From prototype to inference: A pipeline to apply deep learning in sorghum panicle detection

James Chrisbin, Gu Yanyang, Potgieter Andries, David Etienne, Madec Simon, Guo Wei, Baret Frédéric, Eriksson Anders, Chapman Scott. 2023. From prototype to inference: A pipeline to apply deep learning in sorghum panicle detection. Plant Phenomics, 5:0017, 16 p.

https://doi.org/10.34133/plantphenomics.0017

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Résumé : 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.

Mots-clés Agrovoc : Sorghum, analyse d'image, analyse de données, collecte de données, modélisation des cultures, panicule

Mots-clés complémentaires : deep learning

Mots-clés libres : Computer vision, Sorghum, Deep Learning

Classification Agris : F01 - Culture des plantes
U30 - Méthodes de recherche
U10 - Informatique, mathématiques et statistiques

Champ stratégique Cirad : CTS 2 (2019-) - Transitions agroécologiques

Agences de financement hors UE : Grains Research and Development Corporation

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