Unverified paper record
Comprehensive plant health monitoring: expert-level assessment with spatio-temporal image data.
Frontiers in plant science · 30 May 2025 · 10.3389/fpls.2025.1511651
Abstract
Maintaining crop health is essential for global food security, yet traditional plant monitoring methods based on manual inspection are labor-intensive and often inadequate for early detection of stressors and diseases, and insufficient for timely, proactive interventions. To address this challenge, we propose a deep learning-based framework for expert-level, spatiotemporal plant health assessment using sequential RGB images. Our method categorizes plant health into five levels, ranging from very poor to optimal, based on visual and morphological indicators observed throughout the cultivation cycle. To validate the approach, we collected a custom dataset of 12,119 annotated images from 200 tomato plants across three varieties, grown in semi-open greenhouses over multiple cultivation seasons within one year. The framework leverages state-of-the-art CNN and transformer architectures to produce accurate, stage-specific health predictions. These predictions closely align with expert annotations, demonstrating the model's reliability in tracking plant health progression. In addition, the system enables the generation of dynamic cultivation maps for continuous monitoring and early intervention, supporting data-driven crop management. Overall, the results highlight the potential of this framework to advance precision agriculture through scalable, automated plant health monitoring, guided by an understanding of key visual indicators and stressors affecting crop health throughout the cultivation period.
Plant phenotyping relevance
RGB画像から植物の形態指標に基づく健康状態を推定する深層学習フレームワークを開発・検証しており、植物表現型の取得・抽出が研究の中心です。
abstractwe propose a deep learning-based framework for expert-level, spatiotemporal plant health assessment using sequential RGB images.
abstractTo validate the approach, we collected a custom dataset of 12,119 annotated images from 200 tomato plants across three varieties
abstractThe framework leverages state-of-the-art CNN and transformer architectures to produce accurate, stage-specific health predictions.
Code and data availability
The paper's core asset is a custom dataset of 12,119 annotated tomato plant health images, but the data availability statement explicitly restricts access: requests must be directed to the author. No public URL, repository, or code deposit is provided.
No evidence-backed public reproduction asset is currently recorded.
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