Online microwave drying process monitoring has been challenging due to the incompatibility of metal components with microwaves. This paper developed a microwave drying system based on online machine vision, which realized real-time extraction and measurement of images, weight, and temperature. An image-processing algorithm was developed to capture material shrinkage characteristics in real time. Constant-temperature microwave drying experiments were conducted, and the artificial neural network (ANN) and extreme learning machine (ELM) were utilized to model and predict the moisture content of materials during the drying process based on the degree of material shrinkage. The results demonstrated that the system and algorithm operated effectively, and ELM provided superior predictive performance and learning efficiency compared to ANN.
Color characteristics are a crucial indicator of green tea quality, particularly in needle-shaped green tea, and are predominantly evaluated through subjective sensory analysis. Thus, the necessity arises for an objective, precise, and efficient assessment methodology. In this st…
Accurate cutting of salmon parts and surface defect detection are the key steps to enhance the added value of its processing. At present, mainstream manual inspection methods have low accuracy and efficiency, making it difficult to meet the demands of industrialized production. A…
The grade of Daqu significantly influences the quality of Baijiu. To address the issues of high subjectivity, substantial labor costs, and low detection efficiency in Daqu grade evaluation, this study focused on light-flavor Daqu and proposed a two-layer classification structure…
Rapid online detection of broken rate can effectively guide maize harvest with minimal damage to prevent kernel fungal damage. The broken rate prediction model based on machine vision and machine learning algorithms is proposed in this manuscript. A new dataset of high moisture c…
This study develops a comprehensive workflow integrating Headspace Solid-Phase Microextraction Gas Chromatography–Mass Spectrometry (HS-SPME-GC-MS) with advanced supervised machine learning to authenticate the botanical origin of honeys from five distinct floral sources—coriander…
Hyperspectral imaging (HSI) has broad applications for detecting the soluble solids content (SSC) of fruits. This study explores the integration of HSI with machine learning and deep learning to predict SSC in two mandarin varieties: Ponkan and Tianchao. Traditional machine learn…