Background Rapid identification and classification of bats are critical for practical applications. However, species identification of bats is a typically detrimental and time-consuming manual task that depends on taxonomists and well-trained experts. Deep Convolutional Neural Networks (DCNNs) provide a practical approach for the extraction of the visual features and classification of objects, with potential application for bat classification. Results In this study, we investigated the capability of deep learning models to classify 7 horseshoe bat taxa (CHIROPTERA: Rhinolophus) from Southern China. We constructed an image dataset of 879 front, oblique, and lateral targeted facial images of live individuals collected during surveys between 2012 and 2021. All images were taken using a standard photograph protocol and setting aimed at enhancing the effectiveness of the DCNNs classification. The results demonstrated that our customized VGG16-CBAM model achieved up to 92.15% classification accuracy with better performance than other mainstream models. Furthermore, the Grad-CAM visualization reveals that the model pays more attention to the taxonomic key regions in the decision-making process, and these regions are often preferred by bat taxonomists for the classification of horseshoe bats, corroborating the validity of our methods. Conclusion Our finding will inspire further research on image-based automatic classification of chiropteran species for early detection and potential application in taxonomy.

Rapid identification and classification of bats are critical for practical applications. However, species identification of bats is a typically detrimental and time-consuming manual task that depends on taxonomists and well-trained experts. Deep Convolutional Neural Networks (DCNNs) provide a practical approach for the extraction of the visual features and classification of objects, with potential application for bat classification.

Rapid identification and classification of bats are critical for practical applications. However, species identification of bats is a typically detrimental and time-consuming manual task that depends on taxonomists and well-trained experts. Deep Convolutional Neural Networks (DCNNs) provide a practical approach for the extraction of the visual features and classification of objects, with potential application for bat classification. In this study, we investigated the capability of deep learning models to classify 7 horseshoe bat taxa (CHIROPTERA: Rhinolophus) from Southern China. We constructed an image dataset of 879 front, oblique, and lateral targeted facial images of live individuals collected during surveys between 2012 and 2021. All images were taken using a standard photograph protocol and setting aimed at enhancing the effectiveness of the DCNNs classification. The results demonstrated that our customized VGG16-CBAM model achieved up to 92.15

2022年7月和9月,本研究组在湖南开展翼手目多样性资源调查时,于常德市桃源县、湘西州保靖县、永州市双牌县和邵阳市绥宁县使用蝙蝠竖琴网分别采集到9只鼠耳蝠(7(♂),2♀)和5只管鼻蝠(2(♂),3♀),结合传统形态学和分子系统发育学方法对上述标本进行物种鉴定.该批鼠耳蝠体型中等,前臂长43.41～50.89 mm,全身毛发呈红褐色,耳廓端部、鼻孔周围、五趾与爪及尾末端均呈黑褐色,翼膜掌指间有三角形状的红褐色斑直达翼缘;头骨顶部平缓,颧弓发达,脑颅高而凸显,其形态特征和度量数据均与渡濑氏鼠耳蝠(Myotis rufiniger)相符.同时,采集的管鼻蝠标本体型中等,前臂长33.83～37.53 mm,背毛棕褐色,腹毛呈浅黄色;颅全长16.74～17.29 mm,颧弓平直而宽厚,其外形特征和度量数据均与中管鼻蝠(Murina huttoni)相符.基于Cyt b和COI基因构建的系统发育树均支持以上形态学鉴定结果.该报道为渡濑氏鼠耳蝠和中管鼻蝠在湖南省分布的新发现.标本现存于广州大学华南生物多样性保护与利用重点实验室,其中渡濑氏鼠耳蝠标本号为GZHU 22429、GZHU 22430、GZHU 22459、GZHU 22523、GZHU 22961、GZHU 22962、GZHUhun22011、GZHU 22549、GZHU 22703;中管鼻蝠标本号为 GZHU hun22001、GZHU hun22008、GZHU 22431、GZHU 22437、GZHUhun22015.

为摸清安徽鹞落坪国家级自然保护区生物多样性状况,于2021年9月在该保护区使用蝙蝠竖琴网采集到一批蝙蝠标本,其中3只蝙蝠(1♂2♀)中等体型,耳廓圆滑,耳屏尖长,鼻孔呈管状向外延伸.背毛长而蓬松,基部灰黑色,逐渐过渡为棕褐色;腹毛稍浅,毛基部棕灰色,毛尖过渡至灰白色.脑颅较平,矢状嵴与人字嵴可见,颧弓平直;齿式为2.1.2.3/3.1.2.3=34.上述特征与中管鼻蝠Murina huttoni的外形及头骨特征相符.基于线粒体COⅠ基因和cyt b基因构建的系统发育树也支持该鉴定结果.本次中管鼻蝠的发现为安徽省翼手目Chiroptera分布新记录.

Bats are a crucial component within ecosystems, providing valuable ecosystem services such as pollination and pest control. In practical conservation efforts, the classification and identification of bats are essential in order to develop effective conservation management programs for bats and their habitats. Traditionally, the identification of bats has been a manual and time-consuming process. With the development of artificial intelligence technology, the accuracy and speed of identification work of such fine-grained images as bats identification can be greatly improved. Bats identification relies on the fine features of their beaks and faces, so mining the fine-grained information in images is crucial to improve the accuracy of bats identification. This paper presents a deep learning-based model designed for the rapid and precise identification of common horseshoe bats (Chiroptera: Rhinolophidae: Rhinolophus) from Southern China. The model was developed by utilizing a comprehensive dataset of 883 high-resolution images of seven distinct Rhinolophus species which were collected during surveys conducted between 2010 and 2022. An improved EfficientNet model with an attention mechanism module is architected to mine the fine-grained appearance of these Rhinolophus. The performance of the model beat other classical models, including SqueezeNet, AlexNet, VGG16_BN, ShuffleNetV2, GoogleNet, ResNet50 and EfficientNet_B0, according to the predicting precision, recall, accuracy, F1-score. Our model achieved the highest identification accuracy of 94.22% and an F1-score of 0.948 with low computational complexity. Heat maps obtained with Grad-CAM show that our model meets the identification criteria of the morphology of Rhinolophus. Our study highlights the potential of artificial intelligence technology for the identification of small mammals, and facilitating fast species identification in the future.