Automated microgreen phenotyping for yield estimation using a consumer-grade depth camera

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.