Classification techniques of ion selective electrode arrays in agriculture: a review

Abstract

Agriculture has a substantial demand for classification, and each agricultural product exhibits a unique ion signal. This paper summarizes the classification techniques of ion-selective electrode arrays in agriculture. Initially, data sample collection methods based on ion-selective electrode arrays are summarized. The paper then discusses the current state of classification algorithms from the perspectives of machine learning, artificial neural networks, extreme learning machines, and deep learning, along with their existing research in ion-selective electrodes and related fields. Then, the potential applications in crop and livestock growth status classification, soil classification, agricultural product quality classification, and agricultural product type classification are discussed. Ultimately, the future challenges of ion-selective electrode research are discussed from the perspectives of the sensor itself and algorithms combined with sensor arrays, which also positively impact the promotion of their application in agriculture. This work will advance the application of classification techniques combined with ion-selective electrode arrays in agriculture.