High Precision Leaf Instance Segmentation for Phenotyping in Point Clouds Obtained Under Real Field Conditions

Measuring plant traits with high throughput allows breeders to monitor and select the best cultivars for subsequent breeding generations. This can enable farmers to improve yield to produce more food, feed, and fiber. Current breeding practices involve extracting leaf parameters on a small subset of...

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Vydané v:IEEE robotics and automation letters Ročník 8; číslo 8; s. 4791 - 4798
Hlavní autori: Marks, Elias, Sodano, Matteo, Magistri, Federico, Wiesmann, Louis, Desai, Dhagash, Marcuzzi, Rodrigo, Behley, Jens, Stachniss, Cyrill
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Agricultural automation
Artificial neural networks
Crops
Deep learning
deep learning for visual perception
Encoding
Image segmentation
Instance segmentation
Neural networks
Physical work
Point cloud compression
robotics and automation in agriculture and forestry
Task analysis
ISSN:2377-3766, 2377-3766
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Shrnutí:Measuring plant traits with high throughput allows breeders to monitor and select the best cultivars for subsequent breeding generations. This can enable farmers to improve yield to produce more food, feed, and fiber. Current breeding practices involve extracting leaf parameters on a small subset of the leaves present in the breeding plots, while still requiring substantial manual labor. To automate this process, an important step is the precise distinction between separate leaves, which is the problem we address in this letter. We exploit recent advancements in 3D deep learning to build a convolutional neural network that learns to segment individual leaves. As done in current breeding practices, we select a subset of leaves to be used for phenotypic trait evaluation as this allows us to alleviate the influence of segmentation errors on the phenotypic trait estimation. To this extent we propose to use an additional neural network to predict the quality of each segmented leaf and discard inaccurate leaf instances. The experiments show that our network yields higher segmentation accuracy on sugar beet breeding plots planted under the supervision of the German Federal Office for Plant Varieties. Furthermore, we show that our neural network helps in filtering out leaves with lower segmentation accuracy.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2023.3288383