Automated plant identification using artificial neural network and support vector machine

Use of a mobile plant identification application and the out-of-school learning method in biodiversity education

Today, many students are no longer able to identify plants and researchers use the term "plant blindness" to describe students' ignorance of plant species. Knowledge of plant species is among the factors that best support an interest in and understanding of environmental issues, biodiversity, and a sustainable lifestyle. With the help of mobile technologies, it is thought that the knowledge level of students about herb and tree varieties can be increased outside of class hours and in outdoor education. The aim of this study was to determine the effect of the use of the PlantNet mobile application and the out-of-school learning method on the knowledge levels of 5th-grade students about the plant species in their environment and their behaviors demonstrating an understanding of biodiversity. For this purpose, at the beginning of the study, a plant species questionnaire and a biodiversity behavior questionnaire were applied to the students. Afterward, the students were asked to examine the plant species around them using the PlantNet mobile application. At the end of the activities, the data collection tools applied at the beginning of the study were applied again. It was determined that the students who participated in the activities displayed more biodiversity-related behaviors than before they participated in the activities, and that the students were able to write down more herb and tree species at the end of the activities. In addition, as a result of the activities, a positive and high-level relationship was found between the students' biodiversity-related behaviors and the total number of plant species they knew.

The Herbarium 2021 Half-Earth Challenge Dataset and Machine Learning Competition

Herbarium sheets present a unique view of the world's botanical history, evolution, and biodiversity. This makes them an all-important data source for botanical research. With the increased digitization of herbaria worldwide and advances in the domain of fine-grained visual classification which can facilitate automatic identification of herbarium specimen images, there are many opportunities for supporting and expanding research in this field. However, existing datasets are either too small, or not diverse enough, in terms of represented taxa, geographic distribution, and imaging protocols. Furthermore, aggregating datasets is difficult as taxa are recognized under a multitude of names and must be aligned to a common reference. We introduce the Herbarium 2021 Half-Earth dataset: the largest and most diverse dataset of herbarium specimen images, to date, for automatic taxon recognition. We also present the results of the Herbarium 2021 Half-Earth challenge, a competition that was part of the Eighth Workshop on Fine-Grained Visual Categorization (FGVC8) and hosted by Kaggle to encourage the development of models to automatically identify taxa from herbarium sheet images.

Reversing extinction trends: new uses of (old) herbarium specimens to accelerate conservation action on threatened species

Although often not collected specifically for the purposes of conservation, herbarium specimens offer sufficient information to reconstruct parameters that are needed to designate a species as 'at-risk' of extinction. While such designations should prompt quick and efficient legal action towards species recovery, such action often lags far behind and is mired in bureaucratic procedure. The increase in online digitization of natural history collections has now led to a surge in the number new studies on the uses of machine learning. These repositories of species occurrences are now equipped with advances that allow for the identification of rare species. The increase in attention devoted to estimating the scope and severity of the threats that lead to the decline of such species will increase our ability to mitigate these threats and reverse the declines, overcoming a current barrier to the recovery of many threatened plant species. Thus far, collected specimens have been used to fill gaps in systematics, range extent, and past genetic diversity. We find that they also offer material with which it is possible to foster species recovery, ecosystem restoration, and de-extinction, and these elements should be used in conjunction with machine learning and citizen science initiatives to mobilize as large a force as possible to counter current extinction trends.

Natural Products, the Fourth Industrial Revolution, and the Quintuple Helix

The profound interconnectedness of the sciences and technologies embodied in the Fourth Industrial Revolution is discussed in terms of the global role of natural products, and how that interplays with the development of sustainable and climate-conscious practices of cyberecoethnopharmacolomics within the Quintuple Helix for the promotion of a healthier planet and society.

Robust and simplified machine learning identification of pitfall trap-collected ground beetles at the continental scale

Insect populations are changing rapidly, and monitoring these changes is essential for understanding the causes and consequences of such shifts. However, large-scale insect identification projects are time-consuming and expensive when done solely by human identifiers. Machine learning offers a possible solution to help collect insect data quickly and efficiently.Here, we outline a methodology for training classification models to identify pitfall trap-collected insects from image data and then apply the method to identify ground beetles (Carabidae). All beetles were collected by the National Ecological Observatory Network (NEON), a continental scale ecological monitoring project with sites across the United States. We describe the procedures for image collection, image data extraction, data preparation, and model training, and compare the performance of five machine learning algorithms and two classification methods (hierarchical vs. single-level) identifying ground beetles from the species to subfamily level. All models were trained using pre-extracted feature vectors, not raw image data. Our methodology allows for data to be extracted from multiple individuals within the same image thus enhancing time efficiency, utilizes relatively simple models that allow for direct assessment of model performance, and can be performed on relatively small datasets.The best performing algorithm, linear discriminant analysis (LDA), reached an accuracy of 84.6% at the species level when naively identifying species, which was further increased to >95% when classifications were limited by known local species pools. Model performance was negatively correlated with taxonomic specificity, with the LDA model reaching an accuracy of ~99% at the subfamily level. When classifying carabid species not included in the training dataset at higher taxonomic levels species, the models performed significantly better than if classifications were made randomly. We also observed greater performance when classifications were made using the hierarchical classification method compared to the single-level classification method at higher taxonomic levels.The general methodology outlined here serves as a proof-of-concept for classifying pitfall trap-collected organisms using machine learning algorithms, and the image data extraction methodology may be used for nonmachine learning uses. We propose that integration of machine learning in large-scale identification pipelines will increase efficiency and lead to a greater flow of insect macroecological data, with the potential to be expanded for use with other noninsect taxa.

