In order to address the challenges of inefficiency and insufficient accuracy in the manual identification of young citrus fruits during thinning processes, this study proposes a detection methodology using the you only look once for complex backgrounds of young citrus fruits (YCCB-YOLO) approach. The method first constructs a dataset containing images of young citrus fruits in a real orchard environment. To improve the detection accuracy while maintaining the computational efficiency, the study reconstructs the detection head and backbone network using pointwise convolution (PWonv) lightweight network, which reduces the complexity of the model without affecting the performance. In addition, the ability of the model to accurately detect young citrus fruits in complex backgrounds is enhanced by integrating the fusion attention mechanism. Meanwhile, the simplified spatial pyramid pooling fast-large kernel separated attention (SimSPPF-LSKA) feature pyramid was introduced to further enhance the multi-feature extraction capability of the model. Finally, the Adam optimization function was used to strengthen the nonlinear representation and feature extraction ability of the model. The experimental results show that the model achieves 91.79% precision (P), 92.75% recall (R), and 97.32% mean average precision (mAP)on the test set, which were improved by 1.33%, 2.24%, and 1.73%, respectively, compared with the original model, and the size of the model is only 5.4 MB. This study could meet the performance requirements for citrus fruit identification, which provides technical support for fruit thinning.

中文翻译：

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绿色隐藏的水果：改进的YOLOV8n，用于检测茂盛柑橘树中的幼柑橘

为了解决间伐过程中人工识别柑橘幼果效率低下和准确性不足的挑战，本研究提出了一种检测方法，采用“只看一次柑橘幼果复杂背景”（YCCB-YOLO）方法。该方法首先构建一个数据集，其中包含真实果园环境中年轻柑橘类水果的图像。为了在保持计算效率的同时提高检测精度，研究采用点状卷积（PWonv）轻量级网络重构检测头和主干网络，在不影响性能的情况下降低了模型的复杂度。此外，通过融合融合注意力机制，增强了模型在复杂背景下准确检测年轻柑橘类水果的能力。同时，引入简化空间金字塔池化快速大核分离注意（SimSPPF-LSKA）特征金字塔，进一步增强模型的多特征提取能力。最后利用Adam优化函数强化模型的非线性表示和特征提取能力。实验结果表明，模型在测试集上实现了91.79%的查准率（P）、92.75%的查全率（R）和97.32%的平均查准率（mAP），分别提高了1.33%、2.24%和1.73%，与原始模型相比，模型大小仅为 5.4 MB。该研究能够满足柑橘类水果鉴定的性能要求，为疏果提供技术支撑。