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

Plant phenology and phenotype prediction using remote sensing data is increasingly gaining the attention of the plant science community to improve agricultural productivity. In this work, we generate synthetic forestry images that satisfy certain phenotypic attributes, viz. canopy greenness. The greenness index of plants describes a particular vegetation type in a mixed forest. Our objective is to develop a Generative Adversarial Network (GAN) to synthesize forestry images conditioned on this continuous attribute, i.e., greenness of vegetation, over a specific region of interest. 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. The synthetic images generated by our method are also used to predict another phenotypic attribute, viz., redness of plants. The Structural SIMilarity (SSIM) index is utilized to assess the quality of the synthetic images. The greenness and redness indices of the generated synthetic images are compared against that of the original images using Root Mean Squared Error (RMSE) in order to evaluate their accuracy and integrity. Moreover, the generalizability and scalability of our proposed GAN model is determined by effectively transforming it to generate synthetic images for other forest sites and vegetation types.