Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis

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. To encourage research in computer vision for agriculture, we present Agriculture-Vision: a large-scale aerial farmland image dataset for 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 RGB and Near-infrared (NIR) channels with resolution 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 Agriculture-Vision poses to both the computer vision and agriculture communities. Future versions of this dataset will include even more aerial images, anomaly patterns and image channels. More information at https://www.agriculture-vision.com.

arXiv commentCVPR 2020

References in corpus (11)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Rethinking Atrous Convolution for Semantic Image Segmentation
• Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
• DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images
• DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning
• Systematic evaluation of CNN advances on the ImageNet
• iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images
• SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems
• Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection
• Any-Precision Deep Neural Networks
• FOAL: Fast Online Adaptive Learning for Cardiac Motion Estimation

Cited by in corpus (8)

• Learned Visual Navigation for Under-Canopy Agricultural Robots
• Computer Vision with Deep Learning for Plant Phenotyping in Agriculture: A Survey
• PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation
• The 1st Agriculture-Vision Challenge: Methods and Results
• Global Wheat Challenge 2020: Analysis of the competition design and winning models
• Effective Data Fusion with Generalized Vegetation Index: Evidence from Land Cover Segmentation in Agriculture
• Reducing the feature divergence of RGB and near-infrared images using Switchable Normalization
• Abiotic Stress Prediction from RGB-T Images of Banana Plantlets