###### Abstract  
Soil nutrient status is the foundation of agricultural development. Exploring the features of soil nutrients and status evaluation can provide a reference for the development of modern agriculture. LightGBM is an optimization algorithm based on the boosting framework, which uses histograms to improve the accuracy of the model. Based on the construction of the LightGBM model, the main nutrient features and status of tobacco planting soil were analyzed in seven towns in Debao County, Guangxi Province, namely Yantong Town, Longguang Town, Najia Town, Zurong Town, Du'an Town, Dongling Town and Jingde Town. The confusion matrix results show the accuracy of the LightGBM model is 94.2%, and the eigenvalue analysis shows that the available potassium (K) contributes the most to the nutrient status. The pH value of soil ranging from 6.1 to 7.8 is favorable for tobacco growth, and the contents of soil organic matter, total nitrogen (N), available phosphorus (P), exchangeable calcium (Ca) and exchangeable magnesium (Mg) are at the appropriate level. Available potassium (K) and available zinc (Zn) are at a high level, but available boron (B) is slightly insufficient. The nutrient status of 10% of soil is at an extremely high level, and about 81.03% of soil is medium level or above. The LightGBM model has high reliability in the automatic evaluation of soil nutrient status, which not only can accurately monitor the soil nutrient status but also reflects the correlation and importance of nutrient factors. Therefore, the LightGBM model is significant for guiding soil cultivation and agricultural production. LightGBM model; tobacco planting soil; confusion matrix; eigenvalue analysis; nutrient features; nutrient status +
Footnote †: journal: Applied  
the soil nutrient status [8]. The commonly used methods include principal component analysis, cluster analysis, the fuzzy mathematical model membership function, Nemero comprehensive index and gray correlation analysis [9]. In recent years, machine learning as a new subject has received wide attention in various fields. As an extension of applied statistics, machine learning is very suitable for the application and research of agronomy and geosciences [10]. For example, random forest and XGBoost in integrated algorithms are often used to solve classification and regression problems in geochemical research due to their good performance [11; 12]. [PERSON] et al. [13] established a random forest model for automatic evaluation of soil nutrient status, and the results are objective and accurate. [PERSON] et al. [14] found that the results of risk assessment and prediction are more accurate than the traditional research methods in the risk assessment of waterlogging in the central cities of the Yangtze River Delta based on the XGBoost model. LightGBM is the Microsoft's latest developed source framework, which uses the histogram decision-tree algorithm and is regarded as an improved version of XGBoost. Compared with previous models, LightGBM has the advantages of fast training speed, high accuracy, less memory and more objective training results, which are mainly used to deal with the classification and regression problems of data analysis [15; 16]. Therefore, applying the LightGBM model to the research of soil nutrient status can obtain more objective and accurate evaluation results. Debao County is one of the main tobacco planting areas in Baise City, which is the largest tobacco planting area in Guangxi Province, with an area of about 8167 ha, accounting for 78% of the tobacco planting area in the province [17; 18]. At present, there are few in-depth studies on the evaluation of soil nutrient status and the status of soil nutrient dynamics in Debao County, and the main research methods are traditional and lack innovation.