A Review on Robust Computational Approaches Based Identification and Authentication of Herbal Raw Drugs

Background:

Over the last decade, there has been a sudden rise in the demand for herbal as well as Information and Technology (IT) industry around the world. Identification of plant species has become useful and relevant to all the members of the society including farmers, traders, hikers, etc. Conventional authentication techniques such as morphological characterization, histological methods, and optical microscopy require multiple skills which are tedious, timeconsuming and difficult to learn for non-experts. This creates a hurdle for individuals interested in acquiring knowledge of species. Relying on rapid, economical and computerized approaches to identify and authenticate medicinal plants has become a recent development.

Objective:

The purpose of this review is to summarize artificial intelligence-based technologies for wider dissemination of common plant-based knowledge such as identification and authentication to common people earlier limited to only experts.

Methods:

A robust plant identification design enabling automated plant-organ and feature-based identification utilizing pattern recognition and image processing techniques resulting in image retrieval and recognition has been highlighted in this review for all the concerned stakeholders. Attempts have been made to compare conventional authentication methods with advanced computerized techniques to emphasize the advantages and future applications of an automated identification system in countering adulteration and providing fair trade opportunities to farmers.

Results:

Major findings suggested that microscopical features such as shape and size of calcium oxalate crystals, trichomes, scleriods, stone cells, fibers, etc. are the essential descriptors for identification and authentication of herbal raw drugs using computational approaches.

Conclusion:

This computational design can be successfully employed to address quality issues of medicinal plants. Therefore, computational techniques proved as a milestone in the growth of agriculture and medicinal plant industries.

An algorithm competition for automatic species identification from herbarium specimens

PREMISE

Plant biodiversity is threatened, yet many species remain undescribed. It is estimated that >50% of undescribed species have already been collected and are awaiting discovery in herbaria. Robust automatic species identification algorithms using machine learning could accelerate species discovery.

METHODS

To encourage the development of an automatic species identification algorithm, we submitted our Herbarium 2019 data set to the Fine-Grained Visual Categorization sub-competition (FGVC6) hosted on the Kaggle platform. We chose to focus on the flowering plant family Melastomataceae because we have a large collection of imaged herbarium specimens (46,469 specimens representing 683 species) and taxonomic expertise in the family. As is common for herbarium collections, some species in this data set are represented by few specimens and others by many.

RESULTS

In less than three months, the FGVC6 Herbarium 2019 Challenge drew 22 teams who entered 254 models for Melastomataceae species identification. The four best algorithms identified species with >88% accuracy.

DISCUSSION

The FGVC competitions provide a unique opportunity for computer vision and machine learning experts to address difficult species-recognition problems. The Herbarium 2019 Challenge brought together a novel combination of collections resources, taxonomic expertise, and collaboration between botanists and computer scientists.

Designing an Herbarium Digitisation Workflow with Built-In Image Quality Management

Digitisation of natural history collections has evolved from creating databases for the recording of specimens’ catalogue and label data to include digital images of specimens. This has been driven by several important factors, such as a need to increase global accessibility to specimens and to preserve the original specimens by limiting their manual handling. The size of the collections pointed to the need of high throughput digitisation workflows. However, digital imaging of large numbers of fragile specimens is an expensive and time-consuming process that should be performed only once. To achieve this, the digital images produced need to be useful for the largest set of applications possible and have a potentially unlimited shelf life. The constraints on digitisation speed need to be balanced against the applicability and longevity of the images, which, in turn, depend directly on the quality of those images. As a result, the quality criteria that specimen images need to fulfil influence the design, implementation and execution of digitisation workflows. Different standards and guidelines for producing quality research images from specimens have been proposed; however, their actual adaptation to suit the needs of different types of specimens requires further analysis. This paper presents the digitisation workflow implemented by Meise Botanic Garden (MBG). This workflow is relevant because of its modular design, its strong focus on image quality assessment, its flexibility that allows combining in-house and outsourced digitisation, processing, preservation and publishing facilities and its capacity to evolve for integrating alternative components from different sources. The design and operation of the digitisation workflow is provided to showcase how it was derived, with particular attention to the built-in audit trail within the workflow, which ensures the scalable production of high-quality specimen images and how this audit trail ensures that new modules do not affect either the speed of imaging or the quality of the images produced.

