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EVEC Microgreens
Automated microgreen phenotyping for yield estimation using a consumer-grade depth camera
Microgreens are the first leafy seedlings of edible plants. Microgreen farming is yet to be automated; the main challenge for automation is the lack of a s
Bhanu Watawana, Mats Isaksson
Smart Agricultural Technology · 2023 · https://doi.org/10.1016/j.atech.2023.100384
Abstract
Microgreens are the first leafy seedlings of edible plants. Microgreen farming is yet to be automated; the main challenge for automation is the lack of a sensory mechanism to detect and quantify microgreen phenotypes. This paper presents a novel automated microgreen phenotyping method targeting yield estimation. The paper demonstrates that phenotyping can be effectively performed using a consumer-grade RGB-D camera. First, the depth and RGB images are captured. Thereafter, the plant segments are filtered and the canopy is identified. Using image processing, the canopy height and density are calculated. Both yield prediction regression analysis and a TensorFlow learning algorithm are evaluated to estimate the yield as a function of height and canopy density. The authors believe the algorithm discussed in this paper is the first phenotyping algorithm combining RGB and depth data for microgreen yield estimation.
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How to credit this work
Bhanu Watawana; Mats Isaksson (2023). Automated microgreen phenotyping for yield estimation using a consumer-grade depth camera. Smart Agricultural Technology. https://doi.org/10.1016/j.atech.2023.100384 Licensed under Creative Commons Attribution NC ND 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/). Hosted by EVEC Athens (microgreens.org.gr). No changes were made to this article. Provided as-is, without warranties. EVEC Athens is not affiliated with, and this copy is not endorsed by, the authors or the publisher.
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