Agriculture-vision: A large aerial image database for agricultural pattern analysis
Chiu, Mang Tik
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https://hdl.handle.net/2142/117694
Description
Title
Agriculture-vision: A large aerial image database for agricultural pattern analysis
Author(s)
Chiu, Mang Tik
Issue Date
2022-08-30
Director of Research (if dissertation) or Advisor (if thesis)
Shi, Humphrey
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
vision
agriculture
segmentation
aerial image
Language
eng
Abstract
The success of deep learning in visual recognition tasks has driven advancements in multiple fields of research. Particularly, increasing attention has been drawn towards its application in agriculture. Nevertheless, while visual pattern recognition on farmlands carries enormous economic values, little progress has been made to merge computer vision and crop sciences due to the lack of suitable agricultural image datasets. Meanwhile, problems in agriculture also pose new challenges in computer vision. For example, semantic segmentation of aerial farmland images requires inference over extremely large-size images with extreme annotation sparsity. These challenges are not present in most of the common object datasets, and we show that they are more challenging than many other aerial image datasets. In this thesis, we present a pilot study in computer vision for agriculture, specifically semantic segmentation of agricultural patterns. We collected $94,986$ high-quality aerial images from $3,432$ farmlands across the US, where each image consists of Red-Green-Blue (RGB) and Near-infrared (NIR) channels with resolutions as high as 10 cm per pixel. We annotate nine types of field anomaly patterns that are most important to farmers. As a pilot study of aerial agricultural semantic segmentation, we perform comprehensive experiments using popular semantic segmentation models; we also propose an effective model designed for aerial agricultural pattern recognition. Our experiments demonstrate several challenges of vision and agriculture.
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