# WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting

arXiv: https://arxiv.org/abs/2103.09408 · HTML: https://mlsanity.com/paper/2103.09408/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 81 references that are in the map.
- First posted on arXiv 17 Mar 2021.

## Builds on (most cited first, 30 of 34)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes](https://mlsanity.com/paper/1802.10062/) (2018)
- [Using Deep Learning for Image-Based Plant Disease Detection](https://mlsanity.com/paper/1604.03169/) (2016)
- [NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization](https://mlsanity.com/paper/2001.03360/) (2020)
- [JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method](https://mlsanity.com/paper/2004.03597/) (2020)
- [CrowdNet: A Deep Convolutional Network for Dense Crowd Counting](https://mlsanity.com/paper/1608.06197/) (2016)
- [CNN-based Density Estimation and Crowd Counting: A Survey](https://mlsanity.com/paper/2003.12783/) (2020)
- [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)
- [A CNN-RNN Framework for Crop Yield Prediction](https://mlsanity.com/paper/1911.09045/) (2019)
- [Crop Yield Prediction Using Deep Neural Networks](https://mlsanity.com/paper/1902.02860/) (2019)
- [SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting](https://mlsanity.com/paper/1908.03716/) (2019)
- [TasselNet: Counting maize tassels in the wild via local counts regression network](https://mlsanity.com/paper/1707.02290/) (2017)
- [Crowd counting via scale-adaptive convolutional neural network](https://mlsanity.com/paper/1711.04433/) (2017)
- [Simultaneous Corn and Soybean Yield Prediction from Remote Sensing Data Using Deep Transfer Learning](https://mlsanity.com/paper/2012.03129/) (2020)
- [Forecasting Corn Yield with Machine Learning Ensembles](https://mlsanity.com/paper/2001.09055/) (2020)
- [DeepCorn: A Semi-Supervised Deep Learning Method for High-Throughput Image-Based Corn Kernel Counting and Yield Estimation](https://mlsanity.com/paper/2007.10521/) (2020)
- [Predicting Yield Performance of Parents in Plant Breeding: A Neural Collaborative Filtering Approach](https://mlsanity.com/paper/2001.09902/) (2020)
- [Convolutional Neural Networks for Image-based Corn Kernel Detection and Counting](https://mlsanity.com/paper/2003.12025/) (2020)

## Built on by (most cited first, 8 of 8)

- [SoybeanNet: Transformer-Based Convolutional Neural Network for Soybean Pod Counting from Unmanned Aerial Vehicle (UAV) Images](https://mlsanity.com/paper/2310.10861/) (2023)
- [Semi-supervised dry herbage mass estimation using automatic data and synthetic images](https://mlsanity.com/paper/2110.13719/) (2021)
- [Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods](https://mlsanity.com/paper/2105.07660/) (2021)
- [SynthSet: Generative Diffusion Model for Semantic Segmentation in Precision Agriculture](https://mlsanity.com/paper/2411.03505/) (2024)
- [Vision-Based Anti Unmanned Aerial Technology: Opportunities and Challenges](https://mlsanity.com/paper/2507.10006/) (2025)
- [Winter wheat yield prediction using convolutional neural networks from environmental and phenological data](https://mlsanity.com/paper/2105.01282/) (2021)
- [Colab NAS: Obtaining lightweight task-specific convolutional neural networks following Occam's razor](https://mlsanity.com/paper/2212.07700/) (2022)
- [Multi-growth stage plant recognition: a case study of Palmer amaranth (Amaranthus palmeri) in cotton (Gossypium hirsutum)](https://mlsanity.com/paper/2307.15816/) (2023)

## Abstract (arXiv)

For a globally recognized planting breeding organization, manually-recorded field observation data is crucial for plant breeding decision making. However, certain phenotypic traits such as plant color, height, kernel counts, etc. can only be collected during a specific time-window of a crop's growth cycle. Due to labor-intensive requirements, only a small subset of possible field observations are recorded each season. To help mitigate this data collection bottleneck in wheat breeding, we propose a novel deep learning framework to accurately and efficiently count wheat heads to aid in the gathering of real-time data for decision making. We call our model WheatNet and show that our approach is robust and accurate for a wide range of environmental conditions of the wheat field. WheatNet uses a truncated MobileNetV2 as a lightweight backbone feature extractor which merges feature maps with different scales to counter image scale variations. Then, extracted multi-scale features go to two parallel sub-networks for simultaneous density-based counting and localization tasks. Our proposed method achieves an MAE and RMSE of 3.85 and 5.19 in our wheat head counting task, respectively, while having significantly fewer parameters when compared to other state-of-the-art methods. Our experiments and comparisons with other state-of-the-art methods demonstrate the superiority and effectiveness of our proposed method.