■微生物改变的精确语义分割对于其评估和治疗至关重要。本研究的重点是利用SegFormer分割模型对草莓病害进行精确的语义分割,旨在提高自然采集条件下的疾病检测精度。
■彻底分析了三种不同的混合变压器编码器-MiT-B0,MiT-B3和MiT-B5-以增强疾病检测,针对角斑病等疾病,炭疽病,花枯病,灰色模具,叶斑病,水果上的白粉病,和叶子上的白粉病。数据集包括2,450张原始图像,扩展到4,574张增强图像。集成到Roboflow注释工具中的SegmentAnything模型促进了高效的注释和数据集准备。
■结果表明,MiT-B0表现出平衡但略微过拟合的行为,MiT-B3快速适应,具有一致的训练和验证性能,MiT-B5提供了偶尔波动的高效学习,提供强大的性能。MiT-B3和MiT-B5在不同疾病类型中的表现始终优于MiT-B0,与MiT-B5实现最精确的分割一般。
■这些发现为研究人员选择最适合疾病检测应用的编码器提供了关键见解,推进该领域的进一步调查。草莓病害分析的成功表明,将这种方法扩展到其他作物和病害的潜力,为未来的研究和跨学科合作铺平道路。
UNASSIGNED: Precise semantic segmentation of microbial alterations is paramount for their evaluation and treatment. This study focuses on harnessing the SegFormer segmentation model for precise semantic segmentation of strawberry diseases, aiming to improve disease detection accuracy under natural acquisition conditions.
UNASSIGNED: Three distinct Mix Transformer encoders - MiT-B0, MiT-B3, and MiT-B5 - were thoroughly analyzed to enhance disease detection, targeting diseases such as Angular leaf spot, Anthracnose rot, Blossom blight, Gray mold, Leaf spot, Powdery mildew on fruit, and Powdery mildew on leaves. The dataset consisted of 2,450 raw images, expanded to 4,574 augmented images. The Segment Anything Model integrated into the Roboflow annotation tool facilitated efficient annotation and dataset preparation.
UNASSIGNED: The results reveal that MiT-B0 demonstrates balanced but slightly overfitting behavior, MiT-B3 adapts rapidly with consistent training and validation performance, and MiT-B5 offers efficient learning with occasional fluctuations, providing robust performance. MiT-B3 and MiT-B5 consistently outperformed MiT-B0 across disease types, with MiT-B5 achieving the most precise segmentation in general.
UNASSIGNED: The findings provide key insights for researchers to select the most suitable encoder for disease detection applications, propelling the field forward for further investigation. The success in strawberry disease analysis suggests potential for extending this approach to other crops and diseases, paving the way for future research and interdisciplinary collaboration.