Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks

TL;DR

This paper introduces a novel conditional generative adversarial network model for predicting future plant growth stages from high-throughput imaging data, aiding farmers in decision-making.

Contribution

It presents a new machine learning-based growth prediction model using conditional GANs that generates realistic future plant images from time-series data.

Findings

The model produces realistic and reliable future plant images.

It enables automatic extraction of phenotypic traits.

Validated on Arabidopsis and cauliflower datasets.

Abstract

Farmers frequently assess plant growth and performance as basis for making decisions when to take action in the field, such as fertilization, weed control, or harvesting. The prediction of plant growth is a major challenge, as it is affected by numerous and highly variable environmental factors. This paper proposes a novel monitoring approach that comprises high-throughput imaging sensor measurements and their automatic analysis to predict future plant growth. Our approach's core is a novel machine learning-based generative growth model based on conditional generative adversarial networks, which is able to predict the future appearance of individual plants. In experiments with RGB time-series images of laboratory-grown Arabidopsis thaliana and field-grown cauliflower plants, we show that our approach produces realistic, reliable, and reasonable images of future growth stages. The automatic interpretation of the generated images through neural network-based instance segmentation allows the derivation of various phenotypic traits that describe plant growth.