@article{Wang2026, 
author = {Xingxing Wang and Benxue Ma and Ying Xu and Yongchuang Xiong and Fujia Dong and Jincheng Chen and Ziming Zhao},
title = {Detecting flat peach growth period in a natural orchard environment using improved YOLOv11n},
year = {2026},
journal = {Transactions of the Chinese Society of Agricultural Engineering},
volume = {42},
number = {15},
pages = {273-284},
keywords = {flat peach, growth period detection, YOLOv11, natural orchard environment},
url = {https://www.sciopen.com/article/10.11975/j.issn.1002-6819.202510163},
doi = {10.11975/j.issn.1002-6819.202510163},
abstract = {Real-time monitoring of flat peaches is often needed during different growth stages under natural orchard environments, particularly for yield estimation, precise fertilization, and mechanical harvesting. However, conventional approaches can only detect peaches at a single specific stage, showing low adaptability during different growth stages of the same fruit. Accurate identification is also limited to the severe occlusion from peach branches and foliage, with dense overlapping clusters of young fruit and significant variations in size, color, fuzziness, and texture during growth, particularly during the color change and enlargement stages. In this study, an Orchard Growth Detection Network (OGD-Net) was proposed using the lightweight YOLOv11n model. Sufficient accuracy was also obtained to identify flat peach growth stages within natural orchard environments. First, an improved adaptive orchard downsampling module (AOD-Module) was designed to integrate the context guided block (CG Block) with depthwise separable convolution to replace the original downsampling layer in the backbone network, enhancing peach feature expression with a low computational cost. Second, an orchard gated spectrum block (OGS-Block) was proposed to combine frequency-domain dynamic convolution with a gating mechanism. The original C3K2 module was replaced for dual frequency-spatial perception and precise identification at different growth stages. Third, an orchard-adaptive feature fusion pyramid (OAFF-Pyramid) was constructed in the neck network. Within OAFF-Pyramid, an orchard attentive upsampler (OAS-Sampler) was designed to combine EUCB and the Shift Channel Mix mechanism to reconstruct fruit contours occluded by branches and leaves; An OAF-Block was designed to fuse C2f with MSCB, CSP, and Shift Channel Mix to replace the original C3k2 feature extraction module. Finally, a separated and enhanced attention module (SEAM) was introduced at the detection head to compensate for feature loss caused by occlusion and model robustness under branch and leaf occlusion conditions. A flat peach dataset was constructed to collect 4 807 original images at the 143rd Regiment flat peach orchard in Shihezi, Xinjiang, China, covering three growth stages, namely the green and hard stage, the expansion and color transition stage, and the mature stage. A final dataset of 11 535 images was obtained after data augmentation was applied only to the training set. Experimental results demonstrated that: 1) The OGD-Net model achieved 6.0 G floating-point operations and a model size of 4.7 MB. On the test dataset from three different growth stages of flat peaches, the better performance was achieved, with the precision, recall, F1 score, mAP0.5, mAP0.75, and mAP0.5-0.95 of 88.17%, 84.60%, 86.31%, 93.14%, 89.12%, and 85.48%, respectively, which were improved by 0.75, 3.25, 2.04, 3.28, 5.36, and 6.38 percentage points over YOLOv11n. 2) Compared with YOLOv11n, the floating-point computation volume and model size of OGD-Net were reduced by 4.76% and 9.62%, respectively, indicating excellent lightweight suitability for mobile-terminal deployment. 3) In fruit enlargement and color transition, the detection accuracy mAP0.5 reached 89.63%, which improved by a 4.51 percentage point over YOLOv11n. 4) Compared with classic neck network architectures, such as BiFPN, GFPN, Gold-YOLO, and CGRFPN, the OAFF-Pyramid achieved an R-value of 83.97% and mAP0.5 of 90.72%, with a model size of only 4.5 MB, which was 13.46% smaller than the baseline. The heatmap highlights were highly concentrated on key characteristic areas of peaches, with no false detections or background false responses observed, indicating that the OAFF-Pyramid architecture exhibited superior detection and stronger generalization for growing peaches in complex orchard environments. 5) OGD-Net was deployed on an MMX5 Plus robot to validate the practicality of the model. Recognition success rates of 100%, 100%, and 93.3% were achieved under no occlusion, branch and leaf occlusion, and fruit overlap conditions, respectively, using an indoor peach orchard simulation platform. Grasping success rates of 93.3%, 86.7%, and 80% were obtained for mature peaches. The OGD-Net model achieved accurate identification of flat peaches during the entire growth period in natural orchard environments. The findings can provide the technical support to monitor precise growth and mechanical harvesting during orchard production.}
}