As one of the world’s economically valuable fruit crops, citrus has its quality and productivity closely tied to the degree of fruit ripeness. However, accurately and efficiently detecting citrus ripeness in complex orchard environments for selective robotic harvesting remains a challenge. To address this, we constructed a citrus ripeness detection dataset under complex orchard conditions, proposed a lightweight algorithm based on visual saliency priors and the RT-DETR model, and named it LightSal-RTDETR. To reduce computational overhead, we designed the E-CSPPC module, which efficiently combines cross-stage partial networks with gated and partial convolutions, combined with cascaded group attention (CGA) and inverted residual mobile block (iRMB), which minimizes model complexity and computational demand and simultaneously strengthens the model’s capacity for feature representation. Additionally, the Inner-SIoU loss function was employed for bounding box regression, while a weight initialization method based on visual saliency maps was proposed. Experiments on our dataset show that LightSal-RTDETR achieves a mAP@50 of 81%, improving by 1.9% over the original model while reducing parameters by 28.1% and computational cost by 26.5%. Therefore, LightSal-RTDETR effectively solves the citrus ripeness detection problem in orchard scenes with high complexity, offering an efficient solution for smart agriculture applications.

Mandarin fruit detection provides crucial technical support for yield prediction and the precise identification and harvesting of mandarin fruits. However, challenges such as occlusion from leaves or branches, the presence of small or partially visible fruits, and limitations in model efficiency pose significant obstacles in a complex orchard environment. To tackle these issues, we propose ELD-YOLO, a lightweight detection framework designed to enhance edge detail preservation and improve the detection of small and occluded fruits. Our method incorporates edge-aware processing to strengthen feature representation, introduces a streamlined detection head that balances accuracy with computational cost, and employs an adaptive upsampling strategy to minimize information loss during feature scaling. Experiments on a mandarin fruit dataset show that ELD-YOLO achieves a precision of 89.7%, a recall of 83.7%, an mAP@50 of 92.1%, and an mAP@50:95 of 68.6% while reducing the parameter count by 15.4% compared with the baseline. These results demonstrate that ELD-YOLO provides an effective and efficient solution for fruit detection in complex orchard scenarios.

In the modern field of biological pest control, especially in the realm of insect population monitoring, deep learning methods have made further advancements. However, due to the small size and elusive nature of insects, visual detection is often impractical. In this context, the recognition of insect sound features becomes crucial. In our study, we introduce a classification module called the “dual-frequency and spectral fusion module (DFSM)”, which enhances the performance of transfer learning models in audio classification tasks. Our approach combines the efficiency of EfficientNet with the hierarchical design of the Dual Towers, drawing inspiration from the way the insect neural system processes sound signals. This enables our model to effectively capture spectral features in insect sounds and form multiscale perceptions through inter-tower skip connections. Through detailed qualitative and quantitative evaluations, as well as comparisons with leading traditional insect sound recognition methods, we demonstrate the advantages of our approach in the field of insect sound classification. Our method achieves an accuracy of 80.26% on InsectSet32, surpassing existing state-of-the-art models by 3 percentage points. Additionally, we conducted generalization experiments using three classic audio datasets. The results indicate that DFSM exhibits strong robustness and wide applicability, with minimal performance variations even when handling different input features.