L-system models for image-based phenomics: case studies of maize and canola

2021/12/10 by Mikolaj Cieslak, Nazifa Azam Khan, Pascal Ferraro +5 · 1 voice · 2 citations
Agricultural and Biological Sciences · Environmental Science · #Greenhouse Technology and Climate Control #Smart Agriculture and AI #Remote Sensing in Agriculture

paper · pdf · doi:10.1093/insilicoplants/diab039

openalex publication_date 2021/12/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Abstract

Abstract Artificial neural networks that recognize and quantify relevant aspects of crop plants show great promise in image-based phenomics, but their training requires many annotated images. The acquisition of these images is comparatively simple, but their manual annotation is time-consuming. Realistic plant models, which can be annotated automatically, thus present an attractive alternative to real plant images for training purposes. Here we show how such models can be constructed and calibrated quickly, using maize and canola as case studies.

Cited by

• Using high-throughput phenotype platform MVS-Pheno to reconstruct the 3D morphological structure of wheat
• MDE for crop representations in smart farming digital twins: a reinforcement learning perspective

Discussions

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