Edge-Optimized Deep Learning Architectures for Classification of Agricultural Insects with Mobile Deployment by Muhammad Hannan Akhtar, Ibrahim Eksheir, Tamer Shanableh

“…The quantized model produced using Post-Training Quantization was able to maintain a classification accuracy of 59.6% while also reducing the model size from 33 MB to 9.6 MB, thus demonstrating that our solution maintains a competitive performance across a broader range of insect classes.…”
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A deep learning pipeline for time-lapse camera monitoring of insects and their floral environments by Kim Bjerge, Henrik Karstoft, Hjalte M.R. Mann, Toke T. Høye

“…Arthropods, including insects, represent the most diverse group and contribute significantly to animal biomass. …”
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Comparison of Two Different DNA Isolation Methods (Quick & Dirty vs. Commercial Kit) in Small Insects by Ferhat Kiremit, Fulya Kaya Apak, Hayrettin İlker Özdemir, Gül Ayar

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Development and evaluation of an automated classification and counting system for rice planthoppers captured on survey boards by Toshihisa Yashiro, Tomohiko Takayama, Ryo Sugiura, Masaya Matsumura, Sachiyo Sanada-Morimura

“…Abstract Rice planthoppers are the most economically important insect pests of rice in Asia. …”
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Are Insect-Based Foods Healthy? An Evaluation of the Products Sold in European E-Commerce by Emma Copelotti, Filippo Fratini, Giulia Sforza, Tiziano Tuccinardi, Gian Carlo Demontis, Simone Mancini

“…The predominant market was based in Western Europe (55.8%), and 24 insect species were sold. Notably, four species were the most representative: <i>Tenebrio molitor</i> (182 products), followed by <i>Acheta domesticus</i> (140), <i>Alphitobius diaperinus</i> (54), and <i>Locusta migratoria</i> (34). …”
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Information about nutritional aspects of edible insects: Perspectives across different European geographies by Raquel P. F. Guiné, Sofia G. Florença, Cristina A. Costa, Paula M. R. Correia, Manuela Ferreira, Ana P. Cardoso, Sofia Campos, Ofélia Anjos, Elena Bartkiene, Marijana Matek Sarić

“…The results further highlighted that the participants were better informed about the high protein content of EIs, while not being well informed about their possible anti-nutritive effects. Tree classification revealed that the most important discriminating variable was country, with Lithuanian participants being better informed than those from Portugal or Croatia.…”
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Endless forms most wonderful: Four new cavernicolous planthopper species (Hemiptera, Fulgoromorpha, Cixiidae and Meenoplidae) from the Canary Islands by Hannelore Hoch, Heriberto López, Manuel Naranjo, Dora Aguín-Pombo, Pedro Oromí

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Classification of Biological Scatters Using Polarimetric Weather Radar by Cheng Hu, Zhuoran Sun, Kai Cui, Huafeng Mao, Rui Wang, Xiao Kou, Dongli Wu, Fan Xia

“…Next, point features and spatial texture features were extracted from the radar images in the dataset for training a classifier using a supervised learning approach, resulting in a classification accuracy of 93.56&#x0025;. Furthermore, the importance of the features was analyzed, uncovering that the most influential attribute was the reflectivity factor at 33.83&#x0025;, surpassing the cumulative influence of other dual-polarization moments. …”
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Effects of species and sex on the gut microbiome of four laboratory-reared fruit fly lines (Diptera: Tephritidae) using full-length 16S rRNA PacBio Kinnex sequencing by Sayaka Aoki, Mikinley Weaver, Tyler J. Simmonds, Ikkei Shikano, Scott M. Geib, Charles J. Mason

“…The use of near full-length 16S rRNA sequencing did not have a marked improvement in beta-diversity interpretation over V4 subunit, with most detected taxa matching those described from other tephritids, but did allow for improved taxonomic classification at the genus level. …”
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Machine learning for automated electrical penetration graph analysis of aphid feeding behavior: Accelerating research on insect-plant interactions. by Quang Dung Dinh, Daniel Kunk, Truong Son Hy, Vamsi Nalam, Phuong D Dao

