Enlisting 3D Crop Models and GANs for More Data Efficient and Generalizable Fruit Detection

TL;DR

This paper introduces a semantically constrained GAN that converts synthetic 3D crop models into photorealistic images, improving data efficiency and generalization in fruit detection across diverse agricultural domains.

Contribution

The proposed method enhances domain adaptation by preserving fruit position and geometry during image translation, outperforming baseline GANs in agricultural image synthesis.

Findings

Generated images improve vineyard grape detection accuracy.

Method reduces labeling effort for new domains.

Images facilitate faster domain adaptation.

Abstract

Training real-world neural network models to achieve high performance and generalizability typically requires a substantial amount of labeled data, spanning a broad range of variation. This data-labeling process can be both labor and cost intensive. To achieve desirable predictive performance, a trained model is typically applied into a domain where the data distribution is similar to the training dataset. However, for many agricultural machine learning problems, training datasets are collected at a specific location, during a specific period in time of the growing season. Since agricultural systems exhibit substantial variability in terms of crop type, cultivar, management, seasonal growth dynamics, lighting condition, sensor type, etc, a model trained from one dataset often does not generalize well across domains. To enable more data efficient and generalizable neural network models in agriculture, we propose a method that generates photorealistic agricultural images from a synthetic 3D crop model domain into real world crop domains. The method uses a semantically constrained GAN (generative adversarial network) to preserve the fruit position and geometry. We observe that a baseline CycleGAN method generates visually realistic target domain images but does not preserve fruit position information while our method maintains fruit positions well. Image generation results in vineyard grape day and night images show the visual outputs of our network are much better compared to a baseline network. Incremental training experiments in vineyard grape detection tasks show that the images generated from our method can significantly speed the domain adaption process, increase performance for a given number of labeled images (i.e. data efficiency), and decrease labeling requirements.