Research Progress in Intelligent Diagnosis Key Technology for Orchard Nutrients

Abstract

The intelligent diagnosis key technology of orchard nutrients provides a decision-making basis for precision fertilization, which has important research significance. This article reviewed the recent research literature, compared and analyzed existing technologies, and summarized solved and unresolved problems. It aimed to find breakthroughs to further improve the level of intelligent diagnosis key technology for orchard nutrients, and promote the implementation and application of the technology. Research had found that the current rapid nutrient detection technologies were mostly based on spectral data, with a focus on preprocessing algorithms and regression models. Hyperspectral technology shows good performance in predicting tree and soil nutrients due to its large number of characteristic variables. Meanwhile, preprocessing algorithms such as filtering, transformation, and feature band selection had also solved the problem of data redundancy. However, there were few studies for small and trace elements, and field applications. Laser breakdown-induced spectroscopy has good prospects for soil nutrient detection, as it can simultaneously detect multiple nutrients. There had been some studies on the technology for generating suitable nutrient standards for orchards in terms of soil and tree nutrients, but it requires a long and extensive experiment, which is time-consuming and laborious. A universal and rapid method needs to be studied to meet the construction needs of suitable nutrient standards for different varieties of fruit trees.