# TasselNet: Counting maize tassels in the wild via local counts regression network

arXiv: https://arxiv.org/abs/1707.02290 · HTML: https://mlsanity.com/paper/1707.02290/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2017.

## Built on by (most cited first, 26 of 26)

- [Rethinking Spatial Invariance of Convolutional Networks for Object Counting](https://mlsanity.com/paper/2206.05253/) (2022)
- [STEERER: Resolving Scale Variations for Counting and Localization via Selective Inheritance Learning](https://mlsanity.com/paper/2308.10468/) (2023)
- [Vision Transformer Off-the-Shelf: A Surprising Baseline for Few-Shot Class-Agnostic Counting](https://mlsanity.com/paper/2305.04440/) (2023)
- [Redesigning Multi-Scale Neural Network for Crowd Counting](https://mlsanity.com/paper/2208.02894/) (2022)
- [Regressor-Segmenter Mutual Prompt Learning for Crowd Counting](https://mlsanity.com/paper/2312.01711/) (2023)
- [FusionCount: Efficient Crowd Counting via Multiscale Feature Fusion](https://mlsanity.com/paper/2202.13660/) (2022)
- [Exploring Contextual Attribute Density in Referring Expression Counting](https://mlsanity.com/paper/2503.12460/) (2025)
- [RGB-D Indiscernible Object Counting in Underwater Scenes](https://mlsanity.com/paper/2304.11677/) (2023)
- [WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting](https://mlsanity.com/paper/2103.09408/) (2021)
- [CrowdMLP: Weakly-Supervised Crowd Counting via Multi-Granularity MLP](https://mlsanity.com/paper/2203.08219/) (2022)
- [A Survey on Class-Agnostic Counting: Advancements from Reference-Based to Open-World Text-Guided Approaches](https://mlsanity.com/paper/2501.19184/) (2025)
- [SoybeanNet: Transformer-Based Convolutional Neural Network for Soybean Pod Counting from Unmanned Aerial Vehicle (UAV) Images](https://mlsanity.com/paper/2310.10861/) (2023)
- [Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods](https://mlsanity.com/paper/2105.07660/) (2021)
- [High precision control and deep learning-based corn stand counting algorithms for agricultural robot](https://mlsanity.com/paper/2103.11276/) (2021)
- [TasselNetV4: A vision foundation model for cross-scene, cross-scale, and cross-species plant counting](https://mlsanity.com/paper/2509.20857/) (2025)
- [Plant Taxonomy Meets Plant Counting: A Fine-Grained, Taxonomic Dataset for Counting Hundreds of Plant Species](https://mlsanity.com/paper/2603.21229/) (2026)
- [Deep Learning Techniques for Visual Counting](https://mlsanity.com/paper/2206.03033/) (2022)
- [Virtual Classification: Modulating Domain-Specific Knowledge for Multidomain Crowd Counting](https://mlsanity.com/paper/2402.03758/) (2024)
- [Depth-Assisted Network for Indiscernible Marine Object Counting with Adaptive Motion-Differentiated Feature Encoding](https://mlsanity.com/paper/2503.08152/) (2025)
- [SDE-DET: A Precision Network for Shatian Pomelo Detection in Complex Orchard Environments](https://mlsanity.com/paper/2509.19990/) (2025)
- [Automatic Plant Cover Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/2106.11154/) (2021)
- [Inception-Based Crowd Counting -- Being Fast while Remaining Accurate](https://mlsanity.com/paper/2210.09796/) (2022)
- [CountMamba: Exploring Multi-directional Selective State-Space Models for Plant Counting](https://mlsanity.com/paper/2410.07528/) (2024)
- [Soybean pod and seed counting in both outdoor fields and indoor laboratories using unions of deep neural networks](https://mlsanity.com/paper/2502.15286/) (2025)
- [Count Anything](https://mlsanity.com/paper/2605.30846/) (2026)
- [AgriCountDINO: Parameter-Efficient Exemplar-Guided Counting and Localization in Agriculture](https://mlsanity.com/paper/2609.29460/) (2026)

## Abstract (arXiv)

Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype. In recent years, computer vision technologies have experienced a significant breakthrough due to the emergence of large-scale datasets and increased computational resources. Naturally image-based approaches have also received much attention in plant-related studies. Yet a fact is that most image-based systems for plant phenotyping are deployed under controlled laboratory environment. When transferring the application scenario to unconstrained in-field conditions, intrinsic and extrinsic variations in the wild pose great challenges for accurate counting of maize tassels, which goes beyond the ability of conventional image processing techniques. This calls for further robust computer vision approaches to address in-field variations. This paper studies the in-field counting problem of maize tassels. To our knowledge, this is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment.