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Updated: Jun 26, 2026

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在RGB-D图像上的多式学习,用于精确的litchi表型和体重估计

Mingchao Yang¹, Riyao Chen², Ding Chen¹

¹Institute of Tropical Fruit Trees, Hainan Academy of Agricultural Sciences, Key Laboratory of Genetic Resources and Utilization of Tropical Fruits and Vegetables (Co-construction by Ministry and Province), Key Laboratory of Tropical Fruit Tree Biology of Hainan Province, Investigation Station of Tropical Fruit Trees of Ministry of Agriculture, Haikou, 571100, People's Republic of China.

|December 5, 2025

概括

新的人工智能工具LitchiPhenoNet准确地测量了利奇果的直径和重量等特征. 这种自动化表型化通过克服手工测量挑战,推动了利奇的繁殖.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
植物育种植物育种

背景情况:

精确的李奇表型定型对于品种选择和育种研究至关重要.
手动测量是耗时的,主观的和低效的.
利奇的复杂的水果表面和种子形态构成了自动化分析的挑战.

研究的目的:

开发一个自动化,精确的表型化工具,用于利奇果的特征.
为了解决由litchi的独特特征引起的语义细分和估计方面的困难.
提高利奇养殖计划的效率和客观性.

主要方法:

开发了LitchiPhenoNet,这是一个使用RGB和深度数据的多式联网深度学习框架.
采用双分支架构与RD-Fusion模块用于交叉模式的特征提取.
利用了RGB-D数据集,包括10个品种的1,198个litchi图像对.

主要成果:

LitchiPhenoNet实现了毫米级直径估计 (R2 ≈ 0.98,ME < 2mm),表现优于YOLO模型.
对于整个水果,果坑和果肉重量的估计,获得了克级精度 (R2高达0.98).
该框架表现出了对复杂的周心表面和跨种植物变异性的稳定性.

结论:

LitchiPhenoNet提供了一种高效,可靠和准确的解决方案,用于量化litchi表型特征.
该框架提高了子育种和选择的客观性和效率.
该方法可扩展到其他纹理水果,并可用于高通量表型化.

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Light Acquisition

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