Paper List
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An AI Implementation Science Study to Improve Trustworthy Data in a Large Healthcare System
This paper addresses the critical gap between theoretical AI research and real-world clinical implementation by providing a practical framework for as...
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The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
This paper addresses the critical gap in cystic fibrosis exacerbation management by providing a formal causal framework that integrates expert knowled...
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Hierarchical Molecular Language Models (HMLMs)
This paper addresses the core challenge of accurately modeling context-dependent signaling, pathway cross-talk, and temporal dynamics across multiple ...
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Stability analysis of action potential generation using Markov models of voltage‑gated sodium channel isoforms
This work addresses the challenge of systematically characterizing how the high-dimensional parameter space of Markov models for different sodium chan...
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Approximate Bayesian Inference on Mechanisms of Network Growth and Evolution
This paper addresses the core challenge of inferring the relative contributions of multiple, simultaneous generative mechanisms in network formation w...
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EnzyCLIP: A Cross-Attention Dual Encoder Framework with Contrastive Learning for Predicting Enzyme Kinetic Constants
This paper addresses the core challenge of jointly predicting enzyme kinetic parameters (Kcat and Km) by modeling dynamic enzyme-substrate interaction...
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Tissue stress measurements with Bayesian Inversion Stress Microscopy
This paper addresses the core challenge of measuring absolute, tissue-scale mechanical stress without making assumptions about tissue rheology, which ...
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DeepFRI Demystified: Interpretability vs. Accuracy in AI Protein Function Prediction
This study addresses the critical gap between high predictive accuracy and biological interpretability in DeepFRI, revealing that the model often prio...
Real-time Cricket Sorting By Sex A low-cost embedded solution using YOLOv8 and Raspberry Pi
Illinois Institute of Technology, Chicago, Illinois | Universidad Politécnica de Madrid, Madrid, Spain
30秒速读
IN SHORT: This paper addresses the critical bottleneck in industrial insect farming: the lack of automated, real-time sex sorting systems for Acheta domesticus crickets, which is essential for selective breeding, optimized reproduction ratios, and nutritional differentiation.
核心创新
- Methodology First integrated system combining embedded computer vision (YOLOv8 nano) with physical actuation for real-time cricket sex sorting, achieving 86.8% overall sorting accuracy.
- Methodology Development of a custom balanced dataset (597 training images) with controlled illumination variability and data augmentation, achieving mAP@0.5 of 0.977 with minimal misclassification (2.3% for females, 2.7% for males).
- Biology Quantitative analysis revealing sex-dependent behavioral differences: males move faster (median 39.5 mm/s vs 18.8 mm/s for females) and are more sensitive to actuator noise, affecting sorting efficiency under different stress conditions.
主要结论
- The system achieved 86.8% overall sorting accuracy across four experiments, with performance varying from 83% under high-stress conditions to 94% under low-stress conditions.
- Movement speed significantly impacts sorting accuracy: misclassified crickets had median speeds of 75.0 mm/s compared to 20.3 mm/s for correctly classified individuals (p<0.05 based on distribution analysis).
- The YOLOv8 nano model demonstrated excellent detection performance with mAP@0.5 of 0.977 and class-wise precision/recall of 0.857-0.878, proving suitable for resource-constrained edge deployment.
摘要: The global demand for sustainable protein sources is driving increasing interest in edible insects, with Acheta domesticus (house cricket) identified as one of the most suitable species for industrial production. Current farming practices typically rear crickets in mixed-sex populations without automated sex sorting, despite potential benefits such as selective breeding, optimized reproduction ratios, and nutritional differentiation. This work presents a low-cost, real-time system for automated sex-based sorting of Acheta domesticus, combining computer vision and physical actuation. The device integrates a Raspberry Pi 5 with the official Raspberry AI Camera and a custom YOLOv8 nano object detection model, together with a servo-actuated sorting arm. The model reached a mean Average Precision at IoU 0.5 (mAP@0.5) of 0.977 during testing, and real-world experiments with groups of crickets achieved an overall sorting accuracy of 86.8%. These results demonstrate the feasibility of deploying lightweight deep learning models on resource-constrained devices for insect farming applications, offering a practical solution to improve efficiency and sustainability in cricket production.