# Automatic counting of planting microsites via local visual detection and global count estimation

arXiv: https://arxiv.org/abs/2311.00796 · HTML: https://mlsanity.com/paper/2311.00796/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 74 references that are in the map.
- First posted on arXiv 1 Nov 2023.

## Builds on (most cited first, 15 of 15)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds](https://mlsanity.com/paper/1808.01050/) (2018)
- [Switching Convolutional Neural Network for Crowd Counting](https://mlsanity.com/paper/1708.00199/) (2017)
- [Crowd Counting and Density Estimation by Trellis Encoder-Decoder Network](https://mlsanity.com/paper/1903.00853/) (2019)
- [CrowdNet: A Deep Convolutional Network for Dense Crowd Counting](https://mlsanity.com/paper/1608.06197/) (2016)
- [Iterative Crowd Counting](https://mlsanity.com/paper/1807.09959/) (2018)
- [NAS-Count: Counting-by-Density with Neural Architecture Search](https://mlsanity.com/paper/2003.00217/) (2020)
- [Towards Using Count-level Weak Supervision for Crowd Counting](https://mlsanity.com/paper/2003.00164/) (2020)
- [Crowd counting via scale-adaptive convolutional neural network](https://mlsanity.com/paper/1711.04433/) (2017)
- [PaDNet: Pan-Density Crowd Counting](https://mlsanity.com/paper/1811.02805/) (2018)
- [Crowd Counting via Weighted VLAD on Dense Attribute Feature Maps](https://mlsanity.com/paper/1604.08660/) (2016)

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

- [Object Counting Across Modalities: Taxonomies, Benchmarks, Applications, and Open Challenges](https://mlsanity.com/paper/2608.23845/) (2026)

## Abstract (arXiv)

In forest industry, mechanical site preparation by mounding is widely used prior to planting operations. One of the main problems when planning planting operations is the difficulty in estimating the number of mounds present on a planting block, as their number may greatly vary depending on site characteristics. This estimation is often carried out through field surveys by several forestry workers. However, this procedure is prone to error and slowness. Motivated by recent advances in UAV imagery and artificial intelligence, we propose a fully automated framework to estimate the number of mounds on a planting block. Using computer vision and machine learning, we formulate the counting task as a supervised learning problem using two prediction models. A local detection model is firstly used to detect visible mounds based on deep features, while a global prediction function is subsequently applied to provide a final estimation based on block-level features. To evaluate the proposed method, we constructed a challenging UAV dataset representing several plantation blocks with different characteristics. The performed experiments demonstrated the robustness of the proposed method, which outperforms manual methods in precision, while significantly reducing time and cost.