Leaf Counting with Deep Convolutional and Deconvolutional Networks

TL;DR

This paper presents a deep learning approach combining deconvolutional and convolutional networks to accurately count plant leaves from RGB images, demonstrating effectiveness across different species and imaging conditions.

Contribution

It introduces a novel data-driven framework that generalizes leaf counting across plant species using deep neural networks and simple data augmentation.

Findings

Achieved mean absolute count difference of 1.62 on test datasets.

Effective leaf segmentation despite limited training data.

Outperformed previous methods on CVPPP-2017 dataset.

Abstract

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.