# Leveraging multiple datasets for deep leaf counting

arXiv: https://arxiv.org/abs/1709.01472 · HTML: https://mlsanity.com/paper/1709.01472/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2017.

## Built on by (most cited first, 6 of 6)

- [Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Review](https://mlsanity.com/paper/2303.02632/) (2023)
- [GroMo: Plant Growth Modeling with Multiview Images](https://mlsanity.com/paper/2503.06608/) (2025)
- [Plant Taxonomy Meets Plant Counting: A Fine-Grained, Taxonomic Dataset for Counting Hundreds of Plant Species](https://mlsanity.com/paper/2603.21229/) (2026)
- [Estimates of maize plant density from UAV RGB images using Faster-RCNN detection model: impact of the spatial resolution](https://mlsanity.com/paper/2105.11857/) (2021)
- [Classification and Visualization of Genotype x Phenotype Interactions in Biomass Sorghum](https://mlsanity.com/paper/2108.04090/) (2021)
- [CLIP-Guided Multi-Task Regression for Multi-View Plant Phenotyping](https://mlsanity.com/paper/2603.04091/) (2026)

## Abstract (arXiv)

The number of leaves a plant has is one of the key traits (phenotypes) describing its development and growth. Here, we propose an automated, deep learning based approach for counting leaves in model rosette plants. While state-of-the-art results on leaf counting with deep learning methods have recently been reported, they obtain the count as a result of leaf segmentation and thus require per-leaf (instance) segmentation to train the models (a rather strong annotation). Instead, our method treats leaf counting as a direct regression problem and thus only requires as annotation the total leaf count per plant. We argue that combining different datasets when training a deep neural network is beneficial and improves the results of the proposed approach. We evaluate our method on the CVPPP 2017 Leaf Counting Challenge dataset, which contains images of Arabidopsis and tobacco plants. Experimental results show that the proposed method significantly outperforms the winner of the previous CVPPP challenge, improving the results by a minimum of ~50% on each of the test datasets, and can achieve this performance without knowing the experimental origin of the data (i.e. in the wild setting of the challenge). We also compare the counting accuracy of our model with that of per leaf segmentation algorithms, achieving a 20% decrease in mean absolute difference in count (|DiC|).