To enhance the efficiency of automatic cherry tomato harvesting in precision agriculture, an improved YOLOv8n algorithm was proposed for fast and accurate recognition in natural environments. The improvements are as follows: first, the ADown down-sampling module replaces part of the original network backbone’s standard convolution, enabling the model to capture higher-level image features for more accurate target detection, while also reducing model complexity by cutting the number of parameters. Secondly, the model’s neck adopts a Slim-Neck (GSConv+VoV-GSCSP) instead of traditional convolution with C2f. It replaces this combination with the more efficient CSConv and swaps the C2f module for VoV-GSCSP. Finally, the model also introduces the EMA attention mechanism, implemented at the P5 layer, which enhances the feature representation capability, enabling the network to extract detailed target features more accurately. This study trained the object-detection algorithm on a self-built cherry tomato dataset before and after improvement and compared it with early deep learning models and YOLO series algorithms. The experimental results show that the improved model increases accuracy by 3.18%, recall by 1.43%, the F1 score by 2.30%, mAP50 by 1.57%, and mAP50-95 by 1.37%. Additionally, the number of parameters is reduced to 2.52 M, and the model size is reduced to 5.08 MB, which outperforms other related models compared to the previous version. The experiment demonstrates the technology’s broad potential for embedded systems and mobile devices. The improved model offers efficient, accurate support for automated cherry tomato harvesting.

This study aims to improve the prediction accuracy of reference evapotranspiration under limited meteorological factors. Based on the commonly recommended PSO-ELM model for ET₀ prediction and addressing its limitations, an improved QPSO algorithm and multiple kernel functions are introduced. Additionally, a novel evapotranspiration prediction model, Kmeans-QPSO-MKELM, is proposed, incorporating K-means clustering to estimate the daily evapotranspiration in Yancheng, Jiangsu Province, China. In the input selection process, based on the variance and correlation coefficients of various meteorological factors, eight input models are proposed, attempting to incorporate the sine and cosine values of the date. The new model is then subjected to ablation and comparison experiments. Ablation experiment results show that introducing K-means clustering improves the model’s running speed, while the improved QPSO algorithm and the introduction of multiple kernel functions enhance the model’s accuracy. The improvement brought by introducing multiple kernel functions was especially significant when wind speed was included. Comparison experiment results indicate that the new model’s prediction accuracy is significantly higher than all other comparison models, especially after including date sine and cosine values in the input. The new model’s running speed is only slower than the RF model. Therefore, the Kmeans-QPSO-MKELM model, using date sine and cosine values as inputs, provides a fast and accurate new approach for predicting evapotranspiration.

Introduction: The phenotypic traits of tomato plants reflect their growth status, and investigating these characteristics can improve tomato production. Traditional deep learning models face challenges such as excessive parameters, high complexity, and susceptibility to overfitting in point cloud segmentation tasks. To address these limitations, this paper proposes a lightweight improved model based on the ResNet architecture.Methods: The proposed network optimizes the traditional residual block by integrating bottleneck modules and downsampling techniques. Additionally, by combining curvature features and geometric characteristics, we custom-designed specialized convolutional layers to enhance segmentation accuracy for tomato stem and leaf point clouds. The model further employs adaptive average pooling to improve generalization and robustness.Results: Experimental validation demonstrated that the optimized model achieved a training accuracy of 95.11%, a 3.26% improvement over the traditional ResNet18 model. Testing time was reduced to 4.02 seconds (25% faster than ResNet18’s 5.37 seconds). Phenotypic parameter extraction yielded high correlation with manual measurements, with coefficients of determination (R²) of 0.941 (plant height), 0.752 (stem diameter), 0.945 (leaf area), and 0.943 (leaf inclination angle). The root mean square errors (RMSE) were 0.506, 0.129, 0.980, and 3.619, respectively, while absolute percentage errors (APE) remained below 6% (1.965%–5.526%).Discussion: The proposed X-ResNet model exhibits superior segmentation performance, demonstrating high accuracy in phenotypic trait extraction. The strong correlations and low errors between extracted and manually measured data validate the feasibility of 3D point cloud technology for tomato phenotyping. This study provides a valuable benchmark for plant phenotyping research, with significant practical and theoretical implications.