论文标题
卫星图像和天气数据与变压器网络的融合，以降低发霉疾病检测
Fusion of Satellite Images and Weather Data with Transformer Networks for Downy Mildew Disease Detection
论文作者
论文摘要
作物疾病显着影响农业生产的数量和质量。在精确农业的目标是最大程度地减少甚至避免使用农药的使用，天气和遥感数据具有深度学习可以在检测作物疾病中发挥关键作用，从而允许对农作物进行局部治疗。但是，结合天气和图像等异质数据仍然是一个热门话题和具有挑战性的任务。变压器体系结构的最新发展表明了从不同域（例如文本图像）融合数据的可能性。当前的趋势是仅定制一个变压器来创建多模式融合模型。相反，我们提出了一种使用三个变压器实现数据融合的新方法。在本文中，我们首先通过使用ConvlstM模型来插值来解决缺失的卫星图像问题。然后，提出了一种多模式融合体系结构，该体系结构共同学习处理视觉和天气信息。该体系结构是由三个主要组件，一个视觉变压器和两个变压器编码器构建的，可以融合图像和天气方式。所提出的方法的结果有望达到97 \％的总体准确性。
Crop diseases significantly affect the quantity and quality of agricultural production. In a context where the goal of precision agriculture is to minimize or even avoid the use of pesticides, weather and remote sensing data with deep learning can play a pivotal role in detecting crop diseases, allowing localized treatment of crops. However, combining heterogeneous data such as weather and images remains a hot topic and challenging task. Recent developments in transformer architectures have shown the possibility of fusion of data from different domains, for instance text-image. The current trend is to custom only one transformer to create a multimodal fusion model. Conversely, we propose a new approach to realize data fusion using three transformers. In this paper, we first solved the missing satellite images problem, by interpolating them with a ConvLSTM model. Then, proposed a multimodal fusion architecture that jointly learns to process visual and weather information. The architecture is built from three main components, a Vision Transformer and two transformer-encoders, allowing to fuse both image and weather modalities. The results of the proposed method are promising achieving 97\% overall accuracy.
