Automated ripeness detection in large-scale strawberry cultivation is often challenged by complex backgrounds, significant target scale variation, and small object size. To address these problems, an efficient strawberry ripeness detection model, YOLOv11-HRS, is proposed. This model incorporates a hybrid channel–space attention mechanism to enhance its attention to key features and to reduce interference from complex backgrounds. Furthermore, the RepNCSPELAN4_L module is devised to enhance multi-scale target representation through contextual feature aggregation. Simultaneously, a 160 × 160 small-target detection head is embedded in the feature pyramid to enhance the detection capability of small targets. It replaces the original SPPF module with the higher-performance SPPELAN module to further enhance detection accuracy. Experimental results on the self-constructed strawberry dataset SRD show that YOLOv11-HRS improves mAP@0.5 and mAP@0.5:0.95 by 3.4% and 6.3%, respectively, reduces the number of parameters by 19%, and maintains a stable inference speed compared to the baseline YOLOv11 model. This study presents an efficient and practical solution for strawberry ripeness detection in natural environments. It also provides essential technical support for advancing intelligent management in large-scale strawberry cultivation.

Drought stress has serious effects on the growth and yield of wheat in both productivity and quality and is an abiotic factor. Traditional methods of crop drought stress monitoring have some deficits. This work has been conducted in order to enhance these conventional methods by proposing a new deep learning approach. This paper has presented a deep learning-based model customized for monitoring drought stress in winter wheat during the critical growth stages. Drought-afflicted winter wheat images were captured at three crucial phases: rising–jointing, heading–flowering, and flowering–maturity. These images are correlated against soil moisture data to construct a comprehensive dataset. DenseNet121 was chosen as the network model since it extracts features from images relating to phenotypes. Several factors, like training methods, learning rate adjustment, and addition of the attention mechanism, are optimized in eight sets of experiments. This provided the final DenseNet-121 model with an average recognition accuracy of 94.67% on the test set, which means that monitoring drought stress during wheat growth’s key periods is feasible and effective.

Drought stress severely affects the normal growth and development of wheat, leading to reduced yields and quality. To address the lag and limitations of traditional drought monitoring methods, this paper proposes a drought stress monitoring model for winter wheat during critical growth stages based on deep learning. By acquiring drought stress images of winter wheat during three critical growth stages: rise-jointing, heading-flowering and flowering-maturity, a dataset correlating these images with soil moisture monitoring data was established. The DenseNet121 network model was selected as the base network to extract phenotypic features of winter wheat under drought stress. Variables such as the model's training approach, changes in learning rate, and whether an attention mechanism was included, were used to conduct eight sets of model training and optimization strategy experiments. A drought stress monitoring model for winter wheat during critical growth stages based on an improved DenseNet-121 was constructed. The results showed that the average recognition accuracy of the eighth group in the test set reached 94.67%, indicating good application prospects in the recognition of drought stress levels during critical growth stages of wheat.

Object detection in drone-captured images must handle large-scale variations, cluttered backgrounds, and limited onboard computing power. To address these issues, this paper presents a lightweight detector named MSG-YOLO. The improvements lie in three aspects: feature extraction, multi-scale perception, and detection head design. We introduce an Attention Refinement Bottleneck (ARB) that combines depthwise separable convolutions with learnable channel scaling factors to achieve effective feature recalibration at very low parameter cost. We design an Omni-Receptive Field Module (ORFM) that employs multi-branch dilated convolutions to capture multi-scale context and incorporates second-order channel attention for better feature discrimination. A P2 detection head (160 × 160) is further added to the feature pyramid to enhance small-object response. Experiments show that ARB and ORFM work synergistically: each alone brings limited gain, but together they improve mAP@0.5 by 0.4 percentage points while reducing parameters by 0.13 M. On VisDrone2019, MSG-YOLO achieves 36.7% mAP@0.5, outperforming the baseline YOLOv11n by 4.3 points while using only 2.69M parameters. On the HazyDet benchmark, the method outperforms the baseline by 3.1 points, confirming robustness under low visibility. Ablation studies and visual analyses further reveal how each component contributes.