Automatic Plant Identification Research Articles

Morphological characters of Species Members of the Solanaceae Family in Menoreh Samigaluh Hill, Yogyakarta

The Solanaceae family has 1,300 genera distributed in the sub-tropics and tropics. In Indonesia, the genus of the Solanaceae family is used as a source of food and medicine. Solanaceae as plants used for food, medicine and health care, require a precise and accurate identification process. Automatic plant identification can use visual morphological characteristics. Environmental conditions such as extreme temperatures, drought, and water stress cause plants to adapt. The adaptation process can induce morphological changes, this study aims to study the morphological characters of solanaceae in the karst area, the hills of the menorah samigaluh Yogyakarta. The data collection method uses Visual Encounter Census (VES) by purposive sampling. Data analysis was carried out in a qualitative descriptive manner. The results obtained three genera, and 7 species (Solanum melongena, Solanum torvum, Solanum lycopersicum, Solanum diphyllum, Capsicum annuum, Capsicum frutescens, and Physalis angulata). There are 23 morphological characters used as a reference in identifying species of Solanaceae members.

Developing a Neural Network-based Model for Identifying Medicinal Plant Leaves using Image Recognition Techniques

Herbal plants play a vital role in human health and the environment, as they can provide both medical benefits and oxygen. Many herbal plants contain valuable therapeutic elements that can be passed down to future generations. Traditional methods of identifying plant species, such as manual measurement and examination of characteristics, are labor-intensive and time-consuming. There has been a push to develop more efficient methods using technology, such as digital image processing and pattern recognition techniques to address this. The proper identification of plant methods using computer vision and neural network techniques has been proposed. This approach involves neural network models such as CNN, ALexnet and ResNet for identifying the medical plants based on their respective features. Classification metrics give the 96.82 average accuracies. These results have been promising, and further research will involve using a larger dataset and going more into deep-learning neural networks to improve the accuracy of medicinal plant identification. It is hoped that a web or mobile-based system for automatic plant identification can help increase knowledge about medicinal plants, improve species identification techniques, and protect endangered species.

Developing A Neural Network-Based Model for Identifying Medicinal Plant Leaves Using Image Recognition Techniques

Herbal plants contribute an important role in people's health and the environment, as they can provide both medical benefits and oxygen. Many herbal plants contain valuable therapeutic elements that can be passed down to future generations. Traditional methods of identifying plant species, such as manual measurement and examination of characteristics, are labor-intensive and time-consuming. To address this, there has been a push to develop more efficient methods using technology, such as digital image processing and pattern recognition techniques. The exact recognition of plants uses methodologies like computer vision and neural networks, which have been proposed earlier. This approach involves neural network models such as CNN, ALexnet, and ResNet for identifying the medical plants based on their respective features. Classification metrics give the 96.82 average accuracies. These results have been promising, and further research will involve using a larger dataset and going more into deep-learning neural networks to improve the accuracy of medicinal plant identification. It is hoped that a web or mobile-based system for automatic plant identification can help increase knowledge about medicinal plants, improve techniques for species recognition, and participate in the preservation of species that are considered ad endangered.

Application of Drone Surveillance for Advance Agriculture Monitoring by Android Application Using Convolution Neural Network

Plant diseases are a significant threat to global food security, impacting crop yields and economic growth. Accurate identification of plant diseases is crucial to minimize crop loses and optimize plant health. Traditionally, plant classification is performed manually, relying on the expertise of the classifier. However, recent advancements in deep learning techniques have enabled the creation of efficient crop classification systems using computer technology. In this context, this paper proposes an automatic plant identification process based on a synthetic neural network with the ability to detect images of plant leaves. The trained model EfficientNet-B3 was used to achieve a high success rate of 98.80% in identifying the corresponding combination of plant and disease. To make the system user-friendly, an Android application and website were developed, which allowed farmers and users to easily detect diseases from the leaves. In addition, the paper discusses the transfer method for studying various plant diseases, and images were captured using a drone or a smartphone camera. The ultimate goal is to create a user-friendly leaf disease product that can work with mobile and drone cameras. The proposed system provides a powerful tool for rapid and efficient plant disease identification, which can aid farmers of all levels of experience in making informed decisions about the use of chemical pesticides and optimizing plant health.

