Machine vision technology has been widely applied to various fields of agricultural production. In this paper we summarize the outstanding research results at home and abroad and elaborate the main forms of machine vision application in agriculture at this stage. We list the research results of machine vision in crop selection and quality inspection, plant growth information monitoring, field vision navigation, and other application directions. By analyzing its innovative image processing algorithms and the composition of machine vision systems, we show that the current application of machine vision in agriculture is still having problems such as poor reliability, high cost, and low level of intelligence. We analyze its subjective and objective reasons and put forward corresponding suggestions. Combining the current research and application of machine vision in various fields, we look forward to the future development direction of machine vision in agricultural applications. We believe that machine vision systems based on embedded processing modules and multi-technology integration will become the main development trend in the future. The deep learning model represented by convolution neural network will also become the core technology of future image recognition, which can greatly help solve the existing problems of machine vision in agricultural applications.

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