the base point of the leaf and a few reference points on the leaf blades or done this automatically. Then several morphological features are extracted. These features are used as inputs to the classifier system for discrimination as probabilistic neural network. The network was trained with leaves from different plant species. Then the recognition accuracy of the proposed method has been tested. The method works only for the plants with broad flat leaves which are more or less two dimensional in nature. This paper presented various effective algorithms used for plant classification using leaf images and review the main computational, morphological and image processing methods that have been used in recent years and we conclude with a discussion of ongoing work and outstanding problems in the area.

Introduction: Methods used by Kenyan herbalists to identify plants and preserve herbal drugs are unclear. Objective: To assess the accuracy of plant identification, microbial and heavy metal contamination in hypoglycemic herbal preparations. Method: Four herbalists were identified by purposeful sampling and key informant interviews were carried out. Ethnobotanical walks were used to collect herbs and a botanist checked the accuracy of scientific names. Herbalists were asked to submit formulations. Microbial contamination was evaluated using selective and non-selective cultural media. Levels of heavy metals were evaluated by atomic absorption. Analysis: Content thematic approach was used to analyze key informant interviews. Degree of agreement between the names assigned by the herbalists and botanists was measured using percentage. Results: Plant identification relied heavily on macroscopic qualities aided by the plant’s geographical location. Both indigenous and botanical names were...

Multiword Terms (MWTs) are domain-specific Multiword Expressions (MWE) where two or more lexemes converge to form a new unit of meaning . The task of processing MWTs is crucial in many Natural Language Processing (NLP) applications, including Machine Translation (MT) and terminology extraction. However, the automatic detection of those terms is a difficult task and more research is still required to give more insightful and useful results in this field. In this study, we seek to fill this gap by using state-of-the-art transformer models. We evaluate both BERT (Devlin et al., 2019) like discriminative transformer models and generative pre-trained transformer (GPT) models on this task, and we show that discriminative models perform better than current GPT models in the identification of multiword flower and plant names for both English and Spanish. Best discriminative models perform with 94.3, 82.1 F1 scores in English and Spanish data, respectively, while ChatGPT could only return 63.3 and 47.7 F1 scores, respectively.

The impact of rice plant diseases has led to a 37% annual drop in rice production. It may happen basically due to the lack of knowledge in identifying and controlling rice plant diseases, but still there isn't any proper application has been developed which is capable enough to identify these rice plant diseases accurately and control those diseases. In order to identify rice plant disease by an application itself, Convolutional Neural Networks (CNN) can be used. Many of researchers have used CNNs for plant disease identification because of their accuracy in image identification and classification. But, there's still a handful researches have been conducted regarding the identification of rice plant diseases. This study provides a comprehensive understanding of current rice plant illnesses as well as the Deep Learning approaches used to detect such diseases. It also analyzes several kinds of techniques that have been employed in the literature by analyzing all of them with their benefits and drawbacks. It has discovered the most accurate ways in all levels of the image identification procedure as a consequence of this research, that will be valuable in recognizing rice plant illnesses.

In this study, four plant identification mobile apps, namely LeafSnap, PictureThis, Pl@ntNet, and PlantSnap, were tried by volunteer participants who are students, teachers, and plant enthusiasts. The participants were asked to take images of at least four plants and upload them to these apps for their identification. The participants were then asked about their impressions of and difficulties with the use of these mobile apps by a questionnaire. Their responses reveal that they found the use of mobile apps interesting, enjoyable, and very useful. It could add to their knowledge of plants, help them connect with nature and the world, and provide data for research purposes. There were, however, difficulties that they encountered with the use of the mobile apps, such as the slow Internet connection, some inconsistencies in identification, and the need to purchase the apps after the trial period. Despite these difficulties, the satisfaction of the users supports the need to explore the idea of using mobile apps in teaching taxonomy or systematic biology among students or even as citizen's science tools.

This paper is highlights plant leaf classification with the help of leaf image and its methods which can be used for plant leaf classification. A classification problem deals with associating a given input pattern with one of the distinct classes. Plant leaf classification is a technique, where leaf is classified based on its different morphological features. There are various successful classification methods like k-Nearest Neighbor Classifier, Probabilistic Neural Network and Genetic algorithm. Selection of an appropriate method for classification is often a difficult task because the quality of the results can be different for different input data. Plant leaf classifications have wide applications in various fields such as botany, Ayurveda, Agriculture etc.

