# On the Real-Time Semantic Segmentation of Aphid Clusters in the Wild

arXiv: https://arxiv.org/abs/2307.10267 · HTML: https://mlsanity.com/paper/2307.10267/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 17 Jul 2023.

## Builds on (most cited first, 13 of 13)

- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation](https://mlsanity.com/paper/2004.02147/) (2020)
- [ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation](https://mlsanity.com/paper/1606.02147/) (2016)
- [Fast-SCNN: Fast Semantic Segmentation Network](https://mlsanity.com/paper/1902.04502/) (2019)
- [Lite-HRNet: A Lightweight High-Resolution Network](https://mlsanity.com/paper/2104.06403/) (2021)
- [Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluations](https://mlsanity.com/paper/2104.10824/) (2021)
- [SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images](https://mlsanity.com/paper/2101.07422/) (2021)
- [Efficient Golf Ball Detection and Tracking Based on Convolutional Neural Networks and Kalman Filter](https://mlsanity.com/paper/2012.09393/) (2020)
- [Semantic Clustering based Deduction Learning for Image Recognition and Classification](https://mlsanity.com/paper/2112.13165/) (2021)
- [On Efficient Real-Time Semantic Segmentation: A Survey](https://mlsanity.com/paper/2206.08605/) (2022)
- [A New Dataset and Comparative Study for Aphid Cluster Detection](https://mlsanity.com/paper/2307.05929/) (2023)
- [Towards More Effective PRM-based Crowd Counting via A Multi-resolution Fusion and Attention Network](https://mlsanity.com/paper/2112.09664/) (2021)

## Built on by (most cited first, 6 of 6)

- [MOFA: A Model Simplification Roadmap for Image Restoration on Mobile Devices](https://mlsanity.com/paper/2308.12494/) (2023)
- [Aphid Cluster Recognition and Detection in the Wild Using Deep Learning Models](https://mlsanity.com/paper/2308.05881/) (2023)
- [A New Dataset and Comparative Study for Aphid Cluster Detection and Segmentation in Sorghum Fields](https://mlsanity.com/paper/2405.04305/) (2024)
- [Kitchen Food Waste Image Segmentation and Classification for Compost Nutrients Estimation](https://mlsanity.com/paper/2401.15175/) (2024)
- [Self-Supervised Backbone Framework for Diverse Agricultural Vision Tasks](https://mlsanity.com/paper/2403.15248/) (2024)
- [Tracking Moose using Aerial Object Detection](https://mlsanity.com/paper/2507.21256/) (2025)

## Abstract (arXiv)

Aphid infestations can cause extensive damage to wheat and sorghum fields and spread plant viruses, resulting in significant yield losses in agriculture. To address this issue, farmers often rely on chemical pesticides, which are inefficiently applied over large areas of fields. As a result, a considerable amount of pesticide is wasted on areas without pests, while inadequate amounts are applied to areas with severe infestations. The paper focuses on the urgent need for an intelligent autonomous system that can locate and spray infestations within complex crop canopies, reducing pesticide use and environmental impact. We have collected and labeled a large aphid image dataset in the field, and propose the use of real-time semantic segmentation models to segment clusters of aphids. A multiscale dataset is generated to allow for learning the clusters at different scales. We compare the segmentation speeds and accuracy of four state-of-the-art real-time semantic segmentation models on the aphid cluster dataset, benchmarking them against nonreal-time models. The study results show the effectiveness of a real-time solution, which can reduce inefficient pesticide use and increase crop yields, paving the way towards an autonomous pest detection system.