mlsanityData to 7 Oct 2026

A Learned Simulation Environment to Model Plant Growth in Indoor Farming

arXiv 2212.03155 ↗First posted 6 Dec 2022eess.SY cs.LG cs.SYcited by 0

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Abstract (arXiv)

We developed a simulator to quantify the effect of changes in environmental parameters on plant growth in precision farming. Our approach combines the processing of plant images with deep convolutional neural networks (CNN), growth curve modeling, and machine learning. As a result, our system is able to predict growth rates based on environmental variables, which opens the door for the development of versatile reinforcement learning agents.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 17 references that are in the map. First posted on arXiv 6 Dec 2022.

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6 of 17 references.

References · 6

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2016Towards Automatic Wild Animal Monitoring: Identification of Animal Species in Camera-trap Images using Very Deep Convolutional Neural Networkscited by 28
  3. 2021CropGym: a Reinforcement Learning Environment for Crop Managementcited by 20
  4. 2019Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environmentscited by 9
  5. 2019Transfer Learning using CNN for Handwritten Devanagari Character Recognitioncited by 2
  6. 2021GrowSpace: Learning How to Shape Plantscited by 2

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