Automatic classification of the plants is growing area of association with computer science and Botany, it has attracted many researchers to subsidize plant classification using image processing and machine learning techniques. Plants can be classified using number of traits such as leaf color, flowers, leaves, roots, leaf shape, leaf size etc. highly depends upon feature selection methods. However extraction of features from selected trait is most significant state in classification. State-of-the-art classification can be achieved by using leaf characteristics such as leaf venation patterns, leaf spectral signatures, leaf color, leaf shape, etc. This paper describes multimodal plant classification system using leaf venation patterns and its spectral signatures as a significant features. This paper shows that the feature fusion can be used to achieve efficient plant identification. The accuracy of identification for leaf spectral data, leaf venation features and HOG features is validated, it signifies that feature fusion technique performs better than that of non-imaging spectral signatures features only, with recognition result of 98.03% GAR and 93.51% GAR, respectively.

Automatic classification of the plants is growing area of association with computer science and Botany, it has attracted many researchers to subsidize plant classification using image processing and machine learning techniques. Plants can be classified using number of traits such as leaf color, flowers, leaves, roots, leaf shape, leaf size etc. highly depends upon feature selection methods. However extraction of features from selected trait is most significant state in classification. State-of-the-art classification can be achieved by using leaf characteristics such as leaf venation patterns, leaf spectral signatures, leaf color, leaf shape, etc. This paper describes multimodal plant classification system using leaf venation patterns and its spectral signatures as a significant features. This paper shows that the feature fusion can be used to achieve efficient plant identification. The accuracy of identification for leaf spectral data, leaf venation features and HOG features is validated, it signifies that feature fusion technique performs better than that of non-imaging spectral signatures features only, with recognition result of 98.03% GAR and 93.51% GAR, respectively.

This paper describes the participation of Inria within the Pl@ntNet project 7 at the LifeCLEF2014 plant identification task. The aim of the task was to produce a list of relevant species for each plant observation in a test dataset according to a training dataset. Each plant observation contains several annotated pictures with organ/view tags: Flower, Leaf, Fruit, Stem, Branch, Entire, Scan (exclusively of leaf). Our system treated independently each category of organ/view and then a late hierarchical fusion is used in order to combine the results on visual content analysis from the most local level analysis in pictures to the highest level related to a plant observation. For the photographs of flowers, leaves, fruits, stems, branches and entire views of plants, a large scale matching approach of local features extracted using different spatial constraints is used. For scans, the method combines the large scale matching approach with shape descriptors and geometric parameters on shape boundary. Then, several fusion methods are experimented through the four submitted runs in order to combine hierarchically the local responses to the final response at the plant observation level. The four submitted runs obtained good results and got the 4th to the 7th place over 27 submitted runs by 10 participating teams.

The ImageCLEFs plant identification task provides a testbed for a system-oriented evaluation of plant identification about 250 species trees and herbaceous plants based on detailed views of leaves, flowers, fruits, stems and bark or some entire views of the plants. Two types of image content are considered: SheetAsBackgroud which contains only leaves in a front of a generally white uniform background, and NaturalBackground which contains the 5 kinds of detailed views with unconstrained conditions, directly photographed on the plant. The main originality of this data is that it was specifically built through a citizen sciences initiative conducted by Tela Botanica, a French social network of amateur and expert botanists. This makes the task closer to the conditions of a realworld application. This overview presents more precisely the resources and assessments of task, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation results. With a total of twelve groups from nine countries and with a total of thirty three runs submitted, involving distinct and original methods, this third year task confirms Image Retrieval community interest for biodiversity and botany, and highlights further challenging studies in plant identification.

This paper proposes an automated system for recognizing disease conditions of human skin in context to health informatics. The disease conditions are recognized by analyzing skin texture images using a set of normalized symmetrical Grey Level Co-occurrence Matrices (GLCM). GLCM defines the probability of grey level i occurring in the neighborhood of another grey level j at a distance d in direction θ. Directional GLCMs are computed along four directions: horizontal (θ = 0º), vertical (θ = 90º), right diagonal (θ = 45º) and left diagonal (θ= 135º), and a set of features computed from each, are averaged to provide an estimation of the texture class. The system is tested using 180 images pertaining to three dermatological skin conditions viz. Dermatitis, Eczema, Urticaria. An accuracy of 96.6% is obtained using a multilayer perceptron (MLP) as a classifier.

