Plant phenotype plays an important role in genetics, botany, and agronomy, while the
currently popular methods for phenotypic trait measurement have some limitations in …

We found the article by Singh et al.[1] extremely interesting since it introduces and
showcases the utility of machine learning for high throughput data-driven plant phenotyping …

Recent years have witnessed significant advancement in computer vision research based
on deep learning. Success of these tasks largely depends on the availability of a large …

Accurately estimating the number of objects in a single image is a challenging yet
meaningful task and has been applied in many applications such as urban planning and …

Visual counting, a task that predicts the number of objects from an image/video, is an open-
set problem by nature, ie, the number of population can vary in [0,+[?]) in theory. However …

While convolutional neural networks have gained impressive success recently in solving
structured prediction problems such as semantic segmentation, it remains a challenge to …

Plant phenomics has received increasing interest in recent years in an attempt to bridge the
genotype-to-phenotype knowledge gap. There is a need for expanded high-throughput …

Instance segmentation is the problem of detecting and delineating each distinct object of
interest appearing in an image. Current instance segmentation approaches consist of …

Background Accurately counting maize tassels is important for monitoring the growth status
of maize plants. This tedious task, however, is still mainly done by manual efforts. In the …

Image-based approaches to plant phenotyping are gaining momentum providing fertile
ground for several interesting vision tasks where fine-grained categorization is necessary …