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Open AccessArticle

Vision Based Modeling of Plants Phenotyping in Vertical Farming under Artificial Lighting

1
Agricola Moderna, Viale Col di Lana 8, 20136 Milan, Italy
2
Alcor Lab, DIAG, Sapienza University of Rome, Via Ariosto 25, 00185 Rome, Italy
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(20), 4378; https://doi.org/10.3390/s19204378
Received: 2 September 2019 / Revised: 3 October 2019 / Accepted: 8 October 2019 / Published: 10 October 2019
(This article belongs to the Section Intelligent Sensors)
In this paper, we present a novel method for vision based plants phenotyping in indoor vertical farming under artificial lighting. The method combines 3D plants modeling and deep segmentation of the higher leaves, during a period of 25–30 days, related to their growth. The novelty of our approach is in providing 3D reconstruction, leaf segmentation, geometric surface modeling, and deep network estimation for weight prediction to effectively measure plant growth, under three relevant phenotype features: height, weight and leaf area. Together with the vision based measurements, to verify the soundness of our proposed method, we also harvested the plants at specific time periods to take manual measurements, collecting a great amount of data. In particular, we manually collected 2592 data points related to the plant phenotype and 1728 images of the plants. This allowed us to show with a good number of experiments that the vision based methods ensure a quite accurate prediction of the considered features, providing a way to predict plant behavior, under specific conditions, without any need to resort to human measurements. View Full-Text
Keywords: vision based phenotyping; plants growth prediction; vertical farming; LED vision based phenotyping; plants growth prediction; vertical farming; LED
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Franchetti, B.; Ntouskos, V.; Giuliani, P.; Herman, T.; Barnes, L.; Pirri, F. Vision Based Modeling of Plants Phenotyping in Vertical Farming under Artificial Lighting. Sensors 2019, 19, 4378.

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