The agricultural machine vision system is intelligent and automatic in spraying pesticides and fertilizing, harvesting, weeding, and pest detection. Compared with other technologies, machine vision system has high efficiency and low cost, so it has been popularized and applied. In order to solve the shortcomings of the existing agricultural machine vision system research, this paper discusses the composition, key technologies and machine learning algorithms of agricultural machine vision system, and briefly discusses the system hardware selection and software development environment for the application of agricultural machine vision system. And the overall results of agricultural machine vision monitoring system are designed and discussed. The convolutional neural network (RCNN) and K-means algorithm in machine learning are used to study the identification and classification of seedlings in images. Finally, through the experimental analysis of selected samples, it is known that the accuracy of RCNN and K-means algorithm in image recognition detection in agricultural machine vision monitoring system is up to 94.25%. Therefore, it is verified that machine learning algorithm has high practical value in agricultural machine vision system.

Malik Almulihi. Machine Learning Algorithm in Agricultural Machine Vision System. Machine Learning Theory and Practice (2020), Vol. 1, Issue 4: 26-35. https://doi.org/10.38007/ML.2020.010404.

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