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Labor shortages in seedling nurseries, particularly in manual inspection and replanting, hinder operational efficiency despite advancements in automation. This study aims to develop a cost-effective, GPU-free machine vision system to automate the detection of deficient seedlings in plug trays, specifically for small-scale nursery operations. The proposed Deficiency Detection and Replanting Positioning (DDRP) machine integrates low-cost components including an Intel RealSense Depth Camera D435, Raspberry Pi 4B, stepper motors, and a programmable logic controller (PLC). It utilizes OpenCV’s Haar cascade algorithm, HSV color space conversion, and Otsu thresholding to enable real-time image processing without GPU acceleration. The proposed Deficiency Detection and Replanting Positioning (DDRP) machine integrates low-cost components including an Intel RealSense Depth Camera D435, Raspberry Pi 4B, stepper motors, and a programmable logic controller (PLC). It utilizes OpenCV’s Haar cascade algorithm, HSV color space conversion, and Otsu thresholding to enable real-time image processing without GPU acceleration. Under controlled laboratory conditions, the DDRP-Machine achieved high detection accuracy (96.0–98.7%) and precision rates (82.14–83.78%). Benchmarking against deep-learning models such as YOLOv5x and Mask R-CNN showed comparable performance, while requiring only one-third to one-fifth of the cost and avoiding complex infrastructure. The Batch Detection (BD) mode significantly reduced processing time compared to Continuous Detection (CD), enhancing real-time applicability. The DDRP-Machine demonstrates strong potential to improve seedling inspection efficiency and reduce labor dependency in nursery operations. Its modular design and minimal hardware requirements make it a practical and scalable solution for resource-limited environments. This study offers a viable pathway for small-scale farms to adopt intelligent automation without the financial burden of high-end AI systems. Future enhancements, adaptive lighting and self-learning capabilities, will further improve field robustness and including broaden its applicability across diverse nursery conditions.
Details
Artificial neural networks;
Programmable logic controllers;
Shortages;
Small farms;
Small-scale farming;
Image processing;
Automation;
Machine learning;
High performance computing;
Efficiency;
Cameras;
Seedlings;
Machine vision;
Graphics processing units;
Labor shortages;
Algorithms;
Modular design;
Surveillance;
Real time;
Nurseries;
Accuracy;
Deep learning;
Inspection;
Color;
Agriculture;
Stepping motors;
Low cost;
Control algorithms;
Edge computing;
Labor;
Positioning devices (machinery);
Farms;
Morphology
1 Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Taichung 402202, Taiwan
2 Department of Animal Science and Technology, National Taiwan University, Taipei 106032, Taiwan, Bioenergy Research Center, College of Bio-Resources and Agriculture, National Taiwan University, Taipei 106319, Taiwan, Agricultural Net-Zero Carbon Technology and Management Innovation Research Center, College of Bio-Resources and Agriculture, National Taiwan University, Taipei 106319, Taiwan