“…The electrical penetration graph (EPG) is a well-known technique that provides insights into the feeding behavior of insects with piercing-sucking mouthparts, mostly hemipterans. …”
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The lost generation of Pemphigus populiglobuli (Hemiptera, Aphididae): exploring the taxonomy of the Svalbard aphids of genus Pemphigus by Karina Wieczorek, Dominik Chłond, Emmanuelle Jousselin, Stephen J. Coulson

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Automatic Paddy Planthopper Detection and Counting Using Faster R-CNN by Siti Khairunniza-Bejo, Mohd Firdaus Ibrahim, Marsyita Hanafi, Mahirah Jahari, Fathinul Syahir Ahmad Saad, Mohammad Aufa Mhd Bookeri

“…It utilises annotated datasets obtained from sticky light traps, comprising 1654 images across four distinct classes of planthoppers and one class of benign insects. The datasets were subjected to data augmentation and utilised to train four convolutional object detection models based on transfer learning. …”
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Virulence and genome analysis of baculovirus isolates from different Lymantria dispar populations by Y. Akhanaev, S. Pavlushin, M. Yakimova, D. Lebedeva, D. Kurenshchikov, Z. Teshebaeva, S. Toshchakov, Y. Vyatkin, A. Kononchuk, A. Ignatieva, Y. Tokarev, V. Soukhovolsky, N. M. Inoue, V. Martemyanov

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Phylogenomics of palynophagous pine cone weevils (Coleoptera: Cimberididae) recovers the monophyly of Cimberidini and Doydirhynchini and reveals the paraphyly of Cimberis by Duane D. MCKENNA, Brian D. FARRELL, Adriana E. MARVALDI, Rolf G. OBERPRIELER, Xuankun LI

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Identification and Characterization of bZIP Gene Family Combined Transcriptome Analysis Revealed Their Functional Roles on Abiotic Stress and Anthocyanin Biosynthesis in Mulberry (... by Qinghua Liu, Haowen Fang, Hong Zhou, Xiling Wang, Zhiwei Hou

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Exploring the Biology of Quasi-Social Idiobiont Parasitoids in the Genus <i>Sclerodermus</i> (Hymenoptera: Bethylidae) by Serena Malabusini, Daniela Lupi

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Insight into the Phylogenetic Relationships of Phasmatodea and Selection Pressure Analysis of <i>Phraortes liaoningensis</i> Chen & He, 1991 (Phasmatodea: Lonchodidae) Using Mitoge... by Yuxin Chen, Yani Yuan, Wenhui Yang, Kenneth B. Storey, Jiayong Zhang, Danna Yu

“…Stick and leaf insects are a group among the Insecta that are famous for their extraordinary mimicry ability. …”
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Molecular Phylogeny of the Subfamily Notodontinae (Lepidoptera: Noctuoidea: Notodontidae) by Muyu Guo, Qingliu Geng, Dandan Zhang

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Precise Crop Pest Detection Based on Co-Ordinate-Attention-Based Feature Pyramid Module by Chenrui Kang, Lin Jiao, Kang Liu, Zhigui Liu, Rujing Wang

“…Therefore, to overcome these limitations, we first designed a co-ordinate-attention-based feature pyramid network, termed CAFPN, to extract the salient visual features that distinguish small insects from each other. Subsequently, in the network training stage, a dynamic sample selection strategy using positive and negative weight functions, which considers both high classification scores and precise localization, was introduced. …”
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New Mitogenomes from the Genus <i>Ablabesmyia</i> (Diptera: Chironomidae, Tanypodiinae): Characterization and Phylogenetic Implications by Wen-Bin Liu, Wen-Xuan Pei, Ya-Ning Tang, Jia-Xin Nie, Wei Cao, Cheng-Yan Wang, Chun-Cai Yan

“…(1) Background: The insect mitogenome encodes essential genetic components and serves as an effective marker for molecular identification and phylogenetic analysis in insects due to its small size, maternal inheritance, and rapid evolution. …”
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