Dataset: Sensing Potential in the Food Supply Chain - Mango

The overall aim of this research is to explore the feasibility of sensing technology to measure non-destructively fruit quality properties on a batch and an individual product level.<br>The specific objective for this dataset was to utilize various sensor data inputs for AI models aimed at quantifying the brix (sugar content) and firmness of mangoes. AI models were developed using data from individual sensors, as well as models based on the fusion of all sensor data. The brix and firmness were modeled using a multi-sensor approach, incorporating ultrasound and spectroscopy signals. Refractometer measurements for brix and puncture measurements for firmness served as the reference standards.

本研究的总体目标为探究传感技术在批量及单果层面无损检测果品品质属性的可行性。
本数据集的具体研究目标为：利用各类传感器数据输入，构建用于量化芒果白利糖度（brix）与硬度的人工智能模型（AI models）。研究中分别基于单传感器数据开发了人工智能模型，同时构建了融合全传感器数据的人工智能模型。本次对白利糖度与硬度的建模采用多传感器方案，整合了超声波（ultrasound）与光谱法（spectroscopy）信号。以折射仪（refractometer）测得的白利糖度结果、以及穿刺检测（puncture measurements）测得的硬度结果作为参考标准。