With the advancement of computer vision technology, the demand for fruit recognition in agricultural automation is increasing. To improve the accuracy and efficiency of recognizing young red pears, this study proposes an improved model based on the lightweight YOLOv9s, termed YOLOv9s-Pear. By constructing a feature-rich and diverse image dataset of young red pears and introducing spatial-channel decoupled downsampling (SCDown), C2FUIBELAN, and the YOLOv10 detection head (v10detect) modules, the YOLOv9s model was enhanced to achieve efficient recognition of small targets in resource-constrained agricultural environments. Images of young red pears were captured at different times and locations and underwent preprocessing to establish a high-quality dataset. For model improvements, this study integrated the general inverted bottleneck blocks from C2f and MobileNetV4 with the RepNCSPELAN4 module from the YOLOv9s model to form the new C2FUIBELAN module, enhancing the model’s accuracy and training speed for small-scale object detection. Additionally, the SCDown and v10detect modules replaced the original AConv and detection head structures of the YOLOv9s model, further improving performance. The experimental results demonstrated that the YOLOv9s-Pear model achieved high detection accuracy in recognizing young red pears, while reducing computational costs and parameters. The detection accuracy, recall, mean precision, and extended mean precision were 0.971, 0.970, 0.991, and 0.848, respectively. These results confirm the efficiency of the SCDown, C2FUIBELAN, and v10detect modules in young red pear recognition tasks. The findings of this study not only provide a fast and accurate technique for recognizing young red pears but also offer a reference for detecting young fruits of other fruit trees, significantly contributing to the advancement of agricultural automation technology.

The identification and enumeration of peach seedling fruits are pivotal in the realm of precision agriculture, greatly influencing both yield estimation and agronomic practices. This study introduces an innovative, lightweight YOLOv8 model for the automatic detection and quantification of peach seedling fruits, designated as YOLO-Peach, to bolster the scientific rigor and operational efficiency of orchard management. Traditional identification methods, which are labor-intensive and error-prone, have been superseded by this advancement. A comprehensive dataset was meticulously curated, capturing the rich characteristics and diversity of peach seedling fruits through high-resolution imagery at various times and locations, followed by meticulous preprocessing to ensure data quality. The YOLOv8s model underwent a series of lightweight optimizations, including the integration of MobileNetV3 as its backbone, the p2BiFPN architecture, spatial and channel reconstruction convolution, and coordinate attention mechanism, all of which have significantly bolstered the model’s capability to detect small targets with precision. The YOLO-Peach model excels in detection accuracy, evidenced by a precision and recall of 0.979, along with an mAP50 of 0.993 and an mAP50-95 of 0.867, indicating its superior capability for peach sapling identification with efficient computational performance. The findings underscore the model’s efficacy and practicality in the context of peach seedling fruit recognition. Ablation studies have shed light on the indispensable role of each component, with MobileNetV3 streamlining the model’s complexity and computational load, while the p2BiFPN architecture, ScConv convolutions, and coordinate attention mechanism have collectively enhanced the model’s feature extraction and detection precision for minute targets. The implications of this research are profound, offering a novel approach to peach seedling fruit recognition and serving as a blueprint for the identification of young fruits in other fruit species. This work holds significant theoretical and practical value, propelling forward the broader field of agricultural automation.

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.

Prediction of spring and summer maize yield in China based on feature analysis and hybrid DHKELM algorithms