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An intelligent prediction model for rice yield in small-scale cultivation areas can provide precise forecasting results for farmers, rice planting enterprises, and researchers, holding significant importance for agricultural industries and crop science research within small regions. Although machine learning can handle complex nonlinear problems to enhance prediction accuracy, further improvements in models are still needed to accurately predict rice yields in small areas facing complex planting environments, thereby enhancing model performance. This study employs four rice phenotypic traits, namely, panicle angle, panicle length, total branch length, and grain number, along with seven machine learning methods—multiple linear regression, support vector machine, MLP, random forest, GBR, XGBoost, and LightGBM—to construct a yield prediction model group. Subsequently, the top three models with the best performance in individual model predictions are integrated using voting and stacking ensemble methods to obtain the optimal integrated model. Finally, the impact of different rice phenotypic traits on the performance of the stacked ensemble model is explored. Experimental results indicate that the random forest model performs best after individual machine learning modeling, with RMSE, R2, and MAPE values of 0.2777, 0.9062, and 17.04%, respectively. After model integration, Stacking–3m demonstrates the best performance, with RMSE, R2, and MAPE values of 0.2483, 0.9250, and 6.90%, respectively. Compared to the performance after random forest modeling, the RMSE decreased by 10.58%, R2 increased by 1.88%, and MAPE decreased by 0.76%, indicating improved model performance after stacking ensemble. The Stacking–3m model, which demonstrated the best comprehensive evaluation metrics, was selected for model validation, and the validation results were satisfactory, with MAE, R2, and MAPE values of 8.3384, 0.9285, and 0.2689, respectively. The above research findings demonstrate that this integrated model possesses high practical value and fills a gap in precise yield prediction for small-scale rice cultivation in the Yunnan Plateau region. [ABSTRACT FROM AUTHOR]

Fatigue driving will seriously threaten public safety and health, so monitoring the brain's cognitive state accurately and exploring the fatigue process is essential. This paper proposes a 5-D Brain Cognitive Dynamic Recognition Network (E-5-D-BCDRNet) that synchronizes the brain's cognitive changes with global features, local features, and sequence changes. The global feature refers to the 3-D brain global cognitive power maps (3-D-BGCM) constructed based on the new fatigue cognitive indicators. Local features refer to the 1-D brain local cognitive array (1-D-BLCA) created based on fatigue recognition contribution of different rhythms in each brain region. This paper uses a non-invasive wearable device to collect electroencephalogram (EEG) data from 14 subjects to evaluate the model's performance. Compared with the existing advanced methods, the proposed method achieves the best detection effect (88.5%). In addition, this study finds that the features learned based on different indicators are different and distributed in various brain regions by presenting the learned characteristics. Moreover, by comparing the detection results based on the local features of a single rhythm, this study finds that the frontal and occipital regions of the driver are significantly different before and after. More importantly, only a single region can also achieve significant detection effects, which shows that our method can achieve satisfactory results based on small data. In a word, the research in this paper can lay a theoretical foundation for real-time brain fatigue detection and promote the development of intelligent transportation systems. • This paper designs new fatigue cognitive indicators for brain detection. • This paper constructs global and local features to reflect fatigue comprehensively. • We proposes an E-5-D-BCDRNet to learn global and local features simultaneously. [ABSTRACT FROM AUTHOR]