Few-shot and interpretable agentic framework based on large language models for data-efficient plant phenotyping

Abstract

The integration of electronic system into agricultural production can significantly enhance its efficiency and scalability. However, most of the current research focuses on the data acquisition and automated control. The development of expert-level, interpretable decision-making systems remains a challenge, primarily due to the prohibitive requirement for extensive domain-specific labeled data. In this manuscript, a novel agentic framework integrated with Large Language Models is proposed and demonstrated, using seedling assessment as a case study. The framework achieves high predictive accuracy, strong interpretability, and fast-adaption ability, offering a distinct advantage over methods that demand large labeled datasets. An agentic orchestration framework integrated with the Analytic Hierarchy Process and the reasoning ability of the Large Language Models is constructed to automatically derive the raw assessment rating. Based on a score calibration system using few-shot learning with three different types of lettuce, Butterhead, Grand Rapids, and Ramosa Hort, the final rating score can be derived with good prediction accuracy based on a small dataset (less than 20 labelled data). Additionally, three supplementary plant species (Sprout, Ball Brassica, and Rapa Brassica) are used to demonstrate the framework’s rapid adaptation capability. A field experiment guided by the agentic framework is conducted to prove that this seedling assessment system can be applied to help increase yield by more than 20 %. Our framework presents an important attempt towards an intelligent agricultural system that is capable to achieve expert-level and data-efficient decision making, thereby helping to bridge the critical gap between artificial intelligence research and practical agricultural application.

Plant phenotyping relevance

LLMを用いた解釈可能なエージェント型フレームワークを開発し、苗の評価スコアという植物状態の推定・抽出に適用しているため、表現型取得・評価手法が研究の中心です。

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