Crop yield prediction helps to enhance the stability of agricultural product supply and promote sustainable agricultural development, both of which are crucial for food production and security. To develop simple yet highly accurate crop yield prediction models, this study proposed a spring- and summer-maize yield prediction model based on the deep hybrid kernel extreme learning machine (DHKELM) algorithm. In this study, four tree-based feature importance analysis algorithms, including classification and regression tree, gradient boosting decision tree, random forest, and extreme gradient boosting algorithms, were utilized to analyze the importance of the factors affecting the yield of spring and summer maize. Then, based on the analysis of the four algorithms, different combinations of factors were established to obtain the optimal combination of features. Moreover, to improve the prediction accuracy of the machine learning model, this study utilized three optimization algorithms, including the bald eagle search algorithm, chaos game optimization (CGO) algorithm, and carnivorous plant algorithm, to optimize the hyperparameters in the DHKELM algorithm. The results of the study showed that planting density and plant height were important factors affecting maize yield, and net solar radiation (R_n) received during the reproductive period exhibited the highest relative importance. Appropriate feature combinations can effectively improve model prediction accuracy. The optimal feature combination for spring maize included planting density, plant height, R_n, mean temperature (T_mean), minimum temperature (T_min), and cumulative temperature, and the optimal feature combination for summer maize included R_n, plant height, planting density, T_min, and T_mean. Among the three optimization algorithms, the CGO algorithm exhibited the best optimization effect and could significantly improve the prediction accuracy of the DHKELM algorithm. When the optimal combination of features was used as input, the CGO–DHKELM model used for maize yield prediction provided the following values: RMSE=1.488 t/hm², R²=0.862, MAE=1.051 t/hm², and NSE=0.852 for spring maize; RMSE=1.498 t/hm², R²=0.892, MAE=1.055 t/hm², and NSE=0.891 for summer maize. Thus, the findings of the study provide a reference for high-precision prediction of spring and summer maize yields in China.

Accurate detection of tomato leaf diseases is crucial for early prevention and ensuring agricultural production. This study addresses six tomato leaf diseases: bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, and yellow leaf curl virus. A lightweight detection model, YOLO-LGS, is proposed to achieve efficient and automated disease detection. The dataset of tomato leaf diseases was first augmented to enrich the disease features, thereby improving the model’s detection performance. The YOLO-LGS model is built on the YOLOv11 architecture, incorporating lightweight group attention net (LWGANet) to reconstruct the backbone network, replacing the convolutional block with parallel spatial attention mechanism with the grouped channel-wise self-attention (GCSA) mechanism, and introducing separated and enhanced attention module (SEAM) into the detection head to balance performance and efficiency. Experimental results show that the YOLO-LGS model achieves an mAP50 of 0.693 and an F1 score of 0.677, outperforming other YOLO models (YOLOv8s, YOLOv9s, YOLOv10s, and YOLOv11s). Additionally, the model’s parameter size is only 6.333 M, and its GFLOPs is 13.4, representing reductions of 32.739% and 37.089%, respectively, compared to YOLOv11s, significantly lowering computational cost while maintaining detection performance. The results demonstrate the effectiveness of LWGANet, GCSA, and SEAM. The development of the YOLO-LGS model provides an efficient, lightweight solution for tomato leaf disease detection in resource-constrained environments.

Soil moisture is a critical component of the soil-plant-atmosphere continuum (SPAC) in fruit trees. However, high-precision monitoring of orchard soil moisture at the regional scale still remains a challenge. This study presents a two-stage feature space model to estimate root zone soil moisture using UAV remote sensing data. The results indicate that the temperature-leaf area index (TLDI) is negatively correlated with soil water content. The upper triangular space performs highly effectively for deep soil moisture inversion, with R² values ranging from 0.56 to 0.66, RMSE between 0.20 and 0.27, and RPD from 1.25 to 1.50. Conversely, the lower triangular space yields superior results for shallow soil moisture inversion, with R² values between 0.67 and 0.82, RMSE from 0.15 to 0.19, and RPD between 1.67 and 2.09. The results suggest that the lower triangular space is optimal for shallow soil moisture inversion, while the upper triangular space is more suited for deep soil moisture inversion. This study presents a novel approach for estimating deep soil moisture in orchards, providing a theoretical basis for improving soil moisture management.

As a crucial fruit tree crop, the health and yield of apple trees are intricately linked to soil moisture conditions. This study aimed to integrate the enhanced WOFOST model with the HYDRUS model to simulate the growth and development of apple trees, as well as the dynamics of soil moisture under varying degrees of water deficit. The outputs of evapotranspiration (ET₀) and leaf area index (LAI) from the WOFOST model during the apple growth phase were specifically integrated with HYDRUS-1D. These parameters served as intermediaries to assess the impact of different water deficit scenarios on apple tree growth and soil moisture content. The experimental design included three levels of water deficit treatments in addition to control, with irrigation volumes for the deficit treatments set at 85%, 70%, and 55% of the control’s volume, respectively. The model-predicted LAI across all irrigation treatments exhibited an R² range of 0.89-0.95, a normalized root mean square error (NRMSE) between 8.02% and 14.57%, and yield prediction errors ranging from 6.27% to 9.61%, closely aligned with empirical data. The accuracy of simulated soil moisture content was enhanced in the 0-30 cm layer, with a slight decrease in accuracy observed in the 30-60 cm layer. For each irrigation treatment, the R² values for simulated soil moisture content ranged from 0.77 to 0.89 in the 0-30 cm layer and from 0.75 to 0.81 in the 30-60 cm layer. This study validated the capability of the WOFOST-HYDRUS model to accurately simulate the effects of varied water deficit treatments on soil moisture, LAI, and apple tree yield, providing valuable insights for developing optimal irrigation strategies.