Transfer Learning of Photometric Phenotypes in Agriculture Using Metadata

• URL: http://arxiv.org/abs/2004.00303v1
• Date: Wed, 1 Apr 2020 09:24:34 GMT
• Title: Transfer Learning of Photometric Phenotypes in Agriculture Using Metadata
• Authors: Dan Halbersberg, Aharon Bar Hillel, Shon Mendelson, Daniel Koster, Lena Karol, and Boaz Lerner
• Abstract summary: 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. 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.
• Score: 6.034634994180246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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