# A New Dataset and Comparative Study for Aphid Cluster Detection

arXiv: https://arxiv.org/abs/2307.05929 · HTML: https://mlsanity.com/paper/2307.05929/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 19 references that are in the map.
- First posted on arXiv 12 Jul 2023.

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

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://mlsanity.com/paper/1912.02424/) (2019)
- [VarifocalNet: An IoU-aware Dense Object Detector](https://mlsanity.com/paper/2008.13367/) (2020)
- [Probabilistic Anchor Assignment with IoU Prediction for Object Detection](https://mlsanity.com/paper/2007.08103/) (2020)
- [Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection](https://mlsanity.com/paper/2011.12885/) (2020)
- [Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluations](https://mlsanity.com/paper/2104.10824/) (2021)
- [Miti-DETR: Object Detection based on Transformers with Mitigatory Self-Attention Convergence](https://mlsanity.com/paper/2112.13310/) (2021)
- [An Unsupervised Domain Adaptation Model based on Dual-module Adversarial Training](https://mlsanity.com/paper/2112.15555/) (2021)
- [Efficient Golf Ball Detection and Tracking Based on Convolutional Neural Networks and Kalman Filter](https://mlsanity.com/paper/2012.09393/) (2020)
- [Six-channel Image Representation for Cross-domain Object Detection](https://mlsanity.com/paper/2101.00561/) (2021)
- [Dynamic Label Assignment for Object Detection by Combining Predicted IoUs and Anchor IoUs](https://mlsanity.com/paper/2201.09396/) (2022)

## Built on by (most cited first, 3 of 3)

- [On the Real-Time Semantic Segmentation of Aphid Clusters in the Wild](https://mlsanity.com/paper/2307.10267/) (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)

## Abstract (arXiv)

Aphids are one of the main threats to crops, rural families, and global food security. Chemical pest control is a necessary component of crop production for maximizing yields, however, it is unnecessary to apply the chemical approaches to the entire fields in consideration of the environmental pollution and the cost. Thus, accurately localizing the aphid and estimating the infestation level is crucial to the precise local application of pesticides. Aphid detection is very challenging as each individual aphid is really small and all aphids are crowded together as clusters. In this paper, we propose to estimate the infection level by detecting aphid clusters. We have taken millions of images in the sorghum fields, manually selected 5,447 images that contain aphids, and annotated each aphid cluster in the image. To use these images for machine learning models, we crop the images into patches and created a labeled dataset with over 151,000 image patches. Then, we implement and compare the performance of four state-of-the-art object detection models.