Virtual Reality-Enabled remote Human-Robot interaction for strawberry cultivation in greenhouses

Abstract

This paper investigates the application of a VR-controlled robotic system for yield monitoring in strawberry farming within a greenhouse environment. The study aims to evaluate the effectiveness of the system in identifying and counting ripe strawberries, categorized by size (small and large) and variety (Seascape and Albion), and compares with the obtained results by an onsite human expert. We designed experiments, in a controlled environment agriculture center, and conducted in two trials. The yield monitoring performance of the developed robotic system was evaluated based on two primary metrics of cycle completion times and fruit detection accuracy, 32 strawberry plants which grew 336 ripe fruits. In the first experiment, the system achieved detection rates of 63 % for small strawberries and 72 % for large strawberries, with cycle completion times ranging from 12.5 to 16 s. In the second experiment, improvements were observed, with detection rates increasing to 74 % for both sizes and cycle completion times reduced to between 11.9 and 15.7 s. The developed robotic system demonstrated high accuracy and efficiency, particularly with larger strawberries. However, some limitations were identified, including challenges related to occlusion. These findings suggest that while the VR-controlled robotic system has the potential to complement and even surpass traditional yield monitoring methods managed by human experts, further refinements are necessary. Future research should focus on optimizing the system’s performance and adapting the system to broader applications in agriculture.

Plant phenotyping relevance

VR制御ロボットによるイチゴ果実の検出・計数とサイズ分類を、検出精度および処理時間で評価しており、果実収量関連形質の取得方法と技術性能が研究の中心である。

Code and data availability

公開状態または取得可能な本文経路を確認できませんでした。

No evidence-backed public reproduction asset is currently recorded.