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 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.