Rapid acquisition of chlorophyll content in maize leaves is of great significance for timely monitoring of maize plant health and guiding field management. In order to accurately detect the relative chlorophyll content of summer maize and study the responsiveness of vegetation indices to SPAD (soil and plant analyzer development) values of summer maize at different spatial vertical scales, this paper established a prediction model for SPAD values of summer maize leaves at different spatial scales based on UAV multispectral images. The experiment collected multispectral image data from summer maize at the jointing stage and selected eight vegetation indices. By using the sparrow search optimized kernel limit learning machine (SSA-KELM), the prediction models for canopy leaf (CL) SPAD_CL and ear leaf (EL) SPAD_EL were established, and a linear fitting analysis was conducted combining the measured SPAD_CL values and SPAD_EL values on the ground. The results showed that for SPAD_CL, the R² of the linear fitting between the predicted values and measured values was 0.899, and the RMSE was 1.068. For SPAD_EL, the R² of linear fitting between the predicted values and the measured values was 0.837, and the RMSE was 0.89. Compared with the model established by the partial least squares method (PLSR), it is found that the sparrow search optimized kernel limit learning machine (SSA-KELM) has more precise prediction results with better stability and adaptability for small sample prediction. The research results can provide technical support for remote sensing monitoring of the chlorophyll content of summer maize at different spatial scales.

The accurate estimation of reference crop evapotranspiration (ET_O) plays an important role in guiding regional water resource management and crop water content research. In order to improve the accuracy of ET_O prediction in regions with missing data, this study used the partial correlation analysis method to select factors that have a large impact on ET_O as input combinations to construct ET_O estimation models for typical stations in semi-arid regions of China. A biological heuristic optimization algorithm (Golden Eagle optimization algorithm (GEO) and Sparrow optimization algorithm (SSA)) and Extreme Learning Machine model (ELM) were combined to improve the estimation accuracy. The results showed that Ra was the primary factor affecting the ET_O model, with an importance range of 0.187–0.566. Compared with the independent ELM model, the hybrid model has higher accuracy and stability. The estimated value of the SSA-ELM model under five-factor input condition (Ra, RH, T_max, T_min, U₂) is closest to the standard value calculated by FAO56 PM: RMSE = 0.067–0.085, R² = 0.998–0.999, MAE = 0.050–0.066 and NSE = 0.998–0.999. In general, the combination of a partial correlation analysis algorithm and a hybrid model can be used to estimate ET_O with high accuracy under the condition of reducing input factors. Use of the first five factors extracted from the partial correlation analysis algorithm as input to build an ET_O estimation model based on SSA-ELM in China’s semi-arid regions is recommended, which can also provide a reference for ET_O estimation in similar regions.

Precise estimation of daily reference crop evapotranspiration (ET₀) is critical for water resource management and agricultural irrigation optimization worldwide. In China, diverse climatic zones pose challenges for accurate ET₀ prediction. Here, we evaluate the performance of a support vector machine (SVM) and its hybrid models, PSO-SVM and WOA-SVM, utilizing meteorological data spanning 1960–2020. Our study aims to identify a high-precision, low-input ET₀ estimation tool. The findings indicate that the hybrid models, particularly WOA-SVM, demonstrated superior accuracy with R² values ranging from 0.973 to 0.999 and RMSE values between 0.123 and 0.863 mm/d, outperforming the standalone SVM model with R² values of 0.955 to 0.989 and RMSE values of 0.168 to 0.982 mm/d. The standalone SVM model showed relatively lower accuracy with R² values of 0.822 to 0.887 and RMSE values of 0.381 to 1.951 mm/d. Notably, the WOA-SVM model, with R² values of 0.990 to 0.992 and RMSE values of 0.092 to 0.160 mm/d, emerged as the top performer, showcasing the benefits of the whale optimization algorithm in enhancing SVM’s predictive capabilities. The PSO-SVM model also presented improved performance, especially in the temperate continental zone (TCZ), subtropical monsoon region (SMZ), and temperate monsoon zone (TMZ), when using limited meteorological data as the input. The study concludes that the WOA-SVM model is a promising tool for high-precision daily ET₀ estimation with fewer meteorological parameters across the different climatic zones of China.

Cotton is one of the world’s most important economic crops, and its yield and quality have a significant impact on the agricultural economy. However, Verticillium wilt of cotton, as a widely spread disease, severely affects the growth and yield of cotton. Due to the typically small and densely distributed characteristics of this disease, its identification poses considerable challenges. In this study, we introduce YOLO-MSPM, a lightweight and accurate detection framework, designed on the YOLOv11 architecture to efficiently identify cotton Verticillium wilt. In order to achieve a lightweight model, MobileNetV4 is introduced into the backbone network. Moreover, a single-head self-attention (SHSA) mechanism is integrated into the C2PSA block, allowing the network to emphasize critical areas of the feature maps and thus enhance its ability to represent features effectively. Furthermore, the PC3k2 module combines pinwheel-shaped convolution (PConv) with C3k2, and the mobile inverted bottleneck convolution (MBConv) module is incorporated into the detection head of YOLOv11. Such adjustments improve multi-scale information integration, enhance small-target recognition, and effectively reduce computation costs. According to the evaluation, YOLO-MSPM achieves precision (0.933), recall (0.920), mAP50 (0.970), and mAP50-95 (0.797), each exceeding the corresponding performance of YOLOv11n. In terms of model lightweighting, the YOLO-MSPM model has 1.773 M parameters, which is a 31.332% reduction compared to YOLOv11n. Its GFLOPs and model size are 5.4 and 4.0 MB, respectively, representing reductions of 14.286% and 27.273%. The study delivers a lightweight yet accurate solution to support the identification and monitoring of cotton Verticillium wilt in environments with limited resources.

Estimating soil moisture content in apple orchards using UAV remote sensing data: Application of LST/LAI two-stage feature space theory

Simulation of the soil water content under different water deficits for apple trees via improved WOFOST-HYDRUS coupled model