Developing an IoT-driven delta robot to stimulate the growth of mulberry branch cuttings cultivated aeroponically using machine vision technology

Abstract

Machine vision plays a pivotal role in automatically monitoring the growth of mulberry branch cuttings (Mbc) in aeroponic systems, ensuring productivity, quality, and sustainability. However, the challenge lies in the varying bud growth rates, with some taking longer to break dormancy, leading to inconsistent development and delays in root formation. This paper aims to develop an Internet of Things (IoT)-integrated delta robot, equipped with advanced camera data acquisition and intelligent processing. It is intended to enhance aeroponic systems by precisely and rapidly detecting the Mbc growth state and applying growth stimulation. The system framework requires tiny machine learning models specifically designed to function efficiently on IoT hardware with limited power and resources, such as the lightweight versions of Tiny-YOLO, including YOLOv8-world, YOLOv9, YOLOv10, and YOLOv11. These models were trained on 3,000 images captured from three distinct camera perspectives—side view, elevated view, and angled view—during the growth of Mbc. Further optimization was achieved by progressively refining the weights through stepwise training. Artificial neural networks, for instance Back-Propagation Neural Networks (BPNN) and Elastic Net (ELNET), were deployed to compute the X, Y, and Z coordinates of the robot arm. Alongside, the look-up table approach efficiently identified the Mbc locations by referencing pre-stored data corresponding to the target coordinates. The experimental outcomes indicated that the optimized Tiny-YOLOv9 (Pr = 98.3 %, Re = 98.5 %, Fm = 98.4 %, mAP₅₀–₉₀ = 87.3 %) outperformed other models in both classification and localization of mulberry branches. Data fusion from three cameras with BPNN and ELNET models substantially outshined the use of a single camera. Moreover, the BPNN models for the arm axes (X, Y, Z) exhibited superior accuracy compared to ELNET. The BPNN recorded R² values of 0.999 for all three axes (X, Y, and Z), with corresponding RMSE values of 0.005 (MAE = 0.004), 0.006 (MAE = 0.005), and 0.013 (MAE = 0.010), respectively. This work can assist the agricultural community in monitoring plant growth, enabling timely and effective management decisions. It also holds great promise for expanding our methodology to include other crops in future aeroponic systems.

Plant phenotyping relevance

植物の成長状態をカメラ画像と機械学習で検出するIoT・ロボット型フェノタイピング基盤の開発が中心であり、単なる生物学的実験での routine 測定ではない。

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