Forest plant identification and classification play a pivotal role in various domains, encompassing biodiversity conservation, agricultural advancement, and beyond. Conventional plant identification methods often rely on expert botanists or manual identification approaches, which can be time-consuming and subjective. Deep learning models have emerged as a promising approach to automatically classify plants, offering high accuracy and efficiency. However, these models often rely on convolutional neural networks (CNNs) and their variants to extract features, which may fail to capture the complex relationships among plant characteristics. This paper proposes a novel feature extraction method using semi-supervised learning techniques combined with Masked Autoencoder architecture to enhance the feature extraction of plant data, applicable to problems with limited datasets. The proposed model, named MAE SGD, achieves an accuracy of nearly 94% on the QuangNam-ForestPlant-a dataset collected by our research team in Quang Nam province, Central Vietnam, comprising 24,314 images of 710 different forest plant species. Future research directions will focus on expanding the forest plant dataset and improving the recognition model to increase the model's accuracy and overall performance in identifying forest vegetation.

Deep learning architectures suffer from depth-related performance degradation, limiting the effective depth of neural networks. Approaches like ResNet are able to mitigate this, but they do not completely eliminate the problem. We introduce Globally Connected Neural Networks (GloNet), a novel architecture overcoming depth-related issues, designed to be superimposed on any model, enhancing its depth without increasing complexity or reducing performance. With GloNet, the network's head uniformly receives information from all parts of the network, regardless of their level of abstraction. This enables GloNet to self-regulate information flow during training, reducing the influence of less effective deeper layers, and allowing for stable training irrespective of network depth. This paper details GloNet's design, its theoretical basis, and a comparison with existing similar architectures. Experiments show GloNet's self-regulation ability and resilience to depth-related learning challenges, like performance degradation. Our findings suggest GloNet as a strong alternative to traditional architectures like ResNets.

This thesis is dedication to my mother, Mahnaz Borhanihaghigh, and my husband, Alireza Jahandideh. Thank you for your sacrifice, support, and understanding. Declaration I hereby declare that except where specific reference is made to the work of others, the contents of this dissertation are original and have not been submitted in whole or in part for consideration for any other degree or qualification in this, or any other university. This dissertation is my own work and contains nothing which is the outcome of work done in collaboration with others, except as specified in the text and Acknowledgements. This dissertation contains fewer than 65,000 words including appendices, bibliography, footnotes, tables and equations and has fewer than 150 figures.

Many information systems claim to be "green", meaning in support of environmental sustainability. But at closer look we find that these claims are often unsubstantiated; in other words, many green systems are not making any environment more sustainable. We identify three main root causes. First, the 'environment' is often ill-defined. Second, systems often overlook that 'sustainability' is a targeted function dependent on the goals of some stakeholders, which may include designers, users, organizations, policy makers, society or the planet as a whole. Third, we find that research on green information systems often overlooks conceptualizations such as ecology, environment or sustainability that originate in the sciences of the system, i.e., the basis on which information systems are built. To address these issues we present eight new design principles unique to the development of Green Information Systems that can act as prescriptive coherent design theory for developing information systems that improve environmental sustainability.

Phjac chac is considered as a native plant discovered in Cao Bang province. This plant holds valuable flavoring agents and potential essential oils and is widely used in relieving common cold symptoms in traditional local communities. Despite its significant uses, there has been a scarcity of information to identify this plant species. Therefore, the study aimed to identify this plant using morphological characteristics and trnL– trnF markers. The young stems of this plant display a green and smooth appearance, while the older branches exhibit a dark green to dark coloration. Additionally, the older branches frequently possess numerous nodules on their surface, which have round or oval cross sections and are found either individually or in series. Distinctively, the plant itself emits a noticeable and distinct odor. By chloroplast DNA analysis, this plant was
closely related to Neocinnamomum mekongense species (94.81% similarity) which belongs to the Lauraceae family. The finding may provide useful information to further exploit and conserve this plant for developing the local economic activity of this province.

