Precision Irrigation using IoT and Machine Learning for Drip Irrigation
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The document discusses precision irrigation, which provides the right amount of water to crops at the right time and place, primarily using drip irrigation systems. It highlights the advantages of drip irrigation, such as significant water savings, uniform