From Prototype to Inference:A Pipeline to Apply Deep Learning in Sorghum Panicle Detection
作者机构：School of Agriculture and Food SciencesThe University of QueenslandBrisbaneAustralia School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia Queensland Alliance for Agriculture and Food InnovationThe University of QueenslandBrisbaneAustralia ArvalisInstitut du VégétalParisFrance Graduate School of Agricultural and Life SciencesThe University of TokyoTokyoJapan Institut National de la Recherche AgronomiqueParisFrance
出 版 物：《Plant Phenomics》 (植物表型组学（英文）)
年 卷 期：2023年第5卷第1期
页 面：94-109页
核心收录：
摘 要：Head(panicle)density is a major component in understanding crop yield,especially in crops that produce variable numbers of tillers such as sorghum and *** 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 *** of the easy availability of red–green–blue images,machine learning approaches have been applied to replacing manual ***,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 *** this paper,we provide a comprehensive pipeline from data collection to model deployment in deep-learning-assisted panicle yield estimation for *** pipeline provides a basis from data collection and model training,to model validation and model deployment in commercial *** model training is the foundation of the ***,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 *** we demonstrate our pipeline in a sorghum field,the pipeline can be generalized to other grain *** 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.
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