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

arXiv: https://arxiv.org/abs/2105.07789 · HTML: https://mlsanity.com/paper/2105.07789/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 60 references that are in the map.
- First posted on arXiv 17 May 2021.

## Builds on (most cited first, 23 of 23)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [Pros and Cons of GAN Evaluation Measures](https://mlsanity.com/paper/1802.03446/) (2018)
- [Deep learning in agriculture: A survey](https://mlsanity.com/paper/1807.11809/) (2018)
- [Diversity-Sensitive Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1901.09024/) (2019)
- [Face Aging With Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1702.01983/) (2017)
- [Conditional Image-to-Image Translation](https://mlsanity.com/paper/1805.00251/) (2018)
- [ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network](https://mlsanity.com/paper/1709.00938/) (2017)
- [Counting of Grapevine Berries in Images via Semantic Segmentation using Convolutional Neural Networks](https://mlsanity.com/paper/2004.14010/) (2020)
- [Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments](https://mlsanity.com/paper/1907.00624/) (2019)
- [Unsupervised Image Translation using Adversarial Networks for Improved Plant Disease Recognition](https://mlsanity.com/paper/1909.11915/) (2019)
- [Improved Part Segmentation Performance by Optimising Realism of Synthetic Images using Cycle Generative Adversarial Networks](https://mlsanity.com/paper/1803.06301/) (2018)
- [Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields](https://mlsanity.com/paper/1712.05647/) (2017)

## Built on by (most cited first, 12 of 12)

- [GrowliFlower: An image time series dataset for GROWth analysis of cauLIFLOWER](https://mlsanity.com/paper/2204.00294/) (2022)
- [Enlisting 3D Crop Models and GANs for More Data Efficient and Generalizable Fruit Detection](https://mlsanity.com/paper/2108.13344/) (2021)
- [Generative AI in Agriculture: Creating Image Datasets Using DALL.E's Advanced Large Language Model Capabilities](https://mlsanity.com/paper/2307.08789/) (2023)
- [Data-Centric Digital Agriculture: A Perspective](https://mlsanity.com/paper/2312.03437/) (2023)
- [Agricultural Plant Cataloging and Establishment of a Data Framework from UAV-based Crop Images by Computer Vision](https://mlsanity.com/paper/2201.02885/) (2022)
- [DT/MARS-CycleGAN: Improved Object Detection for MARS Phenotyping Robot](https://mlsanity.com/paper/2310.12787/) (2023)
- [Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review](https://mlsanity.com/paper/2204.04707/) (2022)
- [Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation](https://mlsanity.com/paper/2204.08271/) (2022)
- [Data-driven Crop Growth Simulation on Time-varying Generated Images using Multi-conditional Generative Adversarial Networks](https://mlsanity.com/paper/2312.03443/) (2023)
- [Generative Plant Growth Simulation from Sequence-Informed Environmental Conditions](https://mlsanity.com/paper/2405.14796/) (2024)
- [Predictive Modeling, Pattern Recognition, and Spatiotemporal Representations of Plant Growth in Simulated and Controlled Environments: A Comprehensive Review](https://mlsanity.com/paper/2412.10538/) (2024)
- [Forecasting the Number of Harvest-ready Fruits of Sweet Peppers Using Multimodal Time-Series Data](https://mlsanity.com/paper/2607.19975/) (2026)

## Abstract (arXiv)

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.