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.

To detect plant diseases efficiently, incorporating data collection, sensors, and machine learning can significantly enhance the detection process. Here's an outline of an optimal approach:

The pilot program consists of two critical phases: IoT/sensors/datalogging and AI/ML. To achieve seamless integration, both teams must work collaboratively. The key to success lies in proper design, accurate monitoring, and triggering, with comprehensive datalogging of all relevant data points.