# Maize Seedling Detection Dataset (MSDD): A Curated High-Resolution RGB Dataset for Seedling Maize Detection and Benchmarking with YOLOv9, YOLO11, YOLOv12 and Faster-RCNN

arXiv: https://arxiv.org/abs/2509.15181 · HTML: https://mlsanity.com/paper/2509.15181/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 47 references that are in the map.
- First posted on arXiv 18 Sep 2025.

## Builds on (most cited first, 9 of 9)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [YOLOv12: Attention-Centric Real-Time Object Detectors](https://mlsanity.com/paper/2502.12524/) (2025)
- [Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods](https://mlsanity.com/paper/2005.02162/) (2020)
- [Evaluating Neural Radiance Fields (NeRFs) for 3D Plant Geometry Reconstruction in Field Conditions](https://mlsanity.com/paper/2402.10344/) (2024)
- [A survey of datasets for computer vision in agriculture](https://mlsanity.com/paper/2502.16950/) (2025)

## Built on by (most cited first, 1 of 1)

- [MaizeStandCounting (MaSC): Automated and Accurate Maize Stand Counting from UAV Imagery Using Image Processing and Deep Learning](https://mlsanity.com/paper/2510.07580/) (2025)

## Abstract (arXiv)

Accurate maize seedling detection is crucial for precision agriculture, yet curated datasets remain scarce. We introduce MSDD, a high-quality aerial image dataset for maize seedling stand counting, with applications in early-season crop monitoring, yield prediction, and in-field management. Stand counting determines how many plants germinated, guiding timely decisions such as replanting or adjusting inputs. Traditional methods are labor-intensive and error-prone, while computer vision enables efficient, accurate detection. MSDD contains three classes-single, double, and triple plants-capturing diverse growth stages, planting setups, soil types, lighting conditions, camera angles, and densities, ensuring robustness for real-world use. Benchmarking shows detection is most reliable during V4-V6 stages and under nadir views. Among tested models, YOLO11 is fastest, while YOLOv9 yields the highest accuracy for single plants. Single plant detection achieves precision up to 0.984 and recall up to 0.873, but detecting doubles and triples remains difficult due to rarity and irregular appearance, often from planting errors. Class imbalance further reduces accuracy in multi-plant detection. Despite these challenges, YOLO11 maintains efficient inference at 35 ms per image, with an additional 120 ms for saving outputs. MSDD establishes a strong foundation for developing models that enhance stand counting, optimize resource allocation, and support real-time decision-making. This dataset marks a step toward automating agricultural monitoring and advancing precision agriculture.