Assessing the accuracy of free automated plant identification applications

AbstractWidely available and inexpensive mobile phone applications offer users, whether professional ecologists or interested amateurs, the potential for simple and rapid automated identification of species, without the need to use field guides and identification keys. The increasing accuracy of machine learning is well established, but it is currently unclear if, and under what circumstances, free‐to‐use mobile phone applications are accurate for identifying plants to species level in real‐world field conditions.We test five popular and free identification applications for plants using 857 professionally identified images of 277 species from 204 genera. Across all applications, 85% of images were identified correctly in the top five suggestions, and 69% were correct with the first suggestion. Plant type (woody, forbs, grasses, rushes/sedges, ferns/horsetails) was a significant determinant of identification performance for each application. For some applications, image saliency was also important; exposure and focus were not significant.Applications performed well, with at least one of the three best‐performing applications identifying 96% of images correctly as their first suggestion. We conclude that, subject to some caveats, free phone‐based plant identification applications are valid and useful tools for those wanting rapid identification and for anyone wanting to engage with the natural world.Read the freePlain Language Summaryfor this article on the Journal blog.

Identification of Zingiberaceae Species Based on Leaf Recognition Using Multiclass Support Vector Machine

The family Zingiberaceae or known as the ginger family of flowering plants famous for its medicinal values and is widely distributed especially in Southeast Asia. Malaysia is one of the countries with abundance of Zingiberaceous species. Usually, identification of the plant species begins by identifying the leaves. Identification based on leaf recognition is the most effective method because a healthy plant has leaves and it exists all the time, unlike fruits and flowers which may only exist at certain times. However, the leaf recognition considered as intricate task and challenging especially when using conventional approaches as most plants have similar shape and colour. Thus, the aim of this research is to develop an interactive application for classification and identification of selected Zingiberaceae species, namely: Zingiber officinale, Curcuma longa, Etlingera elatior and Alpinia galanga based on leaf recognition using multiclass Support Vector Machine (SVM). Six steps were constructed to develop an interactive application for classification of Zingiberaceae species: 1) Collection of leaf samples, 2) Creation of database from captured leaf images, 3) Image preprocessing, 4) Leaf image processing, 5) Classification of image textures using SVM and 6) Design of graphical user interface (GUI). The image features extraction in leaf image processing were based on gray-level co-ccurrence matrix (GLCM), Canny and Prewitt algorithms. The combination of GLCM and Prewitt achieved the highest accuracy which was recorded of 95% from the overall accuracy in classification of Zingiberaceae species. By using the automatic plant identification system, results will come up more accurate and faster.

Senescence Reversion in Plant Images Using Perception and Unpaired Data

Recent work on using herbarium images for automatic plant identification, and in particular to do domain adaptation to field images, has been promising. A potential way to address such domain adaptation problem is generative: Hallucinate how a herbarium image would have looked like when it was in the field, and use such synthetic, fresh and green image for plant identification purposes. Such generative task, called senescence reversion, has been poorly explored. To our knowledge, it has been studied only in terms of paired data, meaning, with pairs of dry and fresh images of the same specimen. Such paired data is hard to produce, curate and find. In this work we explore herbarium senescence reversion via unpaired data. The lack of pairs at specimen level presents its own challenges, as capturing the intricacies of each species depends on images of different specimens from different domains. We explore learning a mapping from a herbarium image to leaf plant images and vice-versa, aligning two models, one for each herbarium and leaf domain. We experiment against the state-of-the-art paired baseline Pix2Pix which yields a SSIM of 0.8986, compared to our unpaired approach that yields 0.8865, showing very similar results on reconstruction metrics regardless of our approach not having the luxury of pairs by specimen. Additionally, we apply perceptual loss in order to improve the natural look of the synthetic images. The balance of how much perception is good to avoid reconstruction problems is also studied. Lastly, using a new unpaired dataset built by ourselves, our results show that using a low lambda value from 0.025 to 0.05 for perceptual loss, helps getting lower Frachet Inception Distances and higher Inception Scores. To our knowledge, no other work has focused on reverting senescence of herbarium sheet images based on unpaired data.

