Fusion of Satellite Images and Weather Data with Transformer Networks for Downy Mildew Disease Detection

• URL: http://arxiv.org/abs/2209.02797v1
• Date: Tue, 6 Sep 2022 19:55:16 GMT
• Title: Fusion of Satellite Images and Weather Data with Transformer Networks for Downy Mildew Disease Detection
• Authors: William Maillet, Maryam Ouhami, Adel Hafiane
• Abstract summary: Crop diseases significantly affect the quantity and quality of agricultural production. In this paper, we propose a new approach to realize data fusion using three transformers. The architecture is built from three main components, a Vision Transformer and two transformer-encoders, allowing to fuse both image and weather modalities.
• Score: 3.6868861317674524
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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