Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks

By Jordan R. Ubbens, Ian Stavness. Frontiers in Plant Science, 2017

330 citations recorded by the source corpus.

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 phenotyping capabilities to keep up with an increasing amount of data from high-dimensional imaging sensors and the desire to measure more complex phenotypic traits (Knecht et al., 2016). In this paper, we introduce an open-source deep learning tool called Deep Plant Phenomics. This tool provides pre-trained neural networks for several common plant phenotyping tasks, as well as an easy platform that can be used by plant scientists to train models for their own phenotyping applications. We report performance results on three plant phenotyping benchmarks from the literature, including state of the art performance on leaf counting, as well as the first published results for the mutant classification and age regression tasks for Arabidopsis thaliana.

Subfields: Ecological Modeling, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/39bc14bda0e5b2887aa5cf9512d8dcb2b160fa66

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Citations in the corpus

Crop Phenomics: Current Status and Perspectives (2019, background): “As far Berni et al., 2009; Swain et al., 2010; Chapman et al., 2014 as plant phenotyping, CNNs have been eﬀectively applied to detect and diagnosis (Mohanty et al., 2016), classify fruits and ﬂowers (Pawara et al., 2017), and count leaf number (Ubbens and Stavness, 2017).”
Improving Object Counting with Heatmap Regulation (2018, methodology): “Considering this fact, where counting is the only task at hand, convolutional networks are employed as a high-dimensional regression network to generate a real-valued or discrete count from the input image directly [24,3,6,5,4,7].”
A Survey on Smart Agriculture: Development Modes, Technologies, and Security and Privacy Challenges (2021, background): “[87] proposed a deep plant phenomics system, which was an easy platform by providing a pre-trained model for plant scientists.”
Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments (2021, background): “The development of deep plant phenomics (DPP) created a major contribution to the community of plant phenotyping (Ubbens & Stavness, 2017).”