Automatic Identification of Ivorian Plants from Herbarium Specimens using Deep Learning

— 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. Keywords— Deep learning, Herbarium specimens, image preprocessing, Convolutional Neural Network, Classification.

Identifying and extracting bark key features of 42 tree species using convolutional neural networks and class activation mapping

The significance of automatic plant identification has already been recognized by academia and industry. There were several attempts to utilize leaves and flowers for identification; however, bark also could be beneficial, especially for trees, due to its consistency throughout the seasons and its easy accessibility, even in high crown conditions. Previous studies regarding bark identification have mostly contributed quantitatively to increasing classification accuracy. However, ever since computer vision algorithms surpassed the identification ability of humans, an open question arises as to how machines successfully interpret and unravel the complicated patterns of barks. Here, we trained two convolutional neural networks (CNNs) with distinct architectures using a large-scale bark image dataset and applied class activation mapping (CAM) aggregation to investigate diagnostic keys for identifying each species. CNNs could identify the barks of 42 species with > 90% accuracy, and the overall accuracies showed a small difference between the two models. Diagnostic keys matched with salient shapes, which were also easily recognized by human eyes, and were typified as blisters, horizontal and vertical stripes, lenticels of various shapes, and vertical crevices and clefts. The two models exhibited disparate quality in the diagnostic features: the old and less complex model showed more general and well-matching patterns, while the better-performing model with much deeper layers indicated local patterns less relevant to barks. CNNs were also capable of predicting untrained species by 41.98% and 48.67% within the correct genus and family, respectively. Our methodologies and findings are potentially applicable to identify and visualize crucial traits of other plant organs.

Artificial Intelligence for Automated Plant Species Identification: A Review

Plants are very important for life on Earth. There is a wide variety of plant species and their number increases each year. The plants identification using conventional keys is complex, takes time and it is frustrating for non-experts because of the use of specific botanical terms/techniques. This creates a difficult obstacle to overcome for novices interested in acquiring knowledge about species, which is very important to develop any environmental study, like climate change anticipation models for example. Today, there is an increasing interest in automating the species identification process. The availability and omnipresence of relevant technologies, such as digital cameras, mobile devices, pattern recognition and artificial intelligence techniques in general, have allowed the idea of automated species identification to become a reality. In this paper, we present a review of automated plant identification over all significant available studies in literature. The main result of this synthesis is that the performance of advanced deep learning models, despite the presence of several challenges, is becoming close to the most advanced human expertise.

AI-based Identification of Plant Photographs from Herbarium Specimens

AI-based Identification of Plant Photographs from Herbarium Specimens

Plant image identification application demonstrates high accuracy in Northern Europe.

Automated image-based plant identification has experienced rapid development and has been already used in research and nature management. However, there is a need for extensive studies on how accurately automatic plant identification works and which characteristics of observations and study species influence the results. We investigated the accuracy of the Flora Incognita application, a research-based tool for automated plant image identification. Our study was conducted in Estonia, Northern Europe. Photos originated from the Estonian national curated biodiversity observations database, originally without the intention to use them for automated identification (1496 photos, 542 species) were examined. Flora Incognita was also directly tested in field conditions in various habitats, taking images of plant organs as guided by the application (998 observations, 1703 photos, 280 species). Identification accuracy was compared among species characteristics: plant family, growth forms and life forms, habitat type and regional frequency. We also analysed image characteristics (plant organs, background, number of species in focus), and the number of training images that were available for particular species to develop the automated identification algorithm. From database images 79.6 % of species were correctly identified by Flora Incognita; in the field conditions species identification accuracy reached 85.3 %. Overall, the correct genus was found for 89 % and the correct plant family for 95 % of the species. Accuracy varied among different plant families, life forms and growth forms. Rare and common species and species from different habitats were identified with equal accuracy. Images with reproductive organs or with only the target species in focus were identified with greater success. The number of training images per species was positively correlated with the identification success. Even though a high accuracy has been already achieved for Flora Incognita, allowing its usage for research and practices, our results can guide further improvements of this application and automated plant identification in general.

