The pistachio quality detection based on deep features plus unsupervised clustering

Abstract

AbstractThe opening condition of the pistachio is a trait that has been sorted by machine vision. This study presented a qualitative and quantitative method for pistachio opening detection based on unsupervised learning. First, the convolution neural network ResNet18 was utilized to extract the pistachio features based on the different layers: bn4b_branch2b (bn4b), bn5b_branch2b (bn5b), and fc1000. The high‐dimensional feature was mapped to the low‐dimensional by t‐distributed stochastic neighbor embedding algorithm, and then, the clustering algorithms were utilized to cluster the image feature under different spatial dimensions. It was best that the clustering performance came from the bn5b feature layer. The accuracy of this method was 99.31% in three‐dimensional space. And completed the analysis of pistachio opening degree. This method exhibits significant potential for enhancing the quality detection and product classification of pistachios. The unsupervised clustering is utilized to sort not only pistachios but also general nuts.Practical ApplicationsIn this study, we proposed a quality detection method for pistachios based on unsupervised learning, which was capable of performing both qualitative and quantitative analyses of pistachio opening. This method provides an efficient quality detection and product classification strategy, with important practical value. The unsupervised clustering is utilized to sort not only pistachios but also general nuts. Furthermore, it significantly reduces the consumption of manpower and material resources, paving the way for the development of intelligent sorting equipment in the future, and has reference significance for the quality detection of other agricultural products.