Research progress of image acquisition platform for crop classification and recognition based on CNN

Accurate crop classification and recognition based on machine vision is the premise of agricultural automation and intelligent operation. Convolution neural network（CNN） is one of the most widely used algorithms in crop image classification and recognition. The complexity of crop phenotypic characteristics and growth environment determines the diversity of crop image acquisition platforms. Through the analysis of crop classification and recognition research based on CNN at home and abroad from 2020 to 2022, the image acquisition platforms can be divided into two categories such as general platform and self-built platform, among which the general platform hardware products are mature and easy to deploy, but equipment selection and environment construction should be done well. The self-built platform is divided into fixed and mobile ones, which can obtain experimental data efficiently, but the hardware integration is more complicated. The advantages and disadvantages of various platforms and their applicable scope are compared and analyzed in detail. The future trends of crop image acquisition platforms will include high-throughput, high-efficiency, automated universal image acquisition devices, multi-modal data acquisition and fusion applications integrating a variety of sensors, intelligent cameras with built-in computing processing, etc. The more refined image acquisition platforms will effectively support in-depth research on crop phenotypes.