Lettuce Crop Yield Prediction Analysis using Random Forest Regression Machine Learning Model in Aeroponics System

Aeroponics is a type of soilless agriculture where plants are grown in a misted environment without the use of the soil. The process of yield prediction in aeroponics system for the lettuce crop is one of the key challenges because the crop yield is affected by variety of growth factors such as temperature, humidity, light and nutrient levels. Hence, to predict the lettuce crop yield in aeroponics, the machine learning models were utilized and these models can be trained on the historical data on crop yields and environmental variables. Here, in this work, the Random Forest (RF) regression is chosen as the machine learning model for yield prediction. The data used in this work were collected from the aeroponic lettuce farming tower. The purpose of the research work is to investigate the potential of the random forest model for efficient prediction of the lettuce crop yield in aeroponics system. The RF model can produce accurate prediction results of 92% which is better than the other regression algorithms in terms of forecasting lettuce crop production. Comparing this to the MAE of other regression models, which may be anywhere from 0.098 to 3.76 of the average reported yield, and it is observed that RF has a much more acceptable MAE. Hence, this work provides valuable information for farmers who are interested in utilizing this type of indoor farming to produce the high-yield of lettuce crops.