Strawberry (Fragaria × ananassa), a nutrient-dense fruit with significant economic value in commercial cultivation, faces critical detection challenges in automated harvesting due to complex growth conditions such as foliage occlusion and variable illumination. To address these limitations, this study proposes Ripe-Detection, a novel lightweight object detection framework integrating three key innovations: a PEDblock detection head architecture with depth-adaptive feature learning capability, an ADown downsampling method for enhanced detail perception with reduced computational overhead, and BiFPN-based hierarchical feature fusion with learnable weighting mechanisms. Developed using a purpose-built dataset of 1021 annotated strawberry images (Fragaria × ananassa ‘Red Face’ and ‘Sachinoka’ varieties) from Changchun Xiaohongmao Plantation and augmented through targeted strategies to enhance model robustness, the framework demonstrates superior performance over existing lightweight detectors, achieving mAP50 improvements of 13.0%, 9.2%, and 3.9% against YOLOv7-tiny, YOLOv10n, and YOLOv11n, respectively. Remarkably, the architecture attains 96.4% mAP50 with only 1.3M parameters (57% reduction from baseline) and 4.4 GFLOPs (46% lower computation), simultaneously enhancing accuracy while significantly reducing resource requirements, thereby providing a robust technical foundation for automated ripeness assessment and precision harvesting in agricultural robotics.

With the development of in-orbit processing technology of remote sensing satellites, the intelligent processing units carried on the satellites realize the real-time acquisition and intelligent processing of remote sensing images, thus satisfying users’ individual needs and enhancing the response speed of tasks. However, a large amount of observation data cannot be transmitted back to the ground in time due to the limitation of the transmission capability of remote sensing satellites to the ground and the visible time window between satellites and ground stations in the process of satellite–terrestrial transmission. To solve this problem, a community division-based content distribution (CDCD) strategy is proposed. First, the time slot model is designed to capture the time-varying topological information of the satellite network. Also, a content naming method that fits the characteristics of remote sensing data is presented. Then, a caching scheme based on community division is proposed by analyzing the regional characteristics of user requests. Taking advantage of the community structure characteristics of satellite networks, a novel cache node selection algorithm is designed to meet the user’s demand for fast access to target files. Meanwhile, a cached content prioritization model is constructed to further optimize the utilization of caching resources. Finally, the community structure-based routing (CSR) algorithm is proposed to effectively reduce the redundant transmission during content access through the mutual collaboration of intracommunity and intercommunity routing schemes. Simulation experiments show that the CDCD strategy effectively exploits the limited caching resources in the satellite network compared with other strategies, which promotes the stable and efficient distribution of remote sensing data.

The germination potential of corn seeds, a key index for assessing their quality and directly associated with the ultimate corn yield, is currently defined in a way that cannot effectively portray the seed germination rate, and the prevalent measurement methods are traditional, consuming substantial process resources. To tackle these issues, this paper employs a public corn seed germination dataset, adds noise to it to simulate real - world production conditions, and ultimately acquires a dataset comprising 8148 images. It then proposes an enhanced YOLOv8 target detection model, EBS - YOLOv8, for detecting corn seed germination. Specifically, the ECA lightweight attention mechanism is introduced to decrease small - target feature loss, assist in accurate target recognition, and remove redundant features; simultaneously, the P2BiFPN multiscale feature fusion technique is utilized to boost the detection ability for small targets; furthermore, the ScConv convolution is adopted to enhance the feature - extraction capacity and improve detection accuracy. Combined with the improved model, this paper also proposed a mathematical modeling algorithmnew method for measuring seed germination potential and observing seed germination rate. The results indicate that the proposed model attains a mean average precision at 50% Intersection over Union (mAP50) value of 98.9%, a mean average precision in the range of 50% - 95% Intersection over Union (mAP50 - 95) value of 95.8%, an accuracy of 96.7%, and a recall of 96.3%. In comparison with the original model, the mAP50 has increased by 0.9% and the mAP50 - 95 value has witnessed a 3.7% increment. The experiments have demonstrated that the research method for germination potential put forward in this paper can effectively depict the rate variation of seeds during the germination process, thus offering a novel perspective for future research on seed germination potential.