Ayurveda is one of the oldest holistic treatment systems in the world. Finding an accurate and correct plant is the key to the working of Ayurvedic treatment. Identification of medicinal plants is a tedious job due to the look-alike feature and availability issue of medicinal plants with other plants. This paper emphases on the ideal identification and classification of plants of medicinal use using deep learning approaches. Previously researchers have used traditional machine learning techniques to identify medicinal plants, which lead to mixed results. Such results are good but not enough as the identification of medicinal plants may lead to a worsening situation for patients. This research is conducted to get results closer to an ayurvedic expert. The dataset used for this research has been taken from Mendeley Data. The dataset comprises 30 different species of medicinal plants. Hybrid transfer learning has been applied to this dataset. The model has generated a test accuracy of ...

Medicinal plants are the backbone of the system of medicines; they are the richest bioresource of drugs of traditional systems of medicine, modern medicines, nutraceuticals, food supplements, folk medicines, pharmaceutical intermediates, and chemical entities for synthetic drugs. These plants are classified according to their medicinal values. Classification of medicinal plants is acknowledged as a significant activity in the production of medicines along with the knowledge of its use in the medicinal industry. Medicinal plant classification based on parts such as leaves has shown significant results. An automated system for the identification of medicinal plants from leaves using Image processing and Machine Learning techniques has been presented. This paper provides knowledge of the process of identification of medicinal plants from features extracted from the images of leaves and different preprocessing techniques used for feature extraction from a leaf. Many features were extracted from each leaf such as its length, width, perimeter, area, color, rectangularity, and circularity. It is expected that for the automatic identification of medicinal plants, a web-based or mobile computer system will help the community people to develop their knowledge on medicinal plants, help taxonomists to develop more efficient species identification techniques and also participate significantly in the pharmaceutical drug manufacturing.

Automatic identification of plant species from natural images is a challenging problem with many practical applications in multiple disciplines. An accurate and automated system for plant species identification has important implications in addressing botanical taxonomy gaps, identifying new species, controlling the balance of ecosystems, and estimating yield and resource requirements in agriculture. However, identifying plants from uncontrolled natural images is a challenging problem due to the complexity of natural images, a large number of plant species, inter-species similarity, and the large-scale variance in appearance. In this work, we present a system called WTPlant, specifically designed for identifying plants in natural images. By assembling a collection of Convolutional Neural Networks with stacked/residual blocks and a preprocessing stage for multiscale analysis, WTPlant presents itself as a highly discriminative deep learning approach for this image classification problem.

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Shape is the most popular feature used in plant leaf identification, be it manual or automatic plant identification. In this paper, a study is conducted to investigate the most contributing features among three low-level features for plant leaf identification. Intraand inter-class identification are conducted using 455 herbal medicinal plant leaves, with 70% allocated for training and 30% for testing dataset. Shape feature is extracted using Scale Invariant Feature Transform (SIFT); colour is represented using colour moments; and Segmentation-Based Fractal Texture Analysis (SFTA) is utilized to describe texture feature. Intra-class analysis showed that fusion of texture and shape surpassed fusion of texture, shape and colour. Single texture feature identification also achieved highest identification rate compared to identification using colour or shape. Inter-class analysis further support texture to be the discriminative feature among the low-level features. Results demonstrate that single texture feature outperformed colour or shape feature achieving 92% identification rate. Furthermore, fusion of all three features accomplished 94% identification rate.

Shape is the most popular feature used in plant leaf identification, be it manual or automatic plant identification. In this paper, a study is conducted to investigate the most contributing features among three low-level features for plant leaf identification. Intraand inter-class identification are conducted using 455 herbal medicinal plant leaves, with 70% allocated for training and 30% for testing dataset. Shape feature is extracted using Scale Invariant Feature Transform (SIFT); colour is represented using colour moments; and Segmentation-Based Fractal Texture Analysis (SFTA) is utilized to describe texture feature. Intra-class analysis showed that fusion of texture and shape surpassed fusion of texture, shape and colour. Single texture feature identification also achieved highest identification rate compared to identification using colour or shape. Inter-class analysis further support texture to be the discriminative feature among the low-level features. Results demonstrate that single texture feature outperformed colour or shape feature achieving 92% identification rate. Furthermore, fusion of all three features accomplished 94% identification rate.