Digitization and the Future of Natural History Collections

Natural history collections (NHCs) are the foundation of historical baselines for assessing anthropogenic impacts on biodiversity. Along these lines, the online mobilization of specimens via digitization—the conversion of specimen data into accessible digital content—has greatly expanded the use of NHC collections across a diversity of disciplines. We broaden the current vision of digitization (Digitization 1.0)—whereby specimens are digitized within NHCs—to include new approaches that rely on digitized products rather than the physical specimen (Digitization 2.0). Digitization 2.0 builds on the data, workflows, and infrastructure produced by Digitization 1.0 to create digital-only workflows that facilitate digitization, curation, and data links, thus returning value to physical specimens by creating new layers of annotation, empowering a global community, and developing automated approaches to advance biodiversity discovery and conservation. These efforts will transform large-scale biodiversity assessments to address fundamental questions including those pertaining to critical issues of global change.

Deep Learning Techniques for Grape Plant Species Identification in Natural Images

Frequently, the vineyards in the Douro Region present multiple grape varieties per parcel and even per row. An automatic algorithm for grape variety identification as an integrated software component was proposed that can be applied, for example, to a robotic harvesting system. However, some issues and constraints in its development were highlighted, namely, the images captured in natural environment, low volume of images, high similarity of the images among different grape varieties, leaf senescence, and significant changes on the grapevine leaf and bunch images in the harvest seasons, mainly due to adverse climatic conditions, diseases, and the presence of pesticides. In this paper, the performance of the transfer learning and fine-tuning techniques based on AlexNet architecture were evaluated when applied to the identification of grape varieties. Two natural vineyard image datasets were captured in different geographical locations and harvest seasons. To generate different datasets for training and classification, some image processing methods, including a proposed four-corners-in-one image warping algorithm, were used. The experimental results, obtained from the application of an AlexNet-based transfer learning scheme and trained on the image dataset pre-processed through the four-corners-in-one method, achieved a test accuracy score of 77.30%. Applying this classifier model, an accuracy of 89.75% on the popular Flavia leaf dataset was reached. The results obtained by the proposed approach are promising and encouraging in helping Douro wine growers in the automatic task of identifying grape varieties.

Autonomous Robotics for Identification and Management of Invasive Aquatic Plant Species

Invasive aquatic plant species can expand rapidly throughout water bodies and cause severely adverse economic and ecological impacts. While mechanical, chemical, and biological methods exist for the identification and treatment of these invasive species, they are manually intensive, inefficient, costly, and can cause collateral ecological damage. To address current deficiencies in aquatic weed management, this paper details the development of a small fleet of fully autonomous boats capable of subsurface hydroacoustic imaging (to scan aquatic vegetation), machine learning (for automated weed identification), and herbicide deployment (for vegetation control). These capabilities aim to minimize manual labor and provide more efficient, safe (reduced chemical exposure to personnel), and timely weed management. Geotagged hydroacoustic imagery of three aquatic plant varieties (Hydrilla, Cabomba, and Coontail) was collected and used to create a software pipeline for subsurface aquatic weed classification and distribution mapping. Employing deep learning, the novel software achieved a classification accuracy of 99.06% after training.

A benchmark dataset of herbarium specimen images with label data

BACKGROUND

More and more herbaria are digitising their collections. Images of specimens are made available online to facilitate access to them and allow extraction of information from them. Transcription of the data written on specimens is critical for general discoverability and enables incorporation into large aggregated research datasets. Different methods, such as crowdsourcing and artificial intelligence, are being developed to optimise transcription, but herbarium specimens pose difficulties in data extraction for many reasons.

NEW INFORMATION

To provide developers of transcription methods with a means of optimisation, we have compiled a benchmark dataset of 1,800 herbarium specimen images with corresponding transcribed data. These images originate from nine different collections and include specimens that reflect the multiple potential obstacles that transcription methods may encounter, such as differences in language, text format (printed or handwritten), specimen age and nomenclatural type status. We are making these specimens available with a Creative Commons Zero licence waiver and with permanent online storage of the data. By doing this, we are minimising the obstacles to the use of these images for transcription training. This benchmark dataset of images may also be used where a defined and documented set of herbarium specimens is needed, such as for the extraction of morphological traits, handwriting recognition and colour analysis of specimens.