Traditional agriculture is inefficient since growing a vegetable takes a lot of variables to be perfect, such as weather, temperature, nutrient levels, and so on. As a result, resources and money are wasted. Using the hydroponic system in a controlled environment, such as a greenhouse, allows all of the above variables to be regulated to enhance plant growth.
Another issue with agriculture is that it necessitates the ability and understanding of crop cultivation. Even when using a hydroponic system, knowledge and time are required. Human laborers are necessary to check the nutritional value of the vegetables.
The end goal of this project is to create a functioning hydroponic system with a minimal amount of human intervention to grow plants, in this case, green lettuce. With the help of AI, calculate and predict the ideal number of variables necessary to grow green lettuce under certain conditions from the data collected by the sensors.
This project would also provide researchers with data from the sensors that might be used in the future for future research or in related fields. With the optimization of the hydroponic system, plenty of resources won’t be wasted. If we could make the hydroponic system cheap and readily available, we could potentially mass produce and distribute it across the world.
Summary and Accomplishments
The basic parts of the hydroponic system have been built to carry nutrients to the plants located inside a growing container using water that is pumped from a reservoir using a water pump. Setting up a pH sensor, an EC sensor, and a camera with a Raspberry Pi to be used for monitoring and collecting data from the plants grown in the hydroponic system
The lettuce plants have been grown and harvested, ready for consumption, with all the necessary data collected from the sensors to train a machine learning model to predict the growth rate of the lettuce based on the variables. Machine learning models that can accurately predict the growth rate if all the necessary data are inserted.