Joint Plant Instance Detection and Leaf Count Estimation for In-Field Plant Phenotyping

Precision management of agricultural fields as well as plant breeding are central factors for keeping yields high and to provide food, feed, and fiber for our society. A key element in breeding trials but also for targeted management actions is to analyze the growth state of individual plants object...

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Vydané v:IEEE robotics and automation letters Ročník 6; číslo 2; s. 3599 - 3606
Hlavní autori: Weyler, Jan, Milioto, Andres, Falck, Tillmann, Behley, Jens, Stachniss, Cyrill
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Agriculture
Crops
Deep learning for visual perception
Feeds
Heating systems
Image segmentation
Object recognition
Plants (biology)
robotics and automation in agriculture and forestry
Robots
Three-dimensional displays
ISSN:2377-3766, 2377-3766
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Shrnutí:Precision management of agricultural fields as well as plant breeding are central factors for keeping yields high and to provide food, feed, and fiber for our society. A key element in breeding trials but also for targeted management actions is to analyze the growth state of individual plants objectively and at a large scale. In this letter, we address the problem of analyzing crops in real agricultural fields based on camera data recorded with mobile robots and to derive information about the plant development, e.g., to monitor phenotypic traits such as growth stage. We propose a novel single-stage object detection approach that localizes crops and weeds in the field. At the same time, it detects plant-specific leaf keypoints intending to estimate leaf count at a plant level, which is a key trait for classifying the growth stage. We implemented and thoroughly tested our approach on real sugar beet fields. As our experiments show, it performs the required detections and shows superior performance with respect to a state-of-the-art two-stage approach based on Mask R-CNN.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2021.3060712