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 Traditional identification methods, which are labor-intensive and error-prone, have been superseded by this advancement.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.

The accurate identification of tomato maturity and picking positions is essential for efficient picking. Current deep-learning models face challenges such as large parameter sizes, single-task limitations, and insufficient precision. This study proposes MTS-YOLO, a lightweight and efficient model for detecting tomato fruit bunch maturity and stem picking positions. We reconstruct the YOLOv8 neck network and propose the high- and low-level interactive screening path aggregation network (HLIS-PAN), which achieves excellent multi-scale feature extraction through the alternating screening and fusion of high- and low-level information while reducing the number of parameters. Furthermore, We utilize DySample for efficient upsampling, bypassing complex kernel computations with point sampling. Moreover, context anchor attention (CAA) is introduced to enhance the model’s ability to recognize elongated targets such as tomato fruit bunches and stems. Experimental results indicate that MTS-YOLO achieves an F1-score of 88.7% and an mAP@0.5 of 92.0%. Compared to mainstream models, MTS-YOLO not only enhances accuracy but also optimizes the model size, effectively reducing computational costs and inference time. The model precisely identifies the foreground targets that need to be harvested while ignoring background objects, contributing to improved picking efficiency. This study provides a lightweight and efficient technical solution for intelligent agricultural picking.

Addressing the current reliance on manual sorting and grading of spray rose cut flowers, this paper proposed an improved YOLOv5s model for intelligent recognition and grading detection of rose color series and flowering index of spray rose cut flowers. By incorporating small-scale anchor boxes and small object feature output, the model enhanced the annotation accuracy and the detection precision for occluded rose flowers. Additionally, a convolutional block attention module attention mechanism was integrated into the original network structure to improve the model’s feature extraction capability. The WIoU loss function was employed in place of the original CIoU loss function to increase the precision of the model’s post-detection processing. Test results indicated that for two types of spray rose cut flowers, Orange Bubbles and Yellow Bubbles, the improved YOLOv5s model achieved an accuracy and recall improvement of 10.2% and 20.0%, respectively. For randomly collected images of spray rose bouquets, the model maintained a detection accuracy of 95% at a confidence threshold of 0.8.