In an attempt to make identification of plants easy and less cumbersome, computer-based software called Leasys was developed. It is a computerized version of a field key prepared for on-the-spot identification of savanna tree species in Nigeria. Leasys 1.1 is an improved version of the earlier version developed by the same authors. The current version is more expansive than the first version and thus has more features which make identification process faster and easier. Incorporated in Leasys 1.1 are some other features that make it more robust, flexible and versatile. These features include Leasys Property Registration which makes identification of a plant along two selection paths possible, Leasys Plant Management which inserts, updates and deletes plant data from the database, Leasys Deduction System whicht enables the user identify a plant by inputting a leaf feature and Leasys Quick Search which brings about fast identification of plant without going through the step by step pr...

In this paper a concept of relative measurement of similarity is proposed. With the approach presented we can quantify degree and percentage of similarities between two objects. Objects may be anything, a geometrical shape, a domestic article, a machine part, a piece of art, etc. In this paper, similarity is defined as a set of answers as True or False to a set of questions on attributes of these objects. The paper also discusses the properties of the proposed concept of similarity.

This article presents the participation of the ReVeS project to the ImageCLEF 2013 Plant Identification challenge. Our primary target being tree leaves, some extra effort had to be done this year to process images containing other plant organs. The proposed method tries to benefit from the presence of multiple sources of information for a same individual through the introduction of a late fusion system based on the decisions of classifiers for the different modalities. It also presents a way to incorporate the geographical information in the determination of the species by estimating their plausibility at the considered location. While maintaining its performance on leaf images (ranking 3rd on natural images and 4th on plain backgrounds) our team performed honorably on the brand new modalities with a 6th position.

The automatic identification of plant leaves is a very important current topic of research in vision systems. Several researchers have tried to solve the problem of identification from plant leaves proposing various techniques. The proposed techniques in the literature have obtained excellent results on data sets where the leaves have dissimilar features to each other. However, in cases where the leaves are very similar to each other, the classification accuracy falls significantly. In this paper, we proposed a system to deal with the performance problem of machine learning algorithms where the leaves are very similar. The results obtained show that combination of different features and features selection process can improve the classification accuracy.

Developments in farm related technology have increased the importance of mapping individual plants in the field. An automated mapping system allows the size of these fields to scale up without being hindered by time-intensive, manual surveying. This research focuses on the development of a mapping system which uses geo-located images of the field to automatically locate plants and determine their coordinates. Additionally, this mapping process is capable of differentiating between groupings of plants by using Quick Response (QR) codes. This research applies to green plants that have been grown into seedlings before being planted, known as transplants, and for fields that are planted in nominally straight rows. The development of this mapping system is presented in two stages. First is the design of a robotic platform equipped with a Real Time Kinematic (RTK) receiver that is capable of traversing the field to capture images. Second is the post-processing pipeline which converts the images into a field map. This mapping system was applied to a field at the Land Institute containing approximately 25,000 transplants. The results show the mapped plant locations are accurate to within a few inches, and the use of QR codes is effective for identifying plant groups. These results demonstrate this system is successful in mapping large fields. However, the high overall complexity makes the system restrictive for smaller fields where a simpler solution may be preferable.

Plant identification is the process of matching a specimen plant to a known plant group. Most of the people identify plants visually (morphologically). The natural key systems use morphological characteristics that can be compared with known databases to find the plants' genus. Characteristics observed to include general character, structures of stems, roots, leaves and flowers. People who are not intimately familiar with particular species require assistance in their identification. In this context, we proposed an approach called local graph structure to identify and classify the plant species based on their leaf shape and texture pattern. The experimental results have shown a significant level of accuracy of the plant species identification.

Several thousand grapevine varieties exist, with even more naming identifiers. Adequate specialized labor is not available for proper classification or identification of grapevines, making the value of commercial vines uncertain. Traditional methods, such as genetic analysis or ampelometry, are time-consuming, expensive, and often require expert skills that are even rarer. New vision-based systems benefit from advanced and innovative technology and can be used by nonexperts in ampelometry. To this end, deep learning (DL) and machine learning (ML) approaches have been successfully applied for classification purposes. This work extends the state of the art by applying digital ampelometry techniques to larger grapevine varieties. We benchmarked MobileNet v2, ResNet-34, and VGG-11-BN DL classifiers to assess their ability for digital ampelography. In our experiment, all the models could identify the vines' varieties through the leaf with a weighted F1 score higher than 92%.