Volume Estimation of Agricultural Products Using 2D Images: From Laboratory to Orchard

Abstract

Accurate and non-destructive volume estimation of agricultural products is essential for precision agriculture, yet remains challenging when transitioning from controlled laboratory conditions to complex orchard environments. Although 2D image-based volume estimation methods provide a cost-effective and scalable solution, existing studies are fragmented and lack a unified perspective on their real-world applicability. This review presents a systematic synthesis of 2D image-based volume estimation methods, explicitly framed through the laboratory-to-orchard transition. We categorized existing volume estimation approaches according to the sensing modality into monocular RGB-based approaches and depth-assisted methods, and further reviewed them based on the image processing methods. A key finding is that high-precision geometric estimation can be achieved in laboratory environments, whereas deep learning and RGB-D fusion have driven a shift from conventional geometric modeling toward data-driven and hybrid learning frameworks in orchard settings. However, 2D image-based volume estimation remains fundamentally limited by scale ambiguity, severe occlusion, and sensitivity to illumination and background variability in real orchard environment. Overall, this review provides a unified perspective for understanding volume estimation methodology across environments and offers guidance for developing robust, scalable, and field-deployable volume estimation systems for real-world agricultural applications.

Plant phenotyping relevance

農産物の2D画像から体積を推定する手法を体系的にレビューしており、植物器官・果実の形態形質取得方法が中心である。

Code and data availability

This is a review article synthesizing prior literature on 2D image-based volume estimation. No public phenotype datasets, images, author code, models, or supplements containing paper-specific measurements or analysis are mentioned; no data or code availability statement appears in the supplied blocks.

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