# Deep learning powered real-time identification of insects using citizen science data

arXiv: https://arxiv.org/abs/2306.02507 · HTML: https://mlsanity.com/paper/2306.02507/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 42 references that are in the map.
- First posted on arXiv 4 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021)

## Built on by (most cited first, 8 of 8)

- [AgMMU: A Comprehensive Agricultural Multimodal Understanding Benchmark](https://mlsanity.com/paper/2504.10568/) (2025)
- [MIRAGE: A Benchmark for Multimodal Information-Seeking and Reasoning in Agricultural Expert-Guided Conversations](https://mlsanity.com/paper/2506.20100/) (2025)
- [WeedNet: A Foundation Model-Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification](https://mlsanity.com/paper/2505.18930/) (2025)
- [The Penalized Inverse Probability Measure for Conformal Classification](https://mlsanity.com/paper/2406.08884/) (2024)
- [Uncertainty Guarantees on Automated Precision Weeding using Conformal Prediction](https://mlsanity.com/paper/2501.07185/) (2025)
- [Multi-Sensor and Multi-temporal High-Throughput Phenotyping for Monitoring and Early Detection of Water-Limiting Stress in Soybean](https://mlsanity.com/paper/2402.18751/) (2024)
- [Robust soybean seed yield estimation using high-throughput ground robot videos](https://mlsanity.com/paper/2412.02642/) (2024)
- [CrypticBio: A Large Multimodal Dataset for Visually Confusing Biodiversity](https://mlsanity.com/paper/2505.14707/) (2025)

## Abstract (arXiv)

Insect-pests significantly impact global agricultural productivity and quality. Effective management involves identifying the full insect community, including beneficial insects and harmful pests, to develop and implement integrated pest management strategies. Automated identification of insects under real-world conditions presents several challenges, including differentiating similar-looking species, intra-species dissimilarity and inter-species similarity, several life cycle stages, camouflage, diverse imaging conditions, and variability in insect orientation. A deep-learning model, InsectNet, is proposed to address these challenges. InsectNet is endowed with five key features: (a) utilization of a large dataset of insect images collected through citizen science; (b) label-free self-supervised learning for large models; (c) improving prediction accuracy for species with a small sample size; (d) enhancing model trustworthiness; and (e) democratizing access through streamlined MLOps. This approach allows accurate identification (>96% accuracy) of over 2500 insect species, including pollinator (e.g., butterflies, bees), parasitoid (e.g., some wasps and flies), predator species (e.g., lady beetles, mantises, dragonflies) and harmful pest species (e.g., armyworms, cutworms, grasshoppers, stink bugs). InsectNet can identify invasive species, provide fine-grained insect species identification, and work effectively in challenging backgrounds. It also can abstain from making predictions when uncertain, facilitating seamless human intervention and making it a practical and trustworthy tool. InsectNet can guide citizen science data collection, especially for invasive species where early detection is crucial. Similar approaches may transform other agricultural challenges like disease detection and underscore the importance of data collection, particularly through citizen science efforts..