Project Launch: High-Density Strawberry Farming in a Controlled Environment

After successfully developing a Minimum Viable Product (MVP) to grow lettuce hydroponically using IoT and machine learning, we’re now embarking on an exciting expansion: growing strawberries in a controlled environment agriculture (CEA) system.

This project isn't just about growing berries — it's about creating a repeatable, scalable, and data-driven framework for high-value crop cultivation that aligns with economic principles of Return on Invested Capital (ROIC) > Weighted Average Cost of Capital (WACC).

Hydroponic farming requires precise monitoring and control, often facilitated by IoT devices like Raspberry Pis. Combining data from multiple Pis into a cohesive dataset stored in a database like MongoDB is crucial for effective analysis and machine learning applications.

Measuring various features such as temperature, humidity, light intensity (E), pH levels, carbon dioxide concentration (CO2), plant height, and plant health at frequent intervals using supervised machine learning, particularly logistic regression, can indeed provide insights into predicting successful lettuce leafy green harvests. However, the accuracy of such predictions may vary depending on several factors, including the quality and quantity of data, feature selection, and model complexity.