# WeedVision: Multi-Stage Growth and Classification of Weeds using DETR and RetinaNet for Precision Agriculture

arXiv: https://arxiv.org/abs/2502.14890 · HTML: https://mlsanity.com/paper/2502.14890/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 16 Feb 2025.

## Builds on (most cited first, 5 of 5)

- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Normalization Techniques in Training DNNs: Methodology, Analysis and Application](https://mlsanity.com/paper/2009.12836/) (2020)
- [Context and Attribute Grounded Dense Captioning](https://mlsanity.com/paper/1904.01410/) (2019)
- [Light-Weight RetinaNet for Object Detection](https://mlsanity.com/paper/1905.10011/) (2019)
- [Improved Crop and Weed Detection with Diverse Data Ensemble Learning](https://mlsanity.com/paper/2310.01055/) (2023)

## Built on by (most cited first, 2 of 2)

- [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)
- [Weed Detection in Challenging Field Conditions: A Semi-Supervised Framework for Overcoming Shadow Bias and Data Scarcity](https://mlsanity.com/paper/2508.19511/) (2025)

## Abstract (arXiv)

Weed management remains a critical challenge in agriculture, where weeds compete with crops for essential resources, leading to significant yield losses. Accurate detection of weeds at various growth stages is crucial for effective management yet challenging for farmers, as it requires identifying different species at multiple growth phases. This research addresses these challenges by utilizing advanced object detection models, specifically, the Detection Transformer (DETR) with a ResNet50 backbone and RetinaNet with a ResNeXt101 backbone, to identify and classify 16 weed species of economic concern across 174 classes, spanning their 11 weeks growth stages from seedling to maturity. A robust dataset comprising 203,567 images was developed, meticulously labeled by species and growth stage. The models were rigorously trained and evaluated, with RetinaNet demonstrating superior performance, achieving a mean Average Precision (mAP) of 0.907 on the training set and 0.904 on the test set, compared to DETR's mAP of 0.854 and 0.840, respectively. RetinaNet also outperformed DETR in recall and inference speed of 7.28 FPS, making it more suitable for real time applications. Both models showed improved accuracy as plants matured. This research provides crucial insights for developing precise, sustainable, and automated weed management strategies, paving the way for real time species specific detection systems and advancing AI-assisted agriculture through continued innovation in model development and early detection accuracy.