# Research Progress in Intelligent Diagnosis Key Technology for Orchard Nutrients
## 4 Discussion  
Tree nutrient rapid detection technology based on RGB images mainly predicts nutrient content based on color feature parameters, and its equipment and computational costs are relatively low. Changes in lighting conditions have a significant impact on nutrient prediction results, which has been validated by experimental results. It had high accuracy in leaf detection [3; 4], but low accuracy in canopy detection [1; 2]. Preprocessing algorithms were mainly aimed at removing the background and correcting colors, without addressing color differences caused by lighting changes.  
The application for the multispectral detection of tree nutrients is based on vegetation indices, and the data processing volume is not large. It is often mounted on drones and has high efficiency. Considering environmental changes is crucial as it is directly detected in the field. Compared with existing research, it can also be found that without data preprocessing, the prediction accuracy was lower [5; 6]. Considering the influence of tree structure, background, and lighting intensity, the prediction accuracy was greatly improved [7; 9]. A RGB-NIR multispectral method has also been used to address the issue of uneven illumination [18].  
Compared to multispectral data, hyperspectral data has more spectral bands, which means there are more characteristic variable parameters that can predict nutrients. So it will have higher accuracy, but it will bring significant challenges to data processing. The application of filtering and transformation algorithms for preprocessing effectively eliminated interference information and improved correlation [14; 15]. The preprocessing algorithm for filtering sensitive feature bands reduced data processing and computational costs [10; 13]. The studies on the hyperspectral detection of tree nutrients have mostly been at the laboratory level, with limited field applications. The combination of hyperspectral and multispectral did not show significant gain.  
Rapid detection technologies for soil nutrients based on electrochemistry, high-temperature excitation and artificial olfaction have the advantage of low cost. However, their characteristic variables are single, easily affected by soil components, and have certain limitations. They also require sample preprocessing, such as preparing solutions [19], heating [22], and so on.  
The hyperspectral detection of soil nutrients has been given extensive research, and like detecting tree nutrients, it has also applied filtering, transformation [26; 31], and sensitive band screening algorithms [27] to solve the problem of data redundancy. The impact of soil moisture has also been considered [33]. From the results, it can be found that it had high prediction accuracy. Laser-induced breakdown spectroscopy is atomic spectroscopy that is generally superior for analytical applications with multi-element measurements [37]. Its research has just begun, and due to the complexity and diversity of soil components, spectral analysis poses high challenges. From the results, it can be observed that the combination spectrum did not show a significant improvement in predictive performance.  
At present, many scholars have obtained soil and leaf suitable nutrient standards through long-term and extensive experiments, providing a basis for the intelligent diagnosis of fruit tree nutrients. However, different varieties of fruit trees have different fertilizer requirements for growth, and suitable nutrient standards are not universally applicable, so experimental research needs to be conducted again.