Attention to the natural environment is equivalent to observing the space in which we live. Plant roots, which are important organs of plants, require our close attention. The method of detecting root system without damaging plants has gradually become mainstream. At the same time, machine learning has been achieving good results in recent years; it has helped develop many tools to help us detect the underground environment of plants. Therefore, this article will introduce some existing content related to root detection technology and machine detection algorithms for root detection, proving that machine learning root detection technology has good recognition capabilities.

Root diameter is an important indicator of plant growth and development to a large extent. However, the field monitoring of these parameters is severely limited by the lack of appropriate methods, and some traditional methods may harm the plant and its growing environment. Ground-penetrating radar (GPR) is a new nondestructive detection method for underground root systems. A new method for the estimation of the diameter of coarse roots using GPR with 900 MHz frequency was proposed in this paper. First, a simulation model was established to simulate the root system under natural conditions, and the root diameter estimation model based on the scanning results of GPR was obtained. Secondly, by studying the influence of soil and root relative permittivity on the diameter estimation model, a method was found to devise a coarse root diameter estimation model under different soil and root conditions of relative permittivity. Thirdly, the applicability of the diameter estimation model to roots with different growth orientations was tested by simulating roots with different growth orientations. Finally, the practical applicability of the estimation method was verified by field experiments. The results suggest that the root diameter estimation model can be constructed by extracting the pixel distance (∆p) of waveform parameters from the 900 MHz scanning results. This method can be used to estimate the diameter of coarse roots with diameters of no less than 2 cm and a relative permittivity greater than 5, and to estimate the diameter of roots in any orientation and soil environment effectively. At the same time, the application in the field experiment also resulted in a good estimation effect. This method provides a new opportunity to achieve more reliable root diameter estimation in complex situations. The estimation of coarse root diameter provides an experimental basis and data support for the healthy growth of trees, and also provides some information for the study of coarse root ecology.