The multi-level table and circular diagnostic chart as alternative taxonomic key formats for plant identification

Correct identification of plants is a prerequisite to achieving desirable results in health care delivery, sustainable food production and housing, forest resources management and environmental protection. However, many of the paper-based/printable taxonomic key formats available to the taxonomist for this important responsibility are fraught with inadequacies some of which include fixed sequence of plant identification steps, non- or hardly-susceptible to computerisation, lack of provision for confirmation of suspected plant identity and indeterminable character states, and tedious construction and navigation procedures. This paper with the aim of making the practice of plant taxonomy more attractive, less laborious and dreaded, proposes two new key formats with highlights of their design/features, construction procedures and usage. These alternative key formats, with varying capacities to circumvent some of the enumerated challenges are multi-level table of identification and multi-layer circular diagnostic chart. The status of each of the proposed key formats is discussed with reference to the inadequacies observed in the dichotomous key format with which most taxonomists are familiar. Based on their structural features and functionality attributes, it is conclusive that the two alternative key formats constitute useful templates upon which reliable plant diagnostic tools can be based. Key words: Automated plant identification, computerised key, diagnostic key, dichotomous key, multi-access key, plant identification, single-access key, taxonomic key.

Reconstruction of damaged herbarium leaves using deep learning techniques for improving classification accuracy

Leaf is one of the most commonly used organs for species identification. The traditional identification process involves a manual analysis of individual dried or fresh leaf's features by the botanists. Recent advancements in computer vision techniques have assisted in automating the plants families/species identification process based on the digital images of leaves. However, most of the existing studies have focused on using datasets for fresh and intact leaves. A huge amount of data for preserved plants in the form of digitized herbaria specimens have not been effectively utilized for the task of automated identification because of the presence of damaged leaves in specimens. In this study, deep learning techniques have been proposed as a tool for reconstructing the damaged herbarium leaves in order to maximize the usefulness of the digitized specimens for automated plant identification task by increasing the number of individual samples of leaves. The reconstruction results of two different families of convolution neural networks (CNNs) have been compared for data from ten different plant families namely Anacardiaceae, Annonaceae, Dipterocarpaceae, Ebenaceae, Euphorbiaceae, Malvaceae, Phyllanthaceae, Polygalaceae, Rubiaceae and Sapotaceae. The performance of automated identification task was improved by more than 20% using the reconstructed leaves images as compared to using the original data (i.e. images of specimens with damaged leaves). This work evidently suggests that deep learning techniques can be utilized for reconstruction of damaged leaves even on a challenging herbarium leaves dataset.

Automatic Plant Identification Using Transfer Learning

Plant identification is a widely researched area in the field of computer vision. Many attempts have been made to automate the process of plant identification using an image of a part of plant including flower, leaf and bark. Leaf has proven be the most reliable source of information. After exhaustive experiments, we chose to apply transfer learning to compare the feature extraction capabilities of VGG-16, Xception, MobileNetV2 Convolutional Neural Network (CNN) and DenseNet121 architectures to freely available Swedish, Flavia and MalayaKew leaf image datasets. Random Forest is used as classifier to identify the species of given leaf. The evaluations and comparisons of the specified feature extractor models are provided. DenseNet121 achieved maximum accuracy of 100%, 99% and 92.4% respectively in the three datasets.

