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.

The scenario described and discussed in Part1 of this article, where machine learning models are used to predict the success of a harvest in vertical farming based on various environmental and plant-related data, falls under the broader umbrella of Artificial Intelligence (AI).