Accurate detection of muskmelon fruit ripeness is crucial to ensure fruit quality, optimize picking time, and enhance economic benefits. This study proposes an improved lightweight YOLO-RFEW model based on YOLOv8n, aiming to address the challenges of low efficiency in muskmelon fruit ripeness detection and the complexity of deploying a target detection model to a muskmelon picking robot. Firstly, the RFAConv replaces the Conv in the backbone part of YOLOv8n, allowing the network to focus more on regions with significant contributions in feature extraction. Secondly, the feature extraction and fusion capability are enhanced by improving the C2f module into a C2f-FE module based on FasterNet and an Efficient Multi-Scale attention (EMA) mechanism within the lightweight model. Finally, Weighted Intersection over Union (WIoU) is optimized as the loss function to improve target frame prediction capability and enhance target detection accuracy. The experimental results demonstrate that the YOLO-RFEW model achieves high accuracy, with precision, recall, F1 score, and mean Average Precision (mAP) values of 93.16%, 83.22%, 87.91%, and 90.82%, respectively. Moreover, it maintains a lightweight design and high efficiency with a model size of 4.75 MB and an inference time of 1.5 ms. Additionally, in the two types of maturity tests (M-u and M-r), APs of 87.70% and 93.94% are obtained, respectively, by the YOLO-RFEW model. Compared to YOLOv8n, significant improvements in detection accuracy have been achieved while reducing both model size and computational complexity using the proposed approach for muskmelon picking robots’ real-time detection requirements. Furthermore, when compared to lightweight models such as YOLOv3-Tiny, YOLOv4-Tiny, YOLOv5s, YOLOv7-Tiny, YOLOv8s, and YOLOv8n, the YOLO-RFEW model demonstrates superior performance with only 28.55%, 22.42%, 24.50%, 40.56%, 22.12%, and 79.83% of their respective model sizes, respectively, while achieving the highest F1 score and mAP values among these seven models. The feasibility and effectiveness of our improved scheme are verified through comparisons between thermograms generated by YOLOv8n and YOLO-RFEW as well as detection images. In summary, the YOLO-RFEW model not only improves the accuracy rate of muskmelon ripeness detection but also successfully realizes the lightweight and efficient performance, which has important theoretical support and application value in the field of muskmelon picking robot development.

To detect quickly and accurately “Yuluxiang” pear fruits in non-structural environments, a lightweight YOLO-GEW detection model is proposed to address issues such as similar fruit color to leaves, fruit bagging, and complex environments. This model improves upon YOLOv8s by using GhostNet as its backbone for extracting features of the “Yuluxiang” pears. Additionally, an EMA attention mechanism was added before fusing each feature in the neck section to make the model focus more on the target information of “Yuluxiang” pear fruits, thereby improving target recognition ability and localization accuracy. Furthermore, the CIoU Loss was replaced with the WIoUv3 Loss as the loss function, which enhances the capability of bounding box fitting and improves model performance without increasing its size. Experimental results demonstrated that the enhanced YOLO-GEW achieves an F1 score of 84.47% and an AP of 88.83%, while only occupying 65.50% of the size of YOLOv8s. Compared to lightweight algorithms such as YOLOv8s, YOLOv7-Tiny, YOLOv6s, YOLOv5s, YOLOv4-Tiny, and YOLOv3-Tiny; there are improvements in AP by 2.32%, 1.51%, 2.95%, 2.06%, 2.92%, and 5.38% respectively. This improved model can efficiently detect “Yuluxiang” pears in non-structural environments in real-time and provides a theoretical basis for recognition systems used by picking robots.

