3D shape sensing and deep learning-based segmentation of strawberries

Le Louedec, Justin and Cielniak, Grzegorz (2021) 3D shape sensing and deep learning-based segmentation of strawberries. Computers and Electronics in Agriculture, 190 . ISSN 0168-1699

Full content URL: https://doi.org/10.1016/j.compag.2021.106374

3D shape sensing and deep learning-based segmentation of strawberries
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Automation and robotisation of the agricultural sector are seen as a viable solution to socio-economic challenges
faced by this industry. This technology often relies on intelligent perception systems providing information about
crops, plants and the entire environment. The challenges faced by traditional 2D vision systems can be addressed
by modern 3D vision systems which enable straightforward localisation of objects, size and shape estimation, or
handling of occlusions. So far, the use of 3D sensing was mainly limited to indoor or structured environments. In
this paper, we evaluate modern sensing technologies including stereo and time-of-flight cameras for 3D
perception of shape in agriculture and study their usability for segmenting out soft fruit from background based
on their shape. To that end, we propose a novel 3D deep neural network which exploits the organised nature of
information originating from the camera-based 3D sensors. We demonstrate the superior performance and ef­
ficiency of the proposed architecture compared to the state-of-the-art 3D networks. Through a simulated study,
we also show the potential of the 3D sensing paradigm for object segmentation in agriculture and provide in­
sights and analysis of what shape quality is needed and expected for further analysis of crops. The results of this
work should encourage researchers and companies to develop more accurate and robust 3D sensing technologies
to assure their wider adoption in practical agricultural applications.

Keywords:3D shape sensing, Semantic segmentation for agriculture, Machine learning architectures, Simulation
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
G Mathematical and Computer Sciences > G760 Machine Learning
D Veterinary Sciences, Agriculture and related subjects > D400 Agriculture
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:47035
Deposited On:22 Nov 2021 16:16

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