arXiv AI

Automated identification of Ichneumonoidea wasps via YOLO-based deep learning: Integrating HiresCam for Explainable AI

arXiv:2603. 16351v2 Announce Type: replace-cross Abstract: Accurate taxonomic identification of parasitoid wasps within the superfamily Ichneumonoidea is essential for biodiversity assessment, ecological monitoring, and biological control programs.

arXiv Machine Learning

Sep 22

Vision Transformers versus convolutional neural networks for fine-grained orchid genus identification in a species-rich, data-poor flora: a controlled benchmark on the Orchidaceae of New Guinea

The study benchmarks Vision Transformers (ViTs) against convolutional neural networks (CNNs) for fine‑grained orchid genus identification in New Guinea’s species‑rich, data‑poor flora. Using a two‑stage system that first predicts genus and then retrieves similar species images, the authors fine‑tuned four pretrained backbones on 16,701 photographs from 120 genera and 1,350 species. The self‑supervised ViT DINOv2 achieved the highest genus accuracy (macro top‑1 66.9 %) and outperformed both CNNs and a domain‑matched pretrained ViT, demonstrating strong species retrieval and open‑set detection capabilities.

By Reza Saputra, Diah Harnoni Apriyanti, Andr\'e Schuiteman, Kurt Metzger, Ashley Field, Katharina Nargar, William Edwards

llms rag fine-tuning benchmarks

arXiv Machine Learning

Jun 19

A High-Resolution Landscape Dataset for Concept-Based XAI With Application to Species Distribution Models

arXiv:2604. 13240v2 Announce Type: replace-cross Abstract: Mapping the spatial distribution of species is essential for conservation policy and invasive species management.

By Augustin de la Brosse, Damien Garreau, Thomas Houet, Thomas Corpetti

llms computer-vision robotics

arXiv AI

Jun 29

Explainable AI for Biodiversity Monitoring and Ecological Image Analysis

arXiv:2606. 27667v1 Announce Type: cross Abstract: Artificial intelligence is transforming biodiversity monitoring by enabling automated analysis of ecological imagery collected from camera traps, drones, satellites, underwater platforms, and other sensing systems.

By Brinnae Bent, Holly R. Houliston, Jiayi Zhou, G\"unel Aghakishiyeva, David W. Johnston

computer-vision

arXiv Machine Learning

Jun 25

Taxonomy-aware deep learning for hierarchical marine species classification in underwater imagery

arXiv:2606. 25989v1 Announce Type: cross Abstract: Automated classification of marine species from underwater imagery is essential for scalable ocean biodiversity monitoring and conservation policy.

By Dan Zimmerman, Dimitris A. Pados, George Sklivanitis

arXiv Computer Vision

Sep 22

Enhancing Shrimp Disease Detection via Deep Learning and Data Refinement for Resilient Aquaculture

arXiv:2609.23397v1 Announce Type: new Abstract: Shrimp diseases continue to cause devastating losses in the aquaculture industry, driving a critical need for robust, automated detection. This work co...

By Vinh Canh-Thanh Truong, Hai-Binh Pham, Ngoc Hong Tran

llms fine-tuning

arXiv AI

Jul 31

Multimodal fusion of visual and morphometric features for avian bone classification

arXiv:2607. 26743v1 Announce Type: cross Abstract: Artificial intelligence has shown considerable potential for archaeological applications, yet its use in zooarchaeology remains limited, particularly for the identification of avian skeletal remains.

By Nevio Dubbini, Lisa Yeomans, Marco Pavia, Ramazan Parmaksiz, Ayse Atas Hooglugt, Gabriele Gattiglia, Beatrice Demarchi

multimodal

arXiv Machine Learning

Jun 26

Batch-Invariant Spectral Intelligence for Robust and Explainable Insect Authentication

arXiv:2606. 26757v1 Announce Type: new Abstract: Edible insects offer an efficient source of alternative protein, requiring less land, water and emitting less greenhouse gas than conventional livestock.

By Majharulislam Babor, Giacomo Rossi, Annalisa Altavilla, Oliver Schl\"uter, Marina M. -C. H\"ohne

fine-tuning safety

arXiv Machine Learning

Aug 31

Empowering Local Agriculture: A Deep Learning-Powered Web System for Identifying Bangladeshi Mango Varieties

arXiv:2608.28161v1 Announce Type: cross Abstract: Mango variety identification in Bangladesh is challenging because closely related cultivars can have similar visual characteristics and images are of...

By Monowar Islam, Safaruzzaman Shovo

fine-tuning

arXiv Computer Vision

Sep 2

Degradation-based augmented training for robust individual animal re-identification

arXiv:2603.04163v2 Announce Type: replace Abstract: Wildlife re-identification aims to recognise individual animals by matching query images to a database of previously identified individuals, based...

By Thanos Polychronou, Luk\'a\v{s} Adam, Viktor Penchev, Kostas Papafitsoros

rag benchmarks

arXiv Computer Vision

Sep 1

Camera trap classification with deep learning under ground truth uncertainty

arXiv:2608.30789v1 Announce Type: new Abstract: Supervised deep learning methods enable the rapid processing of ecological image data, but depend on a costly annotation process. Consequently, trainin...

By Leonard Hockerts, Peter S. Stewart, Sarthak Arora, Tiffany J. Vlaar

arXiv AI

Aug 11

TomaMMU: A Comprehensive Multimodal Understanding Benchmark for Tomato Leaf Diseases

arXiv:2608. 08727v1 Announce Type: cross Abstract: To address this gap, we introduce TomaMMU, a large-scale Tomato leaf disease MultiModal Understanding dataset, alongside TomaBench, a benchmark for evaluating VLMs on tomato disease understanding.

By Gia-Han Truong, Khang Nguyen Quoc, Luyl-Da Quach

fine-tuning multimodal benchmarks

arXiv Computer Vision

Aug 24

A Dataset-Centric Benchmark of Deep Learning Methods for Grape Leaf Disease Classification and Detection

The paper introduces a dataset‑centric benchmark for deep learning approaches to grape leaf disease classification and detection. It evaluates publicly available datasets on disease categories, annotations, acquisition conditions, and class distributions, and tests representative models across image‑level classification, region‑level classification, and object detection. Results reveal high accuracy on controlled datasets but significant performance drops on heterogeneous, real‑world data, especially in cross‑dataset transfer and object detection tasks.

By Petar Canoski, Vlatko Spasev, Ivica Dimitrovski, Ivan Kitanovski, Petre Lameski

computer-vision benchmarks

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