Authors

Abstract

Aiming at the problem of differentiated cultivation strategies for different grape varieties, the AF-Swin Transformer model is proposed in this study. Firstly, Focal Loss is used to effectively tackle data imbalance in grape leaves. Secondly, the AdamW optimizer is selected to better control model complexity and improve generalization. The results show that the training accuracy of AF-Swin Transformer model is 7.87 percentage points higher than that of the original Swin Transformer model. Precision and recall improved by 4.4 and 4.3 percentage points, respectively. This study enables accurate automated variety monitoring within vineyard cultivation systems, assisting growers in implementing targeted cultivation strategies.

Abstract in Chinese

针对不同葡萄品种栽培策略存在差异化问题，本研究提出了AF-Swin Transformer模型。首先，引入Focal Loss,有效应对葡萄叶片数据不平衡，其次，选用AdamW优化器，更好地控制模型复杂度并提高泛化能力。结果表明，AF-Swin Transformer模型的训练集准确比原始Swin Transformer模型提高了7.87个百分点；精准率和召回率分别提高了4.4和4.3个百分点。本研究能够在葡萄园中种植系统中实现准确的自动化品种监测，帮助种植者实施有针对性的种植策略