论文标题
土壤分类的视觉变压器
Visual Transformer for Soil Classification
论文作者
论文摘要
我们的粮食安全建立在土壤的基础上。如果土壤不健康，农民将无法用纤维，食物和燃料喂养我们。准确预测土壤的类型有助于计划土壤的使用，从而提高生产率。这项研究采用了最先进的视觉变压器，并与SVM，Alexnet，Resnet和CNN等不同模型进行了比较。此外，这项研究还着重于区分不同的视觉变压器体系结构。对于土壤类型的分类，数据集由4种不同类型的土壤样品组成，例如冲积，红色，黑色和粘土。 Visual Transformer模型在测试和测试时达到98.13％的训练和93.62％，优于测试和训练精度的其他模型。视觉变压器的性能超过了其他模型的性能至少2％。因此，新颖的视觉变压器可用于计算机视觉任务，包括土壤分类。
Our food security is built on the foundation of soil. Farmers would be unable to feed us with fiber, food, and fuel if the soils were not healthy. Accurately predicting the type of soil helps in planning the usage of the soil and thus increasing productivity. This research employs state-of-the-art Visual Transformers and also compares performance with different models such as SVM, Alexnet, Resnet, and CNN. Furthermore, this study also focuses on differentiating different Visual Transformers architectures. For the classification of soil type, the dataset consists of 4 different types of soil samples such as alluvial, red, black, and clay. The Visual Transformer model outperforms other models in terms of both test and train accuracies by attaining 98.13% on training and 93.62% while testing. The performance of the Visual Transformer exceeds the performance of other models by at least 2%. Hence, the novel Visual Transformers can be used for Computer Vision tasks including Soil Classification.
