Academic Journal
Simple and Affordable Vision-Based Detection of Seedling Deficiencies to Relieve Labor Shortages in Small-Scale Cruciferous Nurseries.
| Title: | Simple and Affordable Vision-Based Detection of Seedling Deficiencies to Relieve Labor Shortages in Small-Scale Cruciferous Nurseries. |
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| Authors: | Su, Po-Jui, Chen, Tse-Min, Su, Jung-Jeng |
| Source: | Agriculture; Basel; Nov2025, Vol. 15 Issue 21, p2227, 24p |
| Subject Terms: | Computer vision, Automation, Inspection & review, Modular design, Seedling quality, Image processing, Labor market, Plant nurseries |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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