A Learned Simulation Environment to Model Plant Growth in Indoor Farming

TL;DR

This paper presents a learned simulation environment that models plant growth in indoor farming by integrating deep learning and growth modeling to predict outcomes based on environmental factors.

Contribution

It introduces a novel simulator combining CNNs and growth curves for accurate plant growth prediction in precision agriculture.

Findings

Accurately predicts plant growth rates from environmental data

Enables development of reinforcement learning agents for farming optimization

Demonstrates effectiveness of deep learning in plant growth modeling

Abstract

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.