Analysis and Prediction of Lettuce Crop Yield in Aeroponic Vertical Farming using Logistic Regression Method

Human population growth strains food supplies, resulting in more than one billion of the world’s 6.5 billion people facing hunger, which is the leading cause of death. In consequence, increasing crop production through traditional farming methods will not address the global food scarcity problem. As a precision agriculture approach, aeroponics, a soilless agriculture technique, can assist in cultivating in-demand food crops, which will help serve the people. Because aeroponics is an indoor farming technology, factors such as pH, EC, light, temperature, PPM, and turbidity will affect crop growth. Hence, monitoring an aeroponic crop growth system efficiently is crucial for achieving a high crop yield and reducing the need for food crops. Even though, many crop monitoring systems use IoT and machine learning algorithms, their prediction accuracy is not up to the level and if they are deployed in the real world, they may not meet customer expectations. In order to efficiently monitor crops, more advanced models are needed. However, understanding the large volume of data can be very tedious. Data visualization techniques can help make the distribution of the dataset easier to understand. Machine learning methods and packages can be used for environmental monitoring and visualization of the data where, the collection of crop growth dataset is carried out using various IoT sensors deployed in the growth environment. This study provides a comprehensive explanation of the data visualization techniques used to study the lettuce crop dataset and predict lettuce yield using the logistic regression machine learning method which is been implemented using Python coding language.