The challenge of efficiently detecting ripe and unripe strawberries in complex environments like greenhouses, marked by dense clusters of strawberries, frequent occlusions, overlaps, and fluctuating lighting conditions, presents significant hurdles for existing detection methodologies. These methods often suffer from low efficiency, high computational expenses, and subpar accuracy in scenarios involving small and densely packed targets. To overcome these limitations, this paper introduces YOLOv11-GSF, a real-time strawberry ripeness detection algorithm based on YOLOv11, which incorporates several innovative features: a Ghost Convolution (GhostConv) convolution method for generating rich feature maps through lightweight linear transformations, thereby reducing computational overhead and enhancing resource utilization; a C3K2-SG module that combines self-moving point convolution (SMPConv) and convolutional gated linear units (CGLU) to better capture the local features of strawberry ripeness; and a F-PIoUv2 loss function inspired by Focaler IoU and PIoUv2, utilizing adaptive penalty factors and interval mapping to expedite model convergence and optimize ripeness classification. Experimental results demonstrate the superior performance of YOLOv11-GSF, achieving an average precision of 97.8%, an accuracy of 95.99%, and a recall rate of 93.62%, representing improvements of 1.8%, 1.3 percentage points, and 2.1% over the original YOLOv11, respectively. Furthermore, it exhibits higher recognition accuracy and robustness compared to alternative algorithms, thus offering a practical and efficient solution for deploying strawberry ripeness detection systems.

In the era of smart agriculture, intelligent fruit maturity detection has become a critical task. However, in complex orchard environments, factors such as occlusion by branches and leaves and interference from bagging materials pose significant challenges to detection accuracy. To address this issue, this study focuses on maturity detection of ‘Jiang’ pomegranates and proposes an improved YOLO-AMAS algorithm. The method integrates an Adaptive Feature Enhancement (AFE) module, a Multi-Scale Convolutional Attention Module (MSCAM), and an Adaptive Spatial Feature Fusion (ASFF) module. The AFE module effectively suppresses complex backgrounds through dual-channel spatial attention mechanisms; the MSCAM enhances multi-scale feature extraction capability using a pyramidal spatial convolution structure; and the ASFF optimizes the representation of both shallow details and deep semantic information via adaptive weighted fusion. A SlideLoss function based on Intersection over Union is introduced to alleviate class imbalance. Experimental validation conducted on a dataset comprising 6564 images from multiple scenarios demonstrates that the YOLO-AMAS model achieves a precision of 90.9%, recall of 86.0%, mAP@50 of 94.1% and mAP@50:95 of 67.6%. The model significantly outperforms mainstream detection models including RT-DETR-1, YOLOv3 to v6, v8, and 11 under multi-object, single-object, and occluded scenarios, with a mAP50 of 96.4% for bagged mature fruits. Through five-fold cross-validation, the model’s strong generalization capability and stability were demonstrated. Compared to YOLOv8, YOLO-AMAS reduces the false detection rate by 30.3%. This study provides a reliable and efficient solution for intelligent maturity detection of ‘Jiang’ pomegranates in complex orchard environments.

Dwarf apple is one of the most important forms of garden economy, which has become a new engine for rural revitalization. The effective detection of coloring and ripening apples in complex environments is important for the sustainable development of smart agricultural operations. Addressing the issues of low detection efficiency in the greenhouse and the challenges associated with deploying complex target detection algorithms on low-cost equipment, we propose an enhanced lightweight model rooted in YOLOv7. Firstly, we enhance the model training performance by incorporating the Squeeze-and-Excite attention mechanism, which can enhance feature extraction capability. Then, an SCYLLA-IoU (SIoU) loss function is introduced to improve the ability of extracting occluded objects in complex environments. Finally, the model was simplified by introducing depthwise separable convolution and adding a ghost module after up-sampling layers. The improved YOLOv7 model has the highest AP value, which is 10.00%, 5.61%, and 6.00% higher compared to YOLOv5, YOLOv7, and YOLOX, respectively. The improved YOLOv7 model has an MAP value of 95.65%, which provides higher apple detection accuracy compared to other detection models and is suitable for potted dwarf anvil apple identification and detection.

