Inside out: transforming images of lab-grown plants for machine learning applications in agriculture.

ABSTRACT:

Introduction

Machine learning tasks often require a significant amount of training data for the resultant network to perform suitably for a given problem in any domain. In agriculture, dataset sizes are further limited by phenotypical differences between two plants of the same genotype, often as a result of different growing conditions. Synthetically-augmented datasets have shown promise in improving existing models when real data is not available.

Methods

In this paper, we employ a contrastive unpaired translation (CUT) generative adversarial network (GAN) and simple image processing techniques to translate indoor plant images to appear as field images. While we train our network to translate an image containing only a single plant, we show that our method is easily extenda

SUBMITTER: Krosney AE 

PROVIDER: S-EPMC10358354 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature