WCAF-YOLO: A Lightweight Detection Architecture for Multi-Variety Tomatoes in Unstructured Orchards

LGD-YOLO: An Asymmetric Lightweight Network with Dynamic Feature Alignment for Greenhouse Tomato Maturity Detection

Greenhouse tomato detection faces critical challenges due to dense fruit occlusion, background interference, and the stringent computational constraints inherent to edge-deployed harvesting robots. Resolving these bottlenecks requires efficient architectures. We propose LGD-YOLO as an asymmetric lightweight network adapted from YOLOv10n specifically for edge-based tomato maturity detection. The architecture integrates a C2f-GMKSF module utilizing grouped multi-kernel convolutions to extract multi-scale textures with limited computational overhead. Precise feature alignment under occluded conditions is subsequently achieved through a Dy-HSFPN structure, accompanied by a C2f-CFCGLU module that expands the receptive field while preserving linear complexity. Furthermore, replacing the traditional detection head with a partial convolution head reduces memory access costs. A Focaler-Wise-SIoU loss function is utilized to stabilize bounding box regression against the lightweight penalty without introducing inference latency. Performance evaluations on a custom three-class dataset with a 180-image test set yield an 88.0% mAP@50. Relative to the baseline model, LGD-YOLO improves detection accuracy by 0.6 percentage points while shrinking the parameter volume by 37.6% to 1.41 M and lowering computational demand by 41.5% to 3.8 GFLOPs. Hardware deployment on an NVIDIA Jetson AGX Orin achieves a sustained processing speed of 40.6 FPS, while the weight file is 2.99 MB, supporting its feasibility for real-time agricultural robotics.

Fast-YOLO11n: A Lightweight and Efficient Apple Detection Model for Complex Orchard Environments

The proposed model provides a favorable balance between detection accuracy and computational efficiency, indicating its potential for real-time orchard perception on resource-constrained platforms.

CSAG-DETR: a lightweight detector for accurate weed detection in sugar beet fields under complex field conditions

Introduction Effective weed management is essential for reducing yield losses in sugar beet cultivation. However, existing deep learning-based detectors often experience performance degradation in complex field environments characterized by variable illumination, severe occlusion, dense vegetation, and background interference. In addition, models lacking lightweight designs generally require substantial computational resources, leading to increased inference latency and deployment costs that limit their application in real-time agricultural systems. Methods To address these challenges, we developed CSAG-DETR, a lightweight and robust weed detection framework based on RT-DETR-R18 for accurate weed detection in sugar beet fields. The proposed framework incorporates a Cross-Stage Multi-Scale Network (CSMN) to enhance hierarchical feature interaction, a Cross-Stage Local-Detail Module (CSLM) to preserve fine-grained textures and object boundaries, and Global Attention-Gated Dual-Path Upsampling and Downsampling modules (GAGDU and GAGDD) to improve the stability of cross-scale feature transformation. Furthermore, we constructed the BeetWeed dataset containing nine common weed species and applied diverse data augmentation strategies to improve model robustness under variable field conditions. Results Experimental results showed that CSAG-DETR achieved an mAP@0.5 of 98.9%, an mAP@0.5:0.95 of 78.6%, and an inference speed of 167.3 FPS on the BeetWeed dataset, outperforming 13 mainstream object detection models in terms of overall detection performance and computational efficiency. Generalization experiments conducted on the public CottonWeedDet12 dataset further demonstrated the strong cross-dataset generalization capability and competitive overall performance of the proposed model. Discussion These results indicate that CSAG-DETR effectively balances detection accuracy, inference efficiency, and robustness in complex agricultural environments. The proposed framework therefore provides a practical solution for real-time weed detection and may support the deployment of intelligent weed management systems in sugar beet production.

MFA-YOLO: a lightweight weed detection model for complex farmland environments

Lightweight pear detection in unstructured orchards via selective information propagation

Accurate pear detection in unstructured orchards is important for robotic harvesting and orchard perception. However, pear detection poses compound challenges that differ from those in more chromatically distinctive fruits: mature pears share yellow-green hues with surrounding foliage, their near-spherical geometry offers limited contour priors, and they typically grow in tight spur clusters where mutual boundary occlusion occurs even without branch interference. Under these coupled degradations, lightweight detectors tend to lose accuracy and become difficult to deploy on embedded agricultural platforms. To address this issue, we propose a lightweight pear detection framework guided by the principle of selective information propagation—the idea that, under tight computational budgets, how information is routed at each stage matters more than overall network capacity. The framework instantiates this principle along four stages of the detection pipeline through dedicated modules for efficient global–local context modeling, input-adaptive feature transformation, detail-preserving multiscale fusion, and an adaptive IoU loss tailored for small and occluded fruits. On the self-built Orchard Pear dataset, the proposed method achieves 95.2% mAP@50 and 54.6% mAP@50:95 with only 2.56 M parameters and 5.60 GFLOPs. Consistent improvements are also observed on the public Minne Apple and Mango datasets. Deployment experiments on embedded platforms further show that the proposed method supports real-time inference for agricultural robotic applications. These results suggest that selective feature representation, fusion, and optimization benefit lightweight fruit detection in complex orchard scenes.

A Unified Detection Network with Multi-Scale Attention Fusion for Sugarcane Leaf Disease Identification

Sugarcane yield is reduced by leaf diseases such as mosaic, red rot, rust, and yellow leaf, whose overlapping symptoms make early field diagnosis difficult. This paper presents UniDetNet-MAF, a lightweight unified detection framework that simultaneously localizes and classifies sugarcane leaf diseases. Three components drive its performance: MAF-Conv (a multi-dimensional attention convolution acting jointly on input-channel, kernel-spatial, kernel-count, and output-channel axes), Hierarchical Fusion Block (HFB) and MSAF-Net (a HFB coupled with a multi-scale attention fusion network providing bidirectional feature aggregation), and SAC-Loss (a shape-aware constrained bounding-box regression loss that improves localisation of irregular lesions). Experiments on the publicly available Sugarcane Leaf Disease Dataset, consisting of 2,521 images across five classes, namely Healthy, Mosaic, Red Rot, Rust, and Yellow Leaf, show that UniDetNet-MAF achieves 94.23% accuracy, 95.12% precision, 93.87% recall, 94.51% F1-score, and 93.68% Matthews Correlation Coefficient (MCC), along with mAP@0.5 = 0.937 and 112 FPS on a single GPU, an improvement of roughly 11% over the LeafNet baseline across all classification metrics. The framework is therefore suitable for timely, field-scale sugarcane disease diagnosis.