Datasets:
File size: 1,988 Bytes
6c1c833 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | {
"field_id": "F014",
"field": "agricultural computer vision",
"directions": [
{
"direction_id": "G0",
"name": "Visual monitoring of crop disease and insect pests",
"scope": "Use agricultural imagery to determine crop diseases, find affected plants or symptom regions, or identify, locate, and count insect pests and infestations to provide information on biological threats. A study need not address both diseases and pests."
},
{
"direction_id": "G1",
"name": "Agricultural visual data and label-efficient learning",
"scope": "Address the domain-specific images, annotations, and usable learning evidence needed for agricultural visual research, including both the creation of reusable image resources and data workflows and the learning of effective models when annotations in a new target domain are insufficient."
},
{
"direction_id": "G2",
"name": "Detection and inventory of crops and produce",
"scope": "Distinguish, locate, track, or count discrete plants and agricultural produce in images or video to establish their presence, attributes, and abundance."
},
{
"direction_id": "G3",
"name": "Plant traits and spatial structure recovery",
"scope": "Derive structural, developmental, or measurable traits of plants and their organs from images or multi-view observations, and recover or maintain spatial representations and positions of objects such as trees and fruit. Trait measurement and persistent 3D localization are parallel branches."
},
{
"direction_id": "G4",
"name": "Spatial perception of agricultural fields",
"scope": "Parse the spatial distribution or scene relationships of field vegetation and other targets from agricultural imagery to inform monitoring, navigation, and site-specific management. This includes regional mapping from aerial or satellite views and operational scene perception from ground-level views."
}
]
}
|