# Image-Based Sorghum Head Counting When You Only Look Once

arXiv: https://arxiv.org/abs/2009.11929 · HTML: https://mlsanity.com/paper/2009.11929/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting](https://mlsanity.com/paper/2103.09408/) (2021)
- [Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Review](https://mlsanity.com/paper/2303.02632/) (2023)
- [3D Reconstruction-Based Seed Counting of Sorghum Panicles for Agricultural Inspection](https://mlsanity.com/paper/2211.07748/) (2022)
- [An Applied Deep Learning Approach for Estimating Soybean Relative Maturity from UAV Imagery to Aid Plant Breeding Decisions](https://mlsanity.com/paper/2108.00952/) (2021)

## Abstract (arXiv)

Modern trends in digital agriculture have seen a shift towards artificial intelligence for crop quality assessment and yield estimation. In this work, we document how a parameter tuned single-shot object detection algorithm can be used to identify and count sorghum head from aerial drone images. Our approach involves a novel exploratory analysis that identified key structural elements of the sorghum images and motivated the selection of parameter-tuned anchor boxes that contributed significantly to performance. These insights led to the development of a deep learning model that outperformed the baseline model and achieved an out-of-sample mean average precision of 0.95.