Transfer learning with deep convolutional neural network for automated plant identification in hainan island

The Hainan Island has a generally high biological diversity with a wide variety of plant species, some of which are listed as endemic to the island. It is time-consuming and difficult, even for the botanist experts to determine the name of species based on observations. Automated plant identification enables experts to process significantly greater numbers of plants with higher efficiencies in shorter periods of time. However, plant recognition is a kind of fine-grained visual recognition problem, which is relatively harder than conventional image recognition. In this paper, we employ a Deep Convolutional Neural Network (DCNN) trained on the ImageNet database, which contains millions of images, and then transfer the learning information for automated plant identification based on flower and fruit images. First, we modify the last three layers of the pre-trained network in order to adapt ResNet-50 model to our classification task, and replace the fully connected layer in the original pre-trained network with another fully connected layers, in which the output size represents the class of plants. Secondly, we use transfer experience and fine-tuned pre-trained DCNN for experiments using flower and fruit images. Finally, we evaluate the proposed network on two available botanical datasets: the Oxford flowers dataset with 102 classes and the HNPlant flowers and fruits dataset with 20 classes, and determine the optimal values of the associated hyperparameters to improve the overall performance. Experiment results demonstrate that the highest classification accuracies exhibited by the proposed model on the Oxford-102 and HNPlant-20 datasets are 92.4% and 95.0%, respectively, thus establishing their effectiveness and superiority.

Deep convolutional neural network based plant species recognition through features of leaf

In present scenario, the research under image processing has been rapidly transformed from machine learning to deep learning. The deep learning algorithms are usually applied in the various areas like images to be classified or identified more accurately. One of the application areas of deep learning is the plant identification through its leaf which helps to recognize plant species. Botanists consume most of time in identifying plant species by manually scrutinizing and finding its features. This paper proposes an automated plant identification system, for identifying the plants species through their leaf. This task is accomplished using deep convolutional neural network to achieve higher accuracy. Image pre-processing, feature extraction and recognition are three main identification steps which are taken under consideration. Proposed CNN classifier learns the features of plants such as classification of leafs by using hidden layers like convolutional layer, max pooling layer, dropout layers and fully connected layers. The model acquires a knowledge related to features of Swedish leaf dataset in which 15 tree classes are available, that helps to predict the correct category of unknown plant with accuracy of 97% and minimum losses. Result is slightly better than the previous work that analyzes 93.75% of accuracy.

What plant is that? Tests of automated image recognition apps for plant identification on plants from the British flora.

There has been a recent explosion in development of image recognition technology and its application to automated plant identification, so it is timely to consider its potential for field botany. Nine free apps or websites for automated plant identification and suitable for use on mobile phones or tablet computers in the field were tested on a disparate set of 38 images of plants or parts of plants chosen from the higher plant flora of Britain and Ireland. There were large differences in performance with the best apps identifying >50 % of samples tested to genus or better. Although the accuracy is good for some of the top-rated apps, for any quantitative biodiversity study or for ecological surveys, there remains a need for validation by experts or against conventional floras. Nevertheless, the better-performing apps should be of great value to beginners and amateurs and may usefully stimulate interest in plant identification and nature. Potential uses of automated image recognition plant identification apps are discussed and recommendations made for their future use.

How citizen scientists contribute to monitor protected areas thanks to automatic plant identification tools

Abstract1. Successful monitoring and management of plant resources worldwide needs the involvement of civil society to support natural reserve managers. Because it is difficult to correctly and quickly identify plant species for non‐specialists, the development of recent techniques based on automatic visual identification should facilitate and increase public engagement in citizen science initiatives.2. Automatic identification platforms are new to most citizen scientists and land managers. Pl@ntNet is such a platform, available since 2013 on web and mobile environments, and now included in several workflows such as invasive alien species management, endemic species monitoring, educational activities and eco‐tourism practices. The successful development of such platforms needs to identify their strengths and weaknesses in order to improve and facilitate their use in all aspects of ecosystem management.3. Here we present two Pl@ntNet citizen science initiatives used by conservation practitioners in Europe (France) and Africa (Kenya). We discuss various perspectives, including benefits and limitations. Based on the experiences of field managers, we formulate several recommendations for future initiatives. The recommendations are aimed at a diverse group of conservation managers and citizen science practitioners.

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.