## 5 Conclusions  
This study utilized the GEE to select Landsat 9 satellite remote sensing images from the entire year of 2022 for evaluating cultivated land quality. The spectral reflectance of all bands from the selected Landsat 9 remote sensing images during typical periods was used as feature indicators for model input. An improved BO-Stacking-TabNet model was then employed to assess cultivated land quality at the farm scale in the black soil region of Northeast China. The main conclusions are as follows:  
(1) Among the 66 Landsat 9 satellite remote sensing images analyzed in 2022, those from April, May, August, and October demonstrated the highest classification performance for evaluating cultivated land quality at the farm scale in the black soil region of Northeast China. This study established a preliminary farm-scale cultivated land quality assessment model using the random forest classification model on the GEE platform driven by classification metrics. By sequentially inputting all Landsat 9 remote sensing images from the entire year of 2022 into the model, the cultivated land quality assessment results were calculated and validated. The final results showed that the classification accuracy, overall accuracy, and Kappa values for the six land quality grades corresponding to the model results from the images in April, May, August, and October were the highest among all tested periods. (2) In the remote sensing-based cultivated land quality assessment model at the farm scale in the black soil region of Northeast China, the model using spectral reflectance as input indicators comprehensively outperformed the model using vegetation indices as input indicators. Specifically, the BO-Stacking-TabNet model based on spectral reflectance as input indicators showed a 10.62% improvement in Accuracy, a 1.55% improvement in Precision, an 11.05% improvement in Recall, and a 10.18% improvement in F1-score compared to the BO-Stacking-TabNet model based on vegetation indices as input indicators. (3) The BO-Stacking-TabNet model proposed in this study showed significant improvements in performance for cultivated land quality assessment compared to the original TabNet model. Using spectral reflectance as the input indicators for both models, the BO-Stacking-TabNet model demonstrated a 2.13% improvement in Accuracy, a 12.59% improvement in Precision, a 1.83% improvement in Recall, and a 2.19% improvement in F1-score over the TabNet model. (4) The cultivated land quality assessment results of the BO-Stacking-TabNet model proposed in this study are accurate and reliable. Specifically, compared with the 2022 Shuanghe Farm cultivated land quality dataset, the results of the BO-Stacking-TabNet model showed that 83.95% of the assessment grades were consistent with the dataset,13.18% differed by one grade, 2.06% differed by two grades, 0.52% differed by three grades, and 0.29% differed by four grades. The results of the BO-Stacking-TabNet model are generally consistent with the 2022 Shuanghe Farm cultivated land quality dataset. We proposed an effective method for assessing cultivated land quality at the farm scale in the black soil region of Northeast China. By combining Landsat 9 satellite remote sensing images with the BO-Stacking-TabNet model, it provides a scientific basis for obtaining rapid, accurate, and convenient data on cultivated land quality at the farm scale, thereby guiding agricultural production and management. M.G. : Conceptualization, Formal analysis, Supervision, and Project administration; Z.Y. Methodology, Data curation, Investigation, Validation, and Original draft; X.L. : Formal analysis, Methodology, Original draft preparation and Funding acquisition; H.S. : Supervision and Validation; Y.H. : Review and Editing; B.Y. : Visualization; Y.Z. : Resources. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key Research and Development Program of China, grant number 2021 YFD1500104-2.