Leveraging multiple datasets for deep leaf counting

TL;DR

This paper introduces a deep learning approach for counting plant leaves directly from images, leveraging multiple datasets to improve accuracy without requiring detailed leaf segmentation annotations.

Contribution

It presents a novel deep regression method for leaf counting that benefits from multi-dataset training, outperforming previous segmentation-based approaches.

Findings

Significantly outperforms previous state-of-the-art on CVPPP dataset

Achieves ~50% improvement in counting accuracy

Reduces mean absolute difference in count by 20%

Abstract

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|).