Cropotronics
The project is named Cropotronics, it is a SOLAR POWERED SMART AQUAPONICS SYSTEM, aquaponics combines aquaculture ?fish farming, and hydroponics ?growing plants in a soilless medium (Figure 1), in an aim to provide an alternative to food production on an industrial, as well as, private scale. The pr
2025-06-28 16:31:00 - Adil Khan
Cropotronics
Project Area of Specialization Internet of ThingsProject SummaryThe project is named Cropotronics, it is a SOLAR POWERED SMART AQUAPONICS SYSTEM, aquaponics combines aquaculture –fish farming, and hydroponics –growing plants in a soilless medium (Figure 1), in an aim to provide an alternative to food production on an industrial, as well as, private scale. The project has been designed by considering urban areas with low agricultural-land/per capita due to over-population and urbanization.
Once fully functional, the project will require minimal human interference during the cultivation process with an increase in food output compared to traditional farming technique. The final product will be organic – free from contaminants such as pesticides – and cheaper than traditionally grown crops. The system will also provide user with useful suggestions regarding the next cultivation process and the feasibility of current crop.
A part from all this the project is self-sustainable and runs completely on solar power. The solar energy is harnessed using solar panels, solar chargers and batteries. The fishes used in this project are also readily available in the market and are fed purely organic food.
Project Objectives Objectives (Less than 2500 characters)- To design efficient systems in terms of water usage, and a higher crop yield.
- To design a sensor mesh, generic in nature; so that it may be used in any other related art.
- To develop power efficient and eco-friendly prototypes.
- To make the system solar powered.
- To increase awareness relating to the benefits of aquaponics.
- To install portable, lightweight and sturdy systems; compatible for use in urban environment.
The block diagram below (Figure 2) describes the functionality of the system and interaction of different modules to get the task done.
Figure 2: System Block Diagram
The block diagram, depicts the proposed system. Starting from aqua-culture (1), where fishes are fed, and their waste is deposited at bottom of the tank. The waste water from the aqua-culture is pumped out using a motor (2).
The water reaches plants (3), is checked for its pH, Electrical Conductivity, Total Dissolved Solids and temperature readings. This is done at (4) using the sensor mesh, which communicates with the main server. The main server (5) collects the data from the mesh, and runs it through the Machine Learning algorithm (6) for monitoring purposes and provide valuable feedback to the user about the feasibility of the current crop in the given conditions.
The water travels through (7), where plants are grown inside a PVC structure. There ammonia in the water is reduced to nitrates, absorbed by the plants. The purified water is fed back to the aquaculture, as it is now harmless to the fish.
Benefits of the Project Benefits (Less than 2500 characters)•Less land and water required
•Eco-friendly, oxygen is the by-product
•Home grown vegetables, no pesticides
•Larger yield per area
•Cheap alternative to market products (tomatoes, cucumbers, bell peppers)
•Locality
•Self Sufficiency in Cities
Technical Details of Final Deliverable PVC Structure:
The top and side views show the dimensions of the final structure constructed (Figures 3 and 4). The system has 2 layers with 3 rows each. Each row can have up to 7 net-pots to hold the crops, totaling to 42 plants each. The layers are high enough for a person to place or remove the net-pots easily, leaving enough room for a small fish tank beneath.
Figure 5: Functional Structure
The actual implementation of the system can be seen in Figure 5. The final implementation is of light weight PVC pipes and a solar panel powering the system and storing excess power in the battery.
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A wheat plant growing in the net-pot placed in the system in Figure 6. The plant has its root submerged in water containing fish waste. This water is pumped from the aquarium using a series dc motor water pump.
The aquarium (Figure 7) houses a total of 4 fish, which have the potential for growth. Alongside it is the battery. Both of these are placed under the solar panels to protect them from rain and direct sunlight.
Sensors:|
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The inside and outside view of the sensor mesh can be seen in the 3D model in Figure 8 A & B, the working of sensor mesh can be understood from the flow chart below.
The sensors are interfaced with the Micro Controller Unit which communicates with the main server using BLE and sends sensor values (of the respective sensors shown below) which are stored on My SQL server and also used in ML algorithm to provide with a prediction (Figure 9).
The figure 10, below shows the specific details of the sensors used, i.e. their accuracy, working conditions, limitations etc.
As already mentioned above these sensors are interfaced with the microcontroller, which operates as a peripheral device. The Micro controller used here is Red bear’s BLE NANO.
Figure 11: Redbear BLE Nano v2
It has 6 ADC and 12 Digital I/O. It is BLE enabled and has a flash of 512 KB. It work on input voltages ranging from 3.3V to 13V. The best part is, it is of the size of a penny and can easily be fitted into our sensor mesh.
Figure 12: Data Flow Block Diagram
Central Device:The above mentioned microcontroller will be communicating with the main server (central device) which is based on Raspberry Pi 3b+.
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Figure 13: A&B Server Block Diagram & Raspberry Pi 3B+
The main server based on Raspberry Pi uses BLE for communication, it receives data through UUIDs of TX and RX, and uploads data to a MySQL database for storage. It also runs the data through a pre-trained Machine Algorithm.
Figure 14: Curve Fitting Block Diagram
The collected data of the sensors is stored in an XLS file. The data points of each sensor, are extracted and saved in a numpy-array (array like structures in python). Curve fitting is applied to each attribute, i.e. we will have a curve for pH, EC, temp etc.
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