Synthesizing Forestry Images Conditioned on Plant Phenotype Using a Generative Adversarial Network

TL;DR

This paper presents a GAN-based method to generate realistic forestry images conditioned on plant greenness, aiding phenotypic analysis and visualization of forests under different environmental conditions.

Contribution

The work introduces a novel GAN model that synthesizes phenotypically stable forestry images conditioned on greenness, with demonstrated scalability and applicability to various forest types.

Findings

Synthetic images closely match original greenness indices.

The GAN effectively predicts plant redness from generated images.

The model generalizes well to different forest sites.

Abstract

Plant phenology and phenotype prediction using remote sensing data are increasingly gaining attention within the plant science community as a promising approach to enhance agricultural productivity. This work focuses on generating synthetic forestry images that satisfy certain phenotypic attributes, viz. canopy greenness. We harness a Generative Adversarial Network (GAN) to synthesize biologically plausible and phenotypically stable forestry images conditioned on the greenness of vegetation (a continuous attribute) over a specific region of interest, describing a particular vegetation type in a mixed forest. The training data is based on the automated digital camera imagery provided by the National Ecological Observatory Network (NEON) and processed by the PhenoCam Network. Our method helps render the appearance of forest sites specific to a greenness value. The synthetic images are subsequently utilized to predict another phenotypic attribute, viz., redness of plants. The quality of the synthetic images is assessed using the Structural SIMilarity (SSIM) index and Fr\'echet Inception Distance (FID). Further, the greenness and redness indices of the synthetic images are compared against those of the original images using Root Mean Squared Percentage Error (RMSPE) to evaluate their accuracy and integrity. The generalizability and scalability of our proposed GAN model are established by effectively transforming it to generate synthetic images for other forest sites and vegetation types. From a broader perspective, this approach could be leveraged to visualize forestry based on different phenotypic attributes in the context of various environmental parameters.