To solve the failure problem of the visible/near infrared (VIS/NIR) spectroscopy model, soluble solids content (SSC) detection for fresh jujubes cultivated in different modes was carried out based on the method of variable optimization and model update. Iteratively retained informative variables (IRIV) and successive projections algorithm (SPA) algorithms were used to extract characteristic wavelengths, and least square support vector machine (LS-SVM) was used to establish detection models. Compared with IRIV, IRIV-SPA achieved better performance. Combined with the offset properties of the wavelength, repeated wavelengths were removed, and wavelength recombination was carried out to create a new combination of variables. Using these fused wavelengths, the model was recalibrated based on the Euclidean distance between samples. The LS-SVM detection model of SSC was established using the update method of wavelength fusion-Euclidean distance. Good prediction results were achieved using the proposed model. The determination coefficient (R²), root mean square error (RMSE), and residual predictive deviation (RPD) of the test set on SSC of fresh jujubes cultivated in the open field were 0.82, 1.49%, and 2.18, respectively. The R², RMSE, and RPD of the test set on SSC of fresh jujubes cultivated in the rain shelter were 0.81, 1.44%, and 2.17, respectively. This study realized the SSC detection of fresh jujubes with different cultivation and provided a method for the establishment of a robust VIS/NIR detection model for fruit quality, effectively addressing the industry need for identifying jujubes grown in the open field.

Accurate detection of fresh jujube fruits plays a vital role in precision agriculture, enabling reliable yield estimation and supporting automation tasks such as robotic harvesting. To address the challenges of detecting such small targets (≤32 × 32 pixels) in complex orchard environments, this study proposes JFST-DETR, an efficient and robust detection model based on the Real-Time DEtection TRansformer (RT-DETR). First, to address the insufficient feature representation for small jujube fruit targets, a novel module called the Global Awareness Adaptive Module (GAAM) is designed. Building on GAAM and the innovative Spatial Coding Module (SCM), a new Spatial Enhancement Pyramid Network (SEPN) is proposed. Through the spatial-depth transformation domain and global awareness adaptive processing units, SEPN captures fine-grained features of small targets, enhancing the detection accuracy for small objects. Second, a Dynamic Sampling (DySample) operator is adopted, which optimizes feature space details via dynamic offset calculation and lightweight design, improving detection accuracy while reducing computational costs. Finally, to solve the problem of complex background interference caused by foliage occlusion and illumination variations, Pinwheel-Shaped Convolution (PSConv) is introduced. By using asymmetric padding and multi-directional convolution, PSConv enhances the robustness of feature extraction, ensuring reliable recognition in complex agricultural environments. Experimental results show that JFST-DETR achieves precision, recall, F1, mAP@50, and mAP@50:95 of 93%, 86.8%, 89.8%, 94.3%, and 75.2%. Compared to the baseline model, these metrics improve by 0.8%, 3.7%, 2.4%, 2.6%, and 3.1%, respectively. Cross-dataset evaluations further confirm its strong generalizability, demonstrating potential as a practical solution for small-target detection in intelligent horticulture.

Detecting ripe Prunus humilis Bunge (P. humilis) in natural orchards is challenging because targets are small, densely distributed, occluded, and embedded in reflective or texture-rich backgrounds. We introduce RePRF-Net, a lightweight RGB-D detection framework built around reverse-enhanced progressive residual fusion. The framework takes paired RGB images and depth maps generated offline from the corresponding RGB images by Depth Anything V2. The model uses a dual-path RGB-D backbone to encode appearance and generated depth cues, a P2-P5 progressive residual path to preserve fine boundaries, a reverse residual reinjection branch to return calibrated high-level fusion cues to high-resolution detection layers, and a multi-order gated context aggregation (MGCA) module to suppress background-induced responses. A progressive stability and stride enhancement (PSSE) auxiliary loss regularizes training, and the final deployable model is obtained through structured channel pruning followed by feature and output distillation. On the held-out test set, the final Pruned RePRF-Net achieves an mAP@0.5 of 0.9162 and an mAP@0.5:0.95 of 0.6158. Compared with the lightweight YOLO11n baseline, the final model improves mAP@0.5 by 0.0416. With 2.77 M parameters and 6.88 GFLOPs, Pruned RePRF-Net runs at approximately 32 FPS on an NVIDIA Jetson Orin Nano using an FP16 TensorRT engine. Mechanism-level evaluations further show that the fusion path improves size-stratified and dense/occluded subset performance, whereas MGCA reduces pure-background false positives under complex background conditions. These results indicate that reliable RGB-D fusion and structured compression can support deployable fruit detection in complex orchard scenes.