Intelligent Hydroponics System using Deep Neural Networks
Agriculture has the significant impact on the economy of the country. Hydroponics is a method of growing plants in a water based, nutrient rich solution. It is a modern farming technique where plants can be grown without the need of soil by means of nutrient solution. Hydroponics does not use soil,
2025-06-28 16:27:59 - Adil Khan
Intelligent Hydroponics System using Deep Neural Networks
Project Area of Specialization Electrical/Electronic EngineeringProject SummaryAgriculture has the significant impact on the economy of the country. Hydroponics is a method of growing plants in a water based, nutrient rich solution. It is a modern farming technique where plants can be grown without the need of soil by means of nutrient solution. Hydroponics does not use soil, instead it uses root system which is supported by certain medium such as perlite, rock wool, clay pellets or vermiculite. Automated systems are used for changing water and providing fertilizer. Easy and smart approach, wherein automation processes using machine learning in which different types of algorithms are used in order to estimate and predict accurate values of parameters involved during growth process of are used crops. This will enhance hydroponics farming and also it is an easy and smart technique to maximize yield. To control the hydroponic plant growth we will use machine learning algorithm Like Neural Network. Internet of Things allows for Machine to Machine Interaction and controlling the hydroponic system autonomously and intelligently. This work proposes to develop an intelligent IoT based hydroponic system by employing Deep Neural Networks. This system so developed is intelligent enough in providing the appropriate control action for the hydroponic environment based on multiple input parameters (gathered). In addition to automating the hydroponics by employing IoT technology, there is a need for some intelligence for controlling the hydroponics system. So, towards this machine learning which is a subset of Artificial Intelligence is used in automating the plant growth.
Project ObjectivesThe project objectives are as follows:
- To improve agriculture system by implementing an intelligent and automated IoT based hydroponics system.
- To maintain the nutrients, water level and pH level.
- To design water level and nutrients controller.
- To develop a system by using Deep Neural Network model towards providing appropriate control action to hydroponics system in real time with higher accuracy.
- Implementation of Deep Neural Network at the cloud towards the classification of control action based on parameters collected from hydroponics system.
- To make efficient use of land and water by implementing an intelligent and automated IoT based hydroponics System.
- Integrate optimized components into an existing design of Hydroponic System.
The project will be implement in the following phases.
- The system captures the parameters (pH, Water level, Humidity/ Temperature, Light Intensity, EC) using the appropriate sensors.
- The control action towards the Hydroponics system is done through controller i.e. Arduino and Raspberry Pi which is processed to work on predicting algorithm.
- The fitting model in Pi3 would make an intelligent decision in giving the output decision which is further sent to Arduino in activating the appropriate control system.
- We use Air Pump for controlling the flow of oxygen. The data received by Pi3 sent to Cloud for storage and viewing and then given to the user.
This Project can bring the following benefits to the agriculture industry.
- The ability to produce higher yields than traditional, soil-based agriculture to meet the increasing needs of ever growing population.
- Allowing food to be grown and consumed in areas of the world that cannot support crops in the soil either due to non availability of soil based land or due to shortage of water.
- This work can also be implemented in rooftop gardens and made as a house hold activity so that everyone can involve in farming and get benefit out of it. Also the need of pest control is less as compared to outdoor farming and the food grown is organic.
The project will have following technical deliverable.
- A vertical Hydroponics system in which one layer cascaded over other.
- A fully automatic Hydroponics system controlled by IOT
- An Intelligent Hydroponics using Deep neural networks.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 75580 | |||
| Raspberry pi 4 | Equipment | 2 | 22000 | 44000 |
| Arduino Mega2560 | Equipment | 2 | 2200 | 4400 |
| Arduino Uno | Equipment | 2 | 1000 | 2000 |
| pH Sensor | Equipment | 1 | 6300 | 6300 |
| TDS Module | Equipment | 1 | 2500 | 2500 |
| RGB LED | Equipment | 5 | 10 | 50 |
| Rpi casing | Equipment | 1 | 550 | 550 |
| Heat Sink | Equipment | 1 | 250 | 250 |
| SD Card | Equipment | 1 | 850 | 850 |
| Rpi Charger | Equipment | 1 | 550 | 550 |
| HDMI Cable | Equipment | 1 | 550 | 550 |
| Arduino Cable | Equipment | 4 | 100 | 400 |
| Jumper Wires Bundle F/M | Equipment | 1 | 30 | 30 |
| Jumper Wires Bundle M/M | Equipment | 2 | 180 | 360 |
| Jumper Wires | Equipment | 5 | 100 | 500 |
| Bread Board | Equipment | 3 | 150 | 450 |
| Resistor | Equipment | 20 | 5 | 100 |
| 10K Potentiometer | Equipment | 2 | 25 | 50 |
| LDR | Equipment | 1 | 20 | 20 |
| Fan | Equipment | 2 | 330 | 660 |
| LCD 16x4 | Equipment | 1 | 750 | 750 |
| LCD 16x2 | Equipment | 1 | 300 | 300 |
| ESP-01 | Equipment | 2 | 300 | 600 |
| Water Level Sensor | Equipment | 2 | 150 | 300 |
| Ultrasonic Sensor | Equipment | 1 | 180 | 180 |
| DC Motor | Equipment | 2 | 100 | 200 |
| DHT11 Sensor | Equipment | 2 | 250 | 500 |
| Water Container | Equipment | 1 | 600 | 600 |
| Water Pump | Equipment | 1 | 440 | 440 |
| Buzzer | Equipment | 1 | 30 | 30 |
| Capacitor | Equipment | 4 | 5 | 20 |
| Inductor | Equipment | 3 | 30 | 90 |
| Printing, Etching, Binding | Miscellaneous | 10 | 700 | 7000 |