融合多源评价数据的荔枝果期表型特征评估

Evaluation of Phenotypic Characteristics in Litchi Fruiting Stage Using Multi-source Evaluation Data

摘要:

人工智能技术在荔枝表型获取方面的研究目前主要集中于对象识别、产量预估和采摘定位等，对荔枝完整果期生长质量的评价技术较为缺乏。本研究通过融合多源数据指标，对荔枝果期生长质量进行综合评估，生成荔枝果期评价画像。基于YOLO v7网络框架提出果实识别算法LFS-YOLO，通过减少由动态环境背景引起的误差和影响，集成全局注意力能力，提升全景图像识别的准确性。其次，通过优化CIoU损失函数，添加考虑预期回归向量之间的角度，重新定义并改进角度惩罚测度以减少整体自由度，将预测框更有效地对齐到最近的轴上。通过融合多源数据，建立质量评估函数，为综合评价提供依据。试验结果表明，LFS-YOLO对果实识别精度达到89.1%，精确率为92.3%，召回率为93.0%，且生成的荔枝果期表型特征评估方法可显示荔枝果期影响生长质量各项指标，为荔枝果期综合评价发展提供启示作用。

Abstract:

The application of artificial intelligence technology in litchi phenotype acquisition mainly focuses on object recognition, yield estimation, and picking localization. However, there is a notable lack of evaluation technology for assessing litchi growth quality throughout its entire fruiting stage. Aiming to integrate multi-source data indicators to perform a comprehensive assessment of litchi growth quality during the fruiting stage, thereby generating the evaluation profiles for litchi fruiting stages, based on the YOLO v7 network framework, an object recognition algorithm named LFS-YOLO was proposed. This algorithm enhanced recognition accuracy by mitigating errors and influences stemming from dynamic environmental backgrounds and by incorporating global attention mechanisms. Furthermore, the CIoU loss function was optimized through the inclusion of the angle between predicted regression vectors, which redefined and improved the angle penalty measure. This optimization reduced the overall degrees of freedom, thereby facilitating a more effective alignment of predicted bounding boxes with the nearest axis. By integrating multi-dimensional data, a quality evaluation function was established as the foundation for comprehensive evaluation. Experimental results indicated that the LFS-YOLO algorithm achieved a recognition accuracy of 89.1%, a precision of 92.3%, and a recall of 93.0%. The evaluation profiles generated for the litchi fruiting stage illustrated various indicators that influence growth quality throughout this stage, providing valuable insights for the advancement of comprehensive evaluation technologies pertaining to litchi fruiting stage.