A Learned Simulation Environment to Model Plant Growth in Indoor Farming
This work addresses the need for precise growth modeling in indoor farming, though it appears incremental as it combines existing methods like CNNs and growth curve modeling.
The researchers tackled the problem of modeling plant growth in indoor farming by developing a simulator that predicts growth rates based on environmental variables, achieving results that enable the development of reinforcement learning agents.
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.