A Low-Cost Vegetable Quality Assessment System Based on Microscopy Images in Deep Learning Edge Computing: A Pilot Study on Potato Tuber

This work details design and development of a microscopy image-based vegetable quality assessment system (Prototype) by adopting deep learning (DL) technique on edge device. Current automated machine learning methods primarily utilize outer-surface images of vegetables/fruits, often lacking in preci...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on consumer electronics 2024-01, Vol.70 (3), p.6343-6353
Main Authors: Biswas, Sumona, Barma, Shovan
Format: Article
Language:English
Subjects:
Algorithms
Carbohydrates
Comparative studies
Computer architecture
Deep learning
Edge computing
Electron microscopy
Foldscope
Food quality
Fruits
Image edge detection
Image processing
Image quality
Image segmentation
Image synthesis
Lightweight
lightweight network
Low cost
Machine learning
Microprocessors
Microscopy
Pilot projects
Portable equipment
potato
Potatoes
Prototypes
Quality assessment
Real time
Smart phones
smartphone
Smartphones
vegetable quality
Vegetables
Vitamins
Weight reduction
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This work details design and development of a microscopy image-based vegetable quality assessment system (Prototype) by adopting deep learning (DL) technique on edge device. Current automated machine learning methods primarily utilize outer-surface images of vegetables/fruits, often lacking in precise quantification of nutrient content such as carbohydrates, minerals, vitamins, etc. Indeed, such nutrient ingredients can be assessed by examining micro-level cell attributes of microscopy images in DL framework. However, vegetable quality detection based on microscopy/DLs on resource-constrained edge devices poses significant challenges. To address these problems, a portable, cost-effective, efficient, and real-time prototype has been realized. It involves configuring a microscopy image generation module using low-cost Foldscope lens coupled with smartphones and on-device analysis by designing a new lightweight DL architecture and segmentation algorithm. The analysis is executed via a smartphone application, ensuring advantages like bandwidth and energy efficiency, user privacy, local processing without external servers. For system validation, a pilot study has been conducted on the widely consumed potato tuber, focusing on the assessment of starch presence as a key quality metric. The system successfully assesses cell attributes, i.e., starch quantity of 10-25% in ~24s, which is very much consistent. In a comparative study, the network outperforms the existing state-of-the-art lightweight networks by achieving the highest recognition accuracy upto 88.8% and F1-score 85.83 with lesser parameters (1.5M) and FLOPs (118M). Thus, the study demonstrates its applicability for vegetable quality assessment in an easy, affordable, and effective way. Further, the proposed idea can be extended to other vegetables/fruits.
ISSN:0098-3063
1558-4127
DOI:10.1109/TCE.2024.3412101