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:

A Data-Driven Approach to Vertical Farming
In the world of farming, new methods are constantly being explored to meet the increasing demand for food. One promising approach is the use of data-driven techniques in vertical farming, where crops are grown in controlled indoor environments.

We believe that the success of this project hinges on a multidisciplinary approach and welcome collaboration with domain experts and we welcome all to join us playing a vital role in bringing our innovative ideas to life and contributing to the growth of the start-up community in the region.