AI for Agriculture: the Comparison of Semantic Segmentation Methods for Crop Mapping with Sentinel-2 Imagery
This work addresses crop mapping for agriculture, particularly vineyards, to support food security and climate adaptation, but it is incremental as it focuses on comparing existing methods rather than introducing new ones.
The paper tackles the problem of vineyard segmentation using lower-resolution Sentinel-2 satellite imagery, where texture information is limited, by comparing various machine learning methods and providing guidance on model selection for specific scenarios.
Crop mapping is one of the most common tasks in artificial intelligence for agriculture due to higher food demands from a growing population and increased awareness of climate change. In case of vineyards, the texture is very important for crop segmentation: with higher resolution satellite imagery the texture is easily detected by majority of state-of-the-art algorithms. However, this task becomes increasingly more difficult as the resolution of satellite imagery decreases and the information about the texture becomes unavailable. In this paper we aim to explore the main machine learning methods that can be used with freely available satellite imagery and discuss how and when they can be applied for vineyard segmentation problem. We assess the effectiveness of various widely-used machine learning techniques and offer guidance on selecting the most suitable model for specific scenarios.