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

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. During the age of a global biodiversity crisis, it is increasingly important to recognize which plant species surround us in order to protect them. One solution to improve knowledge about plants is using image-based identification applications powered by artificial intelligence. We examined several thousand images of hundreds of species from Northern Europe to explore one such application, Flora Incognita. We found a high accuracy but not in all plant groups. Performance of the application was also dependent how many training images machine learning had used per species. Even more accurate identification could be expected with additional training data.

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.

The rhizosphere microbiome is pivotal for plant health and growth, providing defence against pests and diseases, facilitating nutrient acquisition and helping plants to withstand abiotic stresses. Plants can actively recruit members of the soil microbial community for positive feedbacks, but the underlying mechanisms and plant traits that drive microbiome assembly and functions are largely unknown. Domestication of plant species has substantially contributed to human civilization, but also caused a strong decrease in the genetic diversity of modern crop cultivars that may have affected the ability of plants to establish beneficial associations with rhizosphere microbes. Here, we review how plants shape the rhizosphere microbiome and how domestication may have impacted rhizosphere microbiome assembly and functions via habitat expansion and via changes in crop management practices, root exudation, root architecture, and plant litter quality. We also propose a “back to the roots” framework that comprises the exploration of the microbiome of indigenous plants and their native habitats for the identification of plant and microbial traits with the ultimate goal to reinstate beneficial associations that may have been undermined during plant domestication.

Winner of the 2024 SHNH Natural History Book Prize (The John Thackray Medal) An exploration of plant wisdom, from the Southern Mountain Tea Flower to the Dawn Redwood China's vast and ancient body of documented knowledge about plants includes horticultural manuals and monographs, comprehensive encyclopedias, geographies, and specialized anthologies of verse and prose written by keen observers of nature. Until the late nineteenth century, however, standard practice did not include deploying a set of diagnostic tools using a common terminology and methodology to identify and describe new and unknown species or properties. Ordering the Myriad Things relates how traditional knowledge of plants in China gave way to scientific botany between the mid-nineteenth and mid-twentieth centuries, when plants came to be understood in a hierarchy of taxonomic relationships to other plants and within a broader ecological context. This shift not only expanded the universe of plants beyond the familiar to encompass unknown species and geographies but fueled a new knowledge of China itself. Nicholas K. Menzies highlights the importance of botanical illustration as a tool for recording nature—contrasting how images of plants were used in the past to the conventions of scientific drawing and investigating the transition of \"traditional\" systems of organization, classification, observation, and description to \"modern\" ones.

Background Hundreds of herbarium collections have accumulated a valuable heritage and knowledge of plants over several centuries. Recent initiatives started ambitious preservation plans to digitize this information and make it available to botanists and the general public through web portals. However, thousands of sheets are still unidentified at the species level while numerous sheets should be reviewed and updated following more recent taxonomic knowledge. These annotations and revisions require an unrealistic amount of work for botanists to carry out in a reasonable time. Computer vision and machine learning approaches applied to herbarium sheets are promising but are still not well studied compared to automated species identification from leaf scans or pictures of plants in the field. Results In this work, we propose to study and evaluate the accuracy with which herbarium images can be potentially exploited for species identification with deep learning technology. In addition, we propose to study if the combination of herbarium sheets with photos of plants in the field is relevant in terms of accuracy, and finally, we explore if herbarium images from one region that has one specific flora can be used to do transfer learning to another region with other species; for example, on a region under-represented in terms of collected data. Conclusions This is, to our knowledge, the first study that uses deep learning to analyze a big dataset with thousands of species from herbaria. Results show the potential of Deep Learning on herbarium species identification, particularly by training and testing across different datasets from different herbaria. This could potentially lead to the creation of a semi, or even fully automated system to help taxonomists and experts with their annotation, classification, and revision works.

The past couple of decades have witnessed global resurgence of herbal‐based health care. As a result, the trade of raw drugs has surged globally. Accurate and fast scientific identification of the plant(s) is the key to success for the herbal drug industry. The conventional approach is to engage an expert taxonomist, who uses a mix of traditional and modern techniques for precise plant identification. However, for bulk identification at industrial scale, the process is protracted and time‐consuming. DNA barcoding, on the other hand, offers an alternative and feasible taxonomic tool box for rapid and robust species identification. For the success of DNA barcode, the barcode loci must have sufficient information to differentiate unambiguously between closely related plant species and discover new cryptic species. For herbal plant identification, matK, rbcL, trnH‐psbA, ITS, trnL‐F, 5S‐rRNA and 18S‐rRNA have been used as successful DNA barcodes. Emerging advances in DNA barcoding coupled with next‐generation sequencing and high‐resolution melting curve analysis have paved the way for successful species‐level resolution recovered from finished herbal products. Further, development of multilocus strategy and its application has provided new vistas to the DNA barcode‐based plant identification for herbal drug industry. For successful and acceptable identification of herbal ingredients and a holistic quality control of the drug, DNA barcoding needs to work harmoniously with other components of the systems biology approach. We suggest that for effectively resolving authentication challenges associated with the herbal market, DNA barcoding must be used in conjunction with metabolomics along with need‐based transcriptomics and proteomics.

Background Herbaria are valuable sources of extensive curated plant material that are now accessible to genetic studies because of advances in high-throughput, next-generation sequencing methods. As an applied assessment of large-scale recovery of plastid and ribosomal genome sequences from herbarium material for plant identification and phylogenomics, we sequenced 672 samples covering 21 families, 142 genera and 530 named and proposed named species. We explored the impact of parameters such as sample age, DNA concentration and quality, read depth and fragment length on plastid assembly error. We also tested the efficacy of DNA sequence information for identifying plant samples using 45 specimens recently collected in the Pilbara. Results Genome skimming was effective at producing genomic information at large scale. Substantial sequence information on the chloroplast genome was obtained from 96.1% of samples, and complete or near-complete sequences of the nuclear ribosomal RNA gene repeat were obtained from 93.3% of samples. We were able to extract sequences for the core DNA barcode regions rbcL and matK from 96 to 93.3% of samples, respectively. Read quality and DNA fragment length had significant effects on sequencing outcomes and error correction of reads proved essential. Assembly problems were specific to certain taxa with low GC and high repeat content ( Goodenia , Scaevola , Cyperus , Bulbostylis , Fimbristylis ) suggesting biological rather than technical explanations. The structure of related genomes was needed to guide the assembly of repeats that exceeded the read length. DNA-based matching proved highly effective and showed that the efficacy for species identification declined in the order cpDNA >> rDNA >  matK  >>  rbcL. Conclusions We showed that a large-scale approach to genome sequencing using herbarium specimens produces high-quality complete cpDNA and rDNA sequences as a source of data for DNA barcoding and phylogenomics.