# Leaf Counting with Deep Convolutional and Deconvolutional Networks

arXiv: https://arxiv.org/abs/1708.07570 · HTML: https://mlsanity.com/paper/1708.07570/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2017.

## Built on by (most cited first, 11 of 11)

- [Regressor-Segmenter Mutual Prompt Learning for Crowd Counting](https://mlsanity.com/paper/2312.01711/) (2023)
- [Scene-Adaptive Attention Network for Crowd Counting](https://mlsanity.com/paper/2112.15509/) (2021)
- [Deep Learning Techniques for Visual Counting](https://mlsanity.com/paper/2206.03033/) (2022)
- [Hierarchical Mask2Former: Panoptic Segmentation of Crops, Weeds and Leaves](https://mlsanity.com/paper/2310.06582/) (2023)
- [Virtual Classification: Modulating Domain-Specific Knowledge for Multidomain Crowd Counting](https://mlsanity.com/paper/2402.03758/) (2024)
- [Classification and Visualization of Genotype x Phenotype Interactions in Biomass Sorghum](https://mlsanity.com/paper/2108.04090/) (2021)
- [Importance of realism in procedurally-generated synthetic images for deep learning: case studies in maize and canola](https://mlsanity.com/paper/2404.05128/) (2024)
- [Exploiting Boundary Loss for the Hierarchical Panoptic Segmentation of Plants and Leaves](https://mlsanity.com/paper/2501.00527/) (2024)
- [CLIP-Guided Multi-Task Regression for Multi-View Plant Phenotyping](https://mlsanity.com/paper/2603.04091/) (2026)
- [ReLeaf: Benchmarking Leaf Segmentation across Domains and Species](https://mlsanity.com/paper/2605.03784/) (2026)
- [Curvature-aware 3D length estimation of greenhouse cucumbers using RGB-D imaging and cubic spline arc-length integration](https://mlsanity.com/paper/2606.22439/) (2026)

## Abstract (arXiv)

In this paper, we investigate the problem of counting rosette leaves from an RGB image, an important task in plant phenotyping. We propose a data-driven approach for this task generalized over different plant species and imaging setups. To accomplish this task, we use state-of-the-art deep learning architectures: a deconvolutional network for initial segmentation and a convolutional network for leaf counting. Evaluation is performed on the leaf counting challenge dataset at CVPPP-2017. Despite the small number of training samples in this dataset, as compared to typical deep learning image sets, we obtain satisfactory performance on segmenting leaves from the background as a whole and counting the number of leaves using simple data augmentation strategies. Comparative analysis is provided against methods evaluated on the previous competition datasets. Our framework achieves mean and standard deviation of absolute count difference of 1.62 and 2.30 averaged over all five test datasets.