## 1 Introduction  
Jujube (_Ziziphus jujuba_ Mill.) is a notable agricultural commodity, experiencing a rise in both cultivation area and yield annually. It is a medicinal and edible fruit that originates from China [1]. The sweet and delicious jujube fruit is rich in vitamin C, amino acids, cyclic adenosine monophosphate, carbohydrates, and minerals like iron and potassium [2]. Regular consumption of this fruit is beneficial for human health and is popular among consumers. Along with the improvement in our living standards, consumers have higher requirements for the quality of rice. Consequently, quality assessment has gained importance. During the processing of jujube fruits, quality testing is crucial. Two key parameters that indicate quality are moisture content (MC) and soluble solids content (SSC). Soluble solids are a comprehensive term for all water-soluble compounds in food, such as sugar, acid, and vitamins. Their content significantly impacts the taste and ripeness of jujube fruit, serving as a key quality indicator [3]. Moisture content directly affects the taste and storage period of jujube fruit and is also one of the main indicators of jujube fruit quality [4]. Therefore, it is essential to accurately and efficiently measure these parameters. Traditional methods for testing MC and SSC in jujube fruit involve using specialized equipment for destructive testing. While these methods yield high accuracy, the testing process is intricate and time-consuming [5]. The progress in spectral technology and associated hardware capabilities has sparked interest in utilizing spectral technology for agricultural product detection. This technology can identify ingredients and determine their content based on the spectral data of a substance [5]. Unmanned aerial vehicle (UAV) multispectral imaging detection technology enables rapid acquisition of orchard spectral data across a broad area [6; 7]. These data can be used to infer the fruit tree growth status and soil quality in orchards [8; 9; 10; 11]. In recent years, this technology has gained attention from numerous scholars, who have conducted extensive research in areas such as processing [12; 13], feature extraction [11; 14], and recognition model construction [15; 16; 17]. [PERSON] et al. [18] employed UAV remote sensing technology to quantify the nitrogen nutrient content of leaves using multispectral images. The findings revealed that this method exhibited a slight advantage in detecting nitrogen nutrient content during the early fruit expansion stage compared to the subsequent stages. [PERSON] et al. [19] conducted a correlation analysis on UAV multispectral image data and field-measured leaf nitrogen content (LNC), leaf potassium content (LKC), and leaf phosphorus content (LPC) values. They determined the vegetation index (VI) that is sensitive to LNC, LKC, and LPC. Prediction models were established using partial least squares (PLS), random forest (RF), support vector machine (SVM), and extreme learning machine (ELM) machine learning algorithms. The optimal combination of spectral variables and machine learning models for predicting LNC, LPC, and LKC for each grape growth period was ultimately determined. To predict soil volumetric water content (VWC) and electrical conductivity (EC), [PERSON] et al. [20] utilized VI and original multispectral data acquired from UAV to develop a prediction model using the random forest method. The study revealed that the prediction outcomes were influenced by varying soil moisture conditions. The detection accuracy of multispectral cameras is generally low due to their limited number of filters. Typically, these cameras can only provide qualitative analysis of samples [21; 22]. In spectral detection technology research, researchers often use either single-directional measurement or integrating sphere measurement to obtain reflection spectrum information [23; 24; 25].