Crops growth management in greenhouses is fundamental for their economical and ecologicalsustainability. Typically, smaller size greenhouses have the challenge to grow more than onecrop variety, each having different growth control strategies. A precise estimate of theexpected harvest and crop balance allows greenhouse managers to preventively sell crops onthe market while minimizing the risk of overselling and waste. Moreover, being able tomonitor the crop balance and plant load of all different varieties in the greenhouse, thegrower can take measures to control crop growth in a way to reduce fluctuations that wouldrequire constantly adjusting the workforce for all required tasks (e.g., harvesting, leafthinning, plant rotation). Crop growth prediction is currently performed manually, collecting data from sampled plants(e.g., measuring stems, counting flowers and fruits), which is expensive, time consuming, andonly gives a sample representation of the greenhouse.Precision agriculture (e.g., depth and spectral cameras, AI, robots) can provide an inexpensiveand effective solution to collect data. A data-driven approach to greenhouse growthmanagement would benefit all producers, optimize production, and reduce waste acrossentire markets.