Research Article | Open Access

Volume 2023 |Article ID 0017 | https://doi.org/10.34133/plantphenomics.0017

From Prototype to Inference: A Pipeline to Apply Deep Learning in Sorghum Panicle Detection

Chrisbin James,^1,7 Yanyang Gu,^2,7 Andries Potgieter,³ Etienne David,⁴ Simon Madec,⁴ Wei Guo,⁵ Frédéric Baret,⁶ Anders Eriksson,² and Scott Chapman ¹

¹School of Agriculture and Food Sciences, The University of Queensland, Brisbane, Australia
²School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
³Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Australia
⁴Arvalis, Institut du Végétal, Paris, France
⁵Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
⁶Institut National de la Recherche Agronomique, Paris, France
⁷These authors contributed equally to this work

Abstract

Head (panicle) density is a major component in understanding crop yield, especially in crops that produce variable numbers of tillers such as sorghum and wheat. Use of panicle density both in plant breeding and in the agronomy scouting of commercial crops typically relies on manual counts observation, which is an inefficient and tedious process. Because of the easy availability of red–green–blue images, machine learning approaches have been applied to replacing manual counting. However, much of this research focuses on detection per se in limited testing conditions and does not provide a general protocol to utilize deep-learning-based counting. In this paper, we provide a comprehensive pipeline from data collection to model deployment in deep-learning-assisted panicle yield estimation for sorghum. This pipeline provides a basis from data collection and model training, to model validation and model deployment in commercial fields. Accurate model training is the foundation of the pipeline. However, in natural environments, the deployment dataset is frequently different from the training data (domain shift) causing the model to fail, so a robust model is essential to build a reliable solution. Although we demonstrate our pipeline in a sorghum field, the pipeline can be generalized to other grain species. Our pipeline provides a high-resolution head density map that can be utilized for diagnosis of agronomic variability within a field, in a pipeline built without commercial software.