ArticlePloS one2026
Design and evaluation of an embedded automation system for optimized cut-shape placement on coconut shells in sustainable key tag manufacturing.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis paper presents the design and evaluation of a novel embedded automation system for optimized cut-shape placement on coconut shells in sustainable key tag manufacturing. The system addresses inefficiencies in manual marking by improving speed, accuracy, and material utilization, while ensuring affordability and suitability for decentralized artisanal contexts. DESIGN/METHODOLOGY/APPROACH: The system integrates a Raspberry Pi 4, VL53L0X Time-of-Flight sensor, Pi-Camera, MG995 servos, and a low-power laser module, orchestrated through a Python-based finite-state machine (FSM). A lightweight CNN model deployed via TensorFlow Lite enables real-time classification of shell geometries. Comparative experiments were conducted on 20 coconut shells under both manual and automated marking conditions. Evaluation metrics included marking time, accuracy of placement, material utilization, and productive yield.
findingsThe proposed system demonstrated significant improvements over manual marking: marking time was reduced by 26.0%, placement accuracy improved by 79.4%, and material utilization increased by 38.4%. The average number of usable cuts per shell increased from 2.85 (manual) to 3.89 (automated), representing a 36.5% gain in productive yield. All improvements were statistically significant (p < 0.05). ORIGINALITY: This is the first known application of a low-cost, edge-AI-enabled embedded system combining ToF sensing, CNN-based shape classification, and adaptive actuation for real-time marking on irregular natural materials. The system dynamically adapts to geometric variability in biodegradable substrates, an area underserved by conventional automation solutions. PRACTICAL IMPLICATIONS: The system is cost-effective (<$132), lightweight (<2.5 kg), and fully modular, making it suitable for deployment in SMEs, rural micro-factories, and vocational training centers. SOCIAL IMPLICATIONS: By augmenting artisanal labor rather than replacing it, the system supports inclusive digital transformation in low-resource environments and contributes to SDGs 8, 9, and 12. It is also generalizable to other natural materials, supporting broader applications in sustainable, circular manufacturing.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.