Furrow Mapping of Sugarcane Billet Density Using Deep Learning and Object Detection
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Author(s)
Scott, J
Busch, A
Griffith University Author(s)
Year published
2020
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Australia's sugar industry is currently undergoing significant hardships, due to global market contractions from COVID-19, increased crop forecasts from larger global producers, and falling oil prices. Current planting practices utilize inefficient mass-flow planting techniques, and no attempt to map the seed using machine vision has been made, to date, in order to understand the underlying problems. This paper investigates the plausibility of creating a labeled sugarcane billet dataset using a readily-available camera positioned beneath a planter and analysing this using a YOLOv3 network. This network resulted in a high ...
View more >Australia's sugar industry is currently undergoing significant hardships, due to global market contractions from COVID-19, increased crop forecasts from larger global producers, and falling oil prices. Current planting practices utilize inefficient mass-flow planting techniques, and no attempt to map the seed using machine vision has been made, to date, in order to understand the underlying problems. This paper investigates the plausibility of creating a labeled sugarcane billet dataset using a readily-available camera positioned beneath a planter and analysing this using a YOLOv3 network. This network resulted in a high mean average precision at intersect over union of 0.5 (mAP50) of 0.852 on test images, and was used to provide planting metrics by generating a furrow map.
View less >
View more >Australia's sugar industry is currently undergoing significant hardships, due to global market contractions from COVID-19, increased crop forecasts from larger global producers, and falling oil prices. Current planting practices utilize inefficient mass-flow planting techniques, and no attempt to map the seed using machine vision has been made, to date, in order to understand the underlying problems. This paper investigates the plausibility of creating a labeled sugarcane billet dataset using a readily-available camera positioned beneath a planter and analysing this using a YOLOv3 network. This network resulted in a high mean average precision at intersect over union of 0.5 (mAP50) of 0.852 on test images, and was used to provide planting metrics by generating a furrow map.
View less >
Conference Title
2020 Digital Image Computing: Techniques and Applications, DICTA 2020
Funder(s)
ARC
Grant identifier(s)
IH180100002
Copyright Statement
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Subject
Computer vision
Agriculture, land and farm management