Pine nuts are not only the important agent of pine reproduction and afforestation, but also the commonly consumed nut with high nutritive values. However, it is difficult to distinguish among pine nuts due to the morphological similarity among species. Therefore, it is important to improve the quality of pine nuts and solve the adulteration problem quickly and non-destructively. In this study, seven pine nuts (Pinus bungeana,Pinus yunnanensis,Pinus thunbergii,Pinus armandii,Pinus massoniana,Pinus elliottiiandPinus taiwanensis) were used as study species. 210 near-infrared (NIR) spectra were collected from the seven species of pine nuts, five machine learning methods (Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Naive Bayes (NB)) were used to identify species of pine nuts. 303 images were used to collect morphological data to construct a classification model based on five convolutional neural network (CNN) models (VGG16, VGG19, Xception, InceptionV3 and ResNet50). The experimental results of NIR spectroscopy show the best classification model is MLP and the accuracy is closed to 0.99. Another experimental result of images shows the best classification model is InceptionV3 and the accuracy is closed to 0.964. Four important range of wavebands, 951–957 nm, 1,147–1,154 nm, 1,907–1,927 nm, 2,227–2,254 nm, were found to be highly related to the classification of pine nuts. This study shows that machine learning is effective for the classification of pine nuts, providing solutions and scientific methods for rapid, non-destructive and accurate classification of different species of pine nuts.

Pines are widely distributed in the Northern Hemisphere and have a long evolutionary history. The availability of transcriptome data has facilitated comparative transcriptomics for studying the evolutionary patterns associated with the different geographical distributions of species in the Pinus phylogeny. The transcriptome of Pinus kesiya var. langbianensis was sequenced using the Illumina HiSeq 2000 platform, and a total of 68,881 unigenes were assembled by Trinity. Transcriptome sequences of another 12 conifer species were downloaded from public databases. All of the pairwise orthologues were identified by comparative transcriptome analysis in 13 conifer species, from which the rate of diversification was calculated and a phylogenetic tree inferred. All of the fast-evolving positive selection sequences were identified, and some salt-, drought-, and abscisic acid-resistance genes were discovered. mRNA sequences of P. kesiya var. langbianensis were obtained by transcriptome sequencing, and a large number of simple sequence repeat and short nucleotide polymorphism loci were detected. These data can be used in molecular marker-assisted selected in pine breeding. Divergence times were estimated in the 13 conifer species using comparative transcriptomic analysis. A number of positive selection genes were found to be related to environmental factors. Salt- and abscisic acid-related genes exhibited different selection patterns between coastal and inland Pinus. Our findings help elucidate speciation patterns in the Pinus lineage.

Wood recognition is necessary for work in the wood trade activities. The advantage of the one-class wood classification method is more generalization, and it only needs positive samples and does not need negative samples in the training phase, so it is suitable for rare wood species inspection. This paper proposed an improved method based on the one-class support vector machine (OCSVM) for wood species recognition. It uses cross-section images acquired with a magnifying glass, which uses a pre-trained VGG16 model for feature extraction, a normal distribution test for key features filtering, and OCSVM to determine the wood species. The results showed that the approach achieved a mean recall of 0.842 for both positive and negative samples, which indicates this method has good performance for wood recognition. In a negative public dataset, the negative recall reached as high as 0.989, which showed that this method has good generalization.

Willows are widely distributed in the northern hemisphere and have good adaptability to different living environment. The increasing of genome and transcriptome data provides a chance for comparative analysis to study the evolution patterns with the different origin and geographical distributions in the Salix phylogeny. Transcript sequences of 10 Salicaceae species were downloaded from public databases. All pairwise of orthologues were identified by comparative analysis in these species, from which we constructed a phylogenetic tree and estimated the rate of diverse. Divergence times were estimated in the 10 Salicaceae using comparative transcriptomic analysis. All of the fast-evolving positive selection sequences were identified, and some cold-, drought-, light-, universal-, and heat- resistance genes were discovered. The divergence time of subgenus Vetrix and Salix was about 17.6–16.0 Mya during the period of Middle Miocene Climate Transition (21–14 Mya). Subgenus Vetrix diverged to migratory and resident groups when the climate changed to the cool and dry trend by 14 Mya. Cold- and light- stress genes were involved in positive selection among the resident Vetrix, and which would help them to adapt the cooling stage. Universal- stress genes exhibited positive selection among the migratory group and subgenus Salix. These data are useful for comprehending the adaptive evolution and speciation in the Salix lineage.

Tomicus yunnanensis Kirkendall (Coleoptera: Scolytinae) is a stem-boring pest that endangers Pinus yunnanensis Franch (Pinales:Pinoideae), which seriously affects the ecological environment safety in southwest China. In order to understand the potential distribution pattern and change in the potential distribution of P. yunnanensis and T. yunnanensis, this study used the maximum entropy model to predict the distribution of potentially suitable areas for P. yunnanensis and T. yunnanensis and explored the relationships between their different spatiotemporal distributions based on change analysis. The experimental results show that altitude is the main factor restricting the current distribution of P. yunnanensis. The current suitable areas of P. yunnanensis are mainly distributed in Yunnan, Sichuan and Guizhou. The minimum temperature of the coldest month is the main factor affecting the current distribution of T. yunnanensis. The current suitable areas of T. yunnanensis are mainly distributed in Yunnan, Sichuan and Tibet. Under future climate scenarios, the total suitable areas of P. yunnanensis and T. yunnanensis are expected to increase. The suitable areas tend to move to higher altitudes in the west and higher latitudes in the north. At the same time, this study finds that there is an obvious bottleneck of expansion to northeastern Sichuan near the Daba Mountains. The results of intersection analysis showed that, with future climate change, P. yunnanensis and T. yunnanensis mainly showed lowly suitable (or unsuitable)—lowly suitable (or unsuitable) to moderately (or highly) suitable—and moderate (or high) variation patterns of suitable areas under the SSP1-2.6 climate scenario. These results will provide an important basis for the breeding of P. yunnanensis and controlling T. yunnanensis.

Birds play a vital and indispensable role in biodiversity and environmental conservation. Protecting bird diversity is crucial for maintaining the balance of nature, promoting ecosystem health, and ensuring sustainable development. The Broad Learning System (BLS) exhibits an excellent ability to extract highly discriminative features from raw inputs and construct complex feature representations by combining feature nodes and enhancement nodes, thereby enabling effective recognition and classification of various birdsongs. However, within the BLS, the selection of feature nodes and enhancement nodes assumes critical significance, yet the model lacks the capability to identify high quality network nodes. To address this issue, this paper proposes a novel method that introduces residual blocks and Mutual Similarity Criterion (MSC) layers into BLS to form an improved BLS (RMSC-BLS), which makes it easier for BLS to automatically select optimal features related to output. Experimental results demonstrate the accuracy of the RMSC-BLS model for the three construction features of MFCC, $d_{MFCC}$, and $d_{squence}$ is 78.85%, 79.29%, and 92.37%, respectively, which is 4.08%, 4.50%, and 2.38% higher than that of original BLS model. In addition, compared with other models, our RMSC-BLS model shows superior recognition performance, has higher stability and better generalization ability, and provides an effective solution for birdsong recognition.