What is it about?

The ability to generate realistic synthetic images of leaves has been explored since time in the field of computer graphics to create scenes with credible landscapes covered with plants, trees or meadows for use in computer games, virtual reality and entertainment industry. Current interest has been expanding to quantitative applications related to advanced approaches in botanics, plant breeding and agriculture. In this work we propose an approach, at the best of our knowledge not explored before, and, namely, we use implement Deep Learning techniques to automatically generate collections of synthetic images of plant leaves.

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Why is it important?

We use the virtually unlimited synthetic images of leaves to enrich dataset of natural leaf images used for training Convolutional Neural Networks dedicated to smart agriculture applications. These models require indeed enormous amounts of annotated training images examples to avoid overfitting phenomena. Yet in real world applications annotated data are very often limited, especially for semantic segmentation tasks that typically require pixel-scale accuracy in manual labeling of training images.


Positive impacts of the present work are the availability of synthetic samples which have a quantitive—and not only qualitative—resemblance to real leaf samples so to alleviate the burden of manually collecting and annotating hundreds of data. Image-sensing in smart agriculture is an innovative approach which can deeply impact everybody’s life, being connected to effective and sustainable food production. Moreover, while our target application was the enrichment of datasets for DL-based image sensing in smart agriculture, we deem that the present approach may be of interest also in a wider range of collateral fields, including computer graphics applications in all their declinations.

Paola Causin
Universita degli Studi di Milano

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This page is a summary of: A deep learning generative model approach for image synthesis of plant leaves, PLoS ONE, November 2022, PLOS, DOI: 10.1371/journal.pone.0276972.
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