NeRF-based 3D reconstruction pipeline for acquisition and analysis of tomato crop morphology

Abstract

Recent advancements in digital phenotypic analysis have revolutionized the morphological analysis of crops, offering new insights into genetic trait expressions. This manuscript presents a novel 3D phenotyping pipeline utilizing the cutting-edge Neural Radiance Fields (NeRF) technology, aimed at overcoming the limitations of traditional 2D imaging methods. Our approach incorporates automated RGB image acquisition through unmanned greenhouse robots, coupled with NeRF technology for dense Point Cloud generation. This facilitates non-destructive, accurate measurements of crop parameters such as node length, leaf area, and fruit volume. Our results, derived from applying this methodology to tomato crops in greenhouse conditions, demonstrate a high correlation with traditional human growth surveys. The manuscript highlights the system’s ability to achieve detailed morphological analysis from limited viewpoint of camera, proving its suitability and practicality for greenhouse environments. The results displayed an R-squared value of 0.973 and a Mean Absolute Percentage Error (MAPE) of 0.089 for inter-node length measurements, while segmented leaf point cloud and reconstructed meshes showed an R-squared value of 0.953 and a MAPE of 0.090 for leaf area measurements. Additionally, segmented tomato fruit analysis yielded an R-squared value of 0.96 and a MAPE of 0.135 for fruit volume measurements. These metrics underscore the precision and reliability of our 3D phenotyping pipeline, making it a highly promising tool for modern agriculture.

Plant phenotyping relevance

NeRFとロボットRGB画像を用いてトマトの形態形質を取得・解析する3Dフェノタイピング手法を開発し、従来測定との精度検証も行っており、方法が研究の中心である。

Code and data availability

The article describes a NeRF-based tomato 3D phenotyping pipeline with robot-acquired RGB images, point clouds, and trait measurements, but no public repository, code deposit, or dataset URL is provided. The data availability statement only offers raw data from the authors upon request. All cited URLs (CloudCompare, ar

No evidence-backed public reproduction asset is currently recorded.