2020/04/01 by Dan Halbersberg, Halbersberg, Dan, Aharon Bar Hillel +11
Agricultural and Biological Sciences · Computer Science · Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Greenhouse Technology and Climate Control #Remote Sensing in Agriculture #Smart Agriculture and AI #cs.CV
paper · pdf · doi:10.48550/arxiv.2004.00303
Paper presented at the ICLR 2020 Workshop on Computer Vision for Agriculture (CV4A)
arxiv created 2020/04/01 · openalex publication_date 2020/04/01 · arxiv updated 2020/04/02 · openalex created_date 2020/04/10 · openalex updated_date 2026/07/28
Estimation of photometric plant phenotypes (e.g., hue, shine, chroma) in field conditions is important for decisions on the expected yield quality, fruit ripeness, and need for further breeding. Estimating these from images is difficult due to large variances in lighting conditions, shadows, and sensor properties. We combine the image and metadata regarding capturing conditions embedded into a network, enabling more accurate estimation and transfer between different conditions. Compared to a state-of-the-art deep CNN and a human expert, metadata embedding improves the estimation of the tomato's hue and chroma.