The intelligent detection of young peaches is the main technology of fruit-thinning robots, which is crucial for enhancing peach fruit quality and reducing labor costs. This study presents the lightweight YOLO-PEM model based on YOLOv8s to achieve high-precision and automatic detection of young “Okubo” peaches. Firstly, the C2f_P module was devised by partial convolution (PConv), replacing all C2f modules in YOLOv8s to achieve the model’s lightweight. Secondly, embedding the efficient multi-scale attention (EMA) module in the lightweight C2f_P_1 module of the backbone network enhanced the feature extraction capability and accuracy for young peaches. Finally, the MPDIoU loss function was utilized to replace the original CIoU loss function, which improved the detection accuracy of the bounding box while speeding up the convergence of the model. The experimental results demonstrate that the YOLO-PEM model achieved an average precision (AP) of 90.86%, F1 score of 86.70%, and model size of 16.1 MB, which was a 1.85% improvement in the AP, 0.85% improvement in the F1 score, and 5.3 MB reduction in the model size compared with YOLOv8s. The AP was 6.26%, 6.01%, 2.05%, 2.12%, and 1.87% higher compared with the other lightweight detection models YOLOv3-tiny, YOLOv4-tiny, YOLOv5s, YOLOv6s, and YOLOv7-tiny, respectively. Furthermore, the FPS of YOLO-PEM was 196.2 f·s^-1, which can fulfill the demand for the real-time detection of young peaches. YOLO-PEM effectively detects young peaches in complex orchard environments and can offer a basis for the theoretical design of the vision system of the “Okubo” peach fruit-thinning robot and scientific management of orchards.

With the expansion of muskmelon cultivation, manual pollination is increasingly inadequate for sustaining industry development. Therefore, the development of automatic pollination robots holds significant importance in improving pollination efficiency and reducing labor dependency. Accurate flower detection and localization is a key technology for enabling automated pollination robots. In this study, the YOLO-MDL model was developed as an enhancement of YOLOv7 to achieve real-time detection and localization of muskmelon flowers. This approach adds a Coordinate Attention (CA) module to focus on relevant channel information and a Contextual Transformer (CoT) module to leverage contextual relationships among input tokens, enhancing the model’s visual representation. The pollination robot converts the 2D coordinates into 3D coordinates using a depth camera and conducts experiments on real-time detection and localization of muskmelon flowers in a greenhouse. The YOLO-MDL model was deployed in ROS to control a robotic arm for automatic pollination, verifying the accuracy of flower detection and measurement localization errors. The results indicate that the YOLO-MDL model enhances AP and F1 scores by 3.3% and 1.8%, respectively, compared to the original model. It achieves AP and F1 scores of 91.2% and 85.1%, demonstrating a clear advantage in accuracy over other models. In the localization experiments, smaller errors were revealed in all three directions. The RMSE values were 0.36 mm for the X-axis, 1.26 mm for the Y-axis, and 3.87 mm for the Z-axis. The YOLO-MDL model proposed in this study demonstrates strong performance in detecting and localizing muskmelon flowers. Based on this model, the robot can execute more precise automatic pollination and provide technical support for the future deployment of automatic pollination robots in muskmelon cultivation.

The rapid expansion of the goose farming industry creates a growing need for real-time flock counting and individual-level behavior monitoring. To meet this challenge, this study proposes an improved YOLOv8-based model, termed DAEF-YOLO (DualConv-augmented C2f, ADown down-sampling, Efficient Channel Attention integrated into SPPF, and FocalerIoU regression loss), designed for simultaneous recognition of Sanhua goose individuals and their diverse behaviors. The model incorporates three targeted architectural improvements: (1) a C2f-Dual module that enhances multi-scale feature extraction and fusion, (2) ECA embedded in the SPPF module to refine channel interaction with minimal parameter cost and (3) an ADown down-sampling module that preserves cross-channel information continuity while reducing information loss. Additionally, the adoption of the FocalerIoU loss function enhances bounding-box regression accuracy in complex detection scenarios. Experimental results demonstrate that DAEF-YOLO surpasses YOLOv5s, YOLOv7-Tiny, YOLOv7, YOLOv9s, and YOLOv10s in both accuracy and computational efficiency. Compared with YOLOv8s, DAEF-YOLO achieved a 4.56% increase in precision, 6.37% in recall, 5.50% in F1-score, and 4.59% in mAP@0.5, reaching 94.65%, 92.17%, 93.39%, and 96.10%, respectively. A generalizable classification strategy is further introduced by adding a complementary “Other” category to include behaviors beyond predefined classes. This approach ensures complete recognition coverage and demonstrates strong transferability for multi-task detection across species and environments. Ablation studies indicated that mAP@0.5 remained consistent (~96.1%), while mAP@0.5:0.95 improved in the absence of the “Other” class (75.68% vs. 69.82%). Despite this trade-off, incorporating the “Other” category ensures annotation completeness and more robust multi-task behavior recognition under real-world variability.