Real-time detection has become an essential component in intelligent agriculture and industry. In this paper, a real-time wheat spike detection method based on the lightweight deep learning network RepYOLO is proposed. Addressing the small and densely packed phenotype characteristics of wheat spikes, the channel attention mechanism module CBAM from the traditional YOLOv4 algorithm is introduced and multiple convolutional kernels are merged using a structural reparameterization method. Additionally, the ATSS algorithm is incorporated to enhance the accuracy of object detection. These approaches significantly reduce the model size, improve the inference speed, and lower the memory access cost. To validate the effectiveness of the model, it is trained and tested on a large dataset of diverse wheat spike images representing various phenotypes. The experimental results demonstrate that the RepYOLO algorithm achieves an average accuracy of 98.42% with a detection speed of 8.2 FPS. On the Jetson Nano platform, the inference speed reaches 34.20 ms. Consequently, the proposed model effectively reduces the memory access cost of deep learning networks without compromising accuracy and successfully improves the utilization of CPU/MCU limited performance.

Computer vision and deep learning are one of the main technologies for weed intelligent recognition in farmland. However, when using the deep learning technology to identify weeds in the field broomcorn millet growing at seedling stage, the weeds grow densely, which not only leads to the imbalance of positive and negative sample data, but also causes the problem of small area segmentation. In this paper, the combination of dice and focal loss was applied to Segformer semantic segmentation network with MiT-B3 as encoder block to identify intensive weeds in seedling broomcorn millet fields. The accuracy scores on the testing set were 95.23 %. The results show that the method proposed in this paper can effectively identify the seedling intensive weeds.

Introduction: Accurate detection of Taiqiu sweet persimmon in orchards is essential for estimating yield, planning harvest operations, and supporting intelligent management in precision agriculture. However, current fruit-detection approaches for this cultivar, especially during the color-transition period, suffer from highly subjective and inefficient manual inspection and from poor adaptability of existing deep-learning models to complex field scenes. Methods: In this study, we propose an improved YOLO11-based detector, YOLO11-FC2T, for robust detection under conditions with strong color–background coupling, small or adherent fruits, and uneven illumination. YOLO11-FC2T introduces four key architectural modifications: (1) a C3k2_FasterBlock to improve gradient-efficient feature learning; (2) a C2PSA_CGA module to enhance channel–spatial focus via coordinate-guided aggregation; (3) a three-layer Dysample-T structure to strengthen multi-scale representation; and (4) a cross-scale attention fusion module, CAFMAttention, to better decouple fruits from cluttered backgrounds. To further enhance generalization in complex orchard scenes without additional labeling cost, we introduced the DiffuseMix data-augmentation method and apply it to color-transition images. Results: Experiments show that YOLO11-FC2T clearly outperforms the YOLO11 baseline. The model achieves a precision of 91.7% (+1.0%), recall of 86.7% (+2.8%), mAP@0.5 of 94.8% (+1.6%), and mAP@0.5-0.95 of 81.2% (+4.0%), where mAP@0.5 uses an IoU threshold of 0.50. On a challenging tail-case set of 537 images, the false detection rate is 1.30%, with a 45.2% reduction in errors relative to YOLO11. In the performance evaluation stage, we first perform causal-effect analysis based on the Average Treatment Effect (ATE) to quantify the independent and joint contributions of each architectural component and of DiffuseMix; at the same time, the efficiency of the model is analyzed by the number of parameters (Params, M) and per-image inference latency (ms). in addition, we construct and use a dedicated tail-case dataset as a supplementary experiment to further verify the robustness and effectiveness of these improvements in the most difficult scenes. Finally, we introduced cross-condition test set to further validate the generalization capability of YOLO11-FC2T. The above results indicate that YOLO11-FC2T not only improves the indicators, but also possesses reliable generalization ability and stability. Discussion: Overall, YOLO11-FC2T addresses key detection challenges during the color-transition period and provides a practical, portable solution for automated fruit identification and counting in precision agriculture. The above results indicate that YOLO11-FC2T not only improves the indicators, but also possesses reliable generalization ability and stability.