An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

Abstract

Machine learning is a widespread technology that plays a crucial role in digitalisation and aims to explore rules and patterns in large datasets to autonomously solve non-linear problems, taking advantage of multiple source data. Due to its versatility, machine learning can be applied to agriculture. Better crop management, plant health assessment, and early disease detection are some of the main challenges facing the agricultural sector. Plant phenotyping can play a key role in addressing these challenges, especially when combined with machine learning techniques. Therefore, this study reviews available scientific literature on the applications of machine learning algorithms in plant phenotyping with a specific focus on sunflowers. The most common algorithms in the agricultural field are described to emphasise possible uses. Subsequently, the overview highlights machine learning application on phenotyping in three primaries areas: crop management (i.e., yield prediction, biomass estimation, and growth stage monitoring), plant health (i.e., nutritional status and water stress), and disease detection. Finally, we focus on the adoption of machine learning techniques in sunflower phenotyping. The role of machine learning in plant phenotyping has been thoroughly investigated. Artificial neural networks and stacked models seems to be the best way to analyse data.

Plant phenotyping relevance

植物フェノタイピングにおける機械学習応用を体系的に概観するレビューであり、表現型推定・取得手法が中心です。

Code and data availability

This is a literature review of machine learning in plant phenotyping with a sunflower focus; it reports no original phenotyping measurements, datasets, images, or models of its own. The only paper-specific analysis material mentioned is the authors' 'elaborations', which are not publicly deposited and are available onl

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