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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.

Accurate and automatic identification of tree species information at the individual tree scale is of great significance for fine-scale investigation and management of forest resources and scientific assessment of forest ecosystems. Despite the fact that numerous studies have been conducted on the delineation of individual tree crown and species classification using drone high-resolution red, green and blue (RGB) images, and Light Detection and Ranging (LiDAR) data, performing the above tasks simultaneously has rarely been explored, especially in complex forest environments. In this study, we improve upon the state of the Mask region-based convolution neural network (Mask R-CNN) with our proposed attention complementary network (ACNet) and edge detection R-CNN (ACE R-CNN) for individual tree species identification in high-density and complex forest environments. First, we propose ACNet as the feature extraction backbone network to fuse the weighted features extracted from RGB images and canopy height model (CHM) data through an attention complementary module, which is able to selectively fuse weighted features extracted from RGB and CHM data at different scales, and enables the network to focus on more effective information. Second, edge loss is added to the loss function to improve the edge accuracy of the segmentation, which is calculated through the edge detection filter introduced in the Mask branch of Mask R-CNN. We demonstrate the performance of ACE R-CNN for individual tree species identification in three experimental areas of different tree species in southern China with precision (P), recall (R), F1-score, and average precision (AP) above 0.9. Our proposed ACNet–the backbone network for feature extraction–has better performance in individual tree species identification compared with the ResNet50-FPN (feature pyramid network). The addition of the edge loss obtained by the Sobel filter further improves the identification accuracy of individual tree species and accelerates the convergence speed of the model training. This work demonstrates the improved performance of ACE R-CNN for individual tree species identification and provides a new solution for tree-level species identification in complex forest environments, which can support carbon stock estimation and biodiversity assessment.

Background Species in genus Amomum always have important medicinal and economic values. Classification of Amomum using morphological characters has long been a challenge because they exhibit high similarity. The main goals of this study were to mine genetic markers from cp genomes for Amomum species identification and discover their evolutionary history through comparative analysis. Results Three species Amomum villosum , Amomum maximum and Amomum longipetiolatum were sequenced and annotated for the complete chloroplast (cp) genomes, and the cp genomes of A. longipetiolatum and A. maximum were the first reported. Three cp genomes exhibited typical quadripartite structures with 163,269-163,591 bp in length. Each genome encodes 130 functional genes including 79 protein-coding, 26 tRNAs and 3 rRNAs genes. 113-152 SSRs and 99 long repeats were identified in the three cp genomes. By designing specific primers, we amplified the highly variable loci and the mined genetic marker ccs A exhibited a relatively high species identification resolution in Amomum . The nonsynonymous and synonymous substitution ratios (Ka/Ks) in Amomum and Alpinia showed that most genes were subjected to a purifying selection. Phylogenetic analysis revealed the evolutionary relationships of Amomum and Alpinia species and proved that Amomum is paraphyletic. In addition, the sequenced sample of A. villosum was found to be a hybrid, becoming the first report of natural hybridization of this genus. Meanwhile, the high-throughput sequencing-based ITS2 analysis was proved to be an efficient tool for interspecific hybrid identification and with the help of the chloroplast genome, the hybrid parents can be also be determined. Conclusion The comparative analysis and mined genetic markers of cp genomes were conducive to species identification and evolutionary relationships of Amomum .

Deep learning algorithms classify plant species with high accuracy, and smartphone applications leverage this technology to enable users to identify plant species in the field. The question we address here is whether such crowd‐sourced data contain substantial macroecological information. In particular, we aim to understand if we can detect known environmental gradients shaping plant co‐occurrences. In this study we analysed 1 million data points collected through the use of the mobile app Flora Incognita between 2018 and 2019 in Germany and compared them with Florkart, containing plant occurrence data collected by more than 5000 floristic experts over a 70‐year period. The direct comparison of the two data sets reveals that the crowd‐sourced data particularly undersample areas of low population density. However, using nonlinear dimensionality reduction we were able to uncover macroecological patterns in both data sets that correspond well to each other. Mean annual temperature, temperature seasonality and wind dynamics as well as soil water content and soil texture represent the most important gradients shaping species composition in both data collections. Our analysis describes one way of how automated species identification could soon enable near real‐time monitoring of macroecological patterns and their changes, but also discusses biases that must be carefully considered before crowd‐sourced biodiversity data can effectively guide conservation measures.

Automatic and accurate individual tree species identification is essential for the realization of smart forestry. Although existing studies have used unmanned aerial vehicle (UAV) remote sensing data for individual tree species identification, the effects of different spatial resolutions and combining multi-source remote sensing data for automatic individual tree species identification using deep learning methods still require further exploration, especially in complex forest conditions. Therefore, this study proposed an improved YOLOv8 model for individual tree species identification using multisource remote sensing data under complex forest stand conditions. Firstly, the RGB and LiDAR data of natural coniferous and broad-leaved mixed forests under complex conditions in Northeast China were acquired via a UAV. Then, different spatial resolutions, scales, and band combinations of multisource remote sensing data were explored, based on the YOLOv8 model for tree species identification. Subsequently, the Attention Multi-level Fusion (AMF) Gather-and-Distribute (GD) YOLOv8 model was proposed, according to the characteristics of the multisource remote sensing forest data, in which the two branches of the AMF Net backbone were able to extract and fuse features from multisource remote sensing data sources separately. Meanwhile, the GD mechanism was introduced into the neck of the model, in order to fully utilize the extracted features of the main trunk and complete the identification of eight individual tree species in the study area. The results showed that the YOLOv8x model based on RGB images combined with current mainstream object detection algorithms achieved the highest mAP of 75.3%. When the spatial resolution was within 8 cm, the accuracy of individual tree species identification exhibited only a slight variation. However, the accuracy decreased significantly with the decrease of spatial resolution when the resolution was greater than 15 cm. The identification results of different YOLOv8 scales showed that x, l, and m scales could exhibit higher accuracy compared with other scales. The DGB and PCA-D band combinations were superior to other band combinations for individual tree identification, with mAP of 75.5% and 76.2%, respectively. The proposed AMF GD YOLOv8 model had a more significant improvement in tree species identification accuracy than a single remote sensing sources and band combinations data, with a mAP of 81.0%. The study results clarified the impact of spatial resolution on individual tree species identification and demonstrated the excellent performance of the proposed AMF GD YOLOv8 model in individual tree species identification, which provides a new solution and technical reference for forestry resource investigation combined multisource remote sensing data.

Identifying tree species from the air has long been desired for forest management. Recently, combination of UAV RGB image and deep learning has shown high performance for tree identification in limited conditions. In this study, we evaluated the practicality and robustness of the tree identification system using UAVs and deep learning. We sampled training and test data from three sites in temperate forests in Japan. The objective tree species ranged across 56 species, including dead trees and gaps. When we evaluated the model performance on the dataset obtained from the same time and same tree crowns as the training dataset, it yielded a Kappa score of 0.97, and 0.72, respectively, for the performance on the dataset obtained from the same time but with different tree crowns. When we evaluated the dataset obtained from different times and sites from the training dataset, which is the same condition as the practical one, the Kappa scores decreased to 0.47. Though coniferous trees and representative species of stands showed a certain stable performance regarding identification, some misclassifications occurred between: (1) trees that belong to phylogenetically close species, (2) tree species with similar leaf shapes, and (3) tree species that prefer the same environment. Furthermore, tree types such as coniferous and broadleaved or evergreen and deciduous do not always guarantee common features between the different trees belonging to the tree type. Our findings promote the practicalization of identification systems using UAV RGB images and deep learning.

The objective of this study was to verify the accuracy of tree species identification using deep learning with leaf images of broadleaf and coniferous trees in outdoor photographs. For each of 12 broadleaf and eight coniferous tree species, we acquired 300 photographs of leaves and used those to produce 72,000 256 × 256-pixel images. We used Caffe as the deep learning framework and AlexNet and GoogLeNet as the deep learning algorithms. We constructed four learning models that combined two learning patterns: one for individual classification of 20 species and the other for two-group classification (broadleaf vs. coniferous trees), with and without data augmentation, respectively. The performance of the proposed model was evaluated according to the MCC and F-score. Both classification models exhibited very high accuracy for all learning patterns; the highest MCC was 0.997 for GoogLeNet with data augmentation. The classification accuracy was higher for broadleaf trees when the model was trained using broadleaf only; for coniferous trees, the classification accuracy was higher when the model was trained using both tree types simultaneously than when it was trained using coniferous trees only.

Oil pollutants pose significant threats to marine and terrestrial ecosystems, necessitating the effective methods of oil species identification for the emergence responses of oil spill incidents. This study employs the excitation-emission matrix (EEM) fluorescence spectroscopy to capture and analyse the spectral characteristics of various oil species at different thicknesses. Some data augmentation techniques, including data smoothing and denoising, are introduced in this study to expand the dataset and enhance data quality. The methodology of transfer learning, which significantly reduces training time and improves model accuracy by sharing parameters, is adopted in this study. The enhancement of transfer learning method is examined using several typical deep learning networks. It is found that the implementation of transfer learning not only reduces the number of trainable parameters, but also improves identification accuracies by leveraging shared parameters, which makes it more efficient and accurate than building models from scratch. The proposed methodology enhances the capability of identifying petroleum pollutants using deep learning method and provides a new perspective on the advancement of oil spill monitoring technology.

Background The identification of tropical African wood species based on microscopic imagery is a challenging problem due to the heterogeneous nature of the composition of wood combined with the vast number of candidate species. Image classification methods that rely on machine learning can facilitate this identification, provided that sufficient training material is available. Despite the fact that the three main anatomical sections contain information that is relevant for species identification, current methods only rely on transverse sections. Additionally, commonly used procedures for evaluating the performance of these methods neglect the fact that multiple images often originate from the same tree, leading to an overly optimistic estimate of the performance. Results We introduce a new image dataset containing microscopic images of the three main anatomical sections of 77 Congolese wood species. A dedicated multi-view image classification method is developed and obtains an accuracy (computed using the naive but common approach) of 95%, outperforming the single-view methods by a large margin. An in-depth analysis shows that naive accuracy estimates can lead to a dramatic over-prediction, of up to 60%, of the accuracy. Conclusions Additional images from non-transverse sections can boost the performance of machine-learning-based wood species identification methods. Additionally, care should be taken when evaluating the performance of machine-learning-based wood species identification methods to avoid an overestimation of the performance.

Insect pests significantly impact global agricultural productivity and crop quality. Effective integrated pest management strategies require the identification of insects, including beneficial and harmful insects. Automated identification of insects under real-world conditions presents several challenges, including the need to handle intraspecies dissimilarity and interspecies similarity, life-cycle stages, camouflage, diverse imaging conditions, and variability in insect orientation. An end-to-end approach for training deep-learning models, InsectNet, is proposed to address these challenges. Our approach has the following key features: (i) uses a large dataset of insect images collected through citizen science along with label-free self-supervised learning to train a global model, (ii) fine-tuning this global model using smaller, expert-verified regional datasets to create a local insect identification model, (iii) which provides high prediction accuracy even for species with small sample sizes, (iv) is designed to enhance model trustworthiness, and (v) democratizes access through streamlined machine learning operations. This global-to-local model strategy offers a more scalable and economically viable solution for implementing advanced insect identification systems across diverse agricultural ecosystems. We report accurate identification (>96% accuracy) of numerous agriculturally and ecologically relevant insect species, including pollinators, parasitoids, predators, and harmful insects. InsectNet provides fine-grained insect species identification, works effectively in challenging backgrounds, and avoids making predictions when uncertain, increasing its utility and trustworthiness. The model and associated workflows are available through a web-based portal accessible through a computer or mobile device. We envision InsectNet to complement existing approaches, and be part of a growing suite of AI technologies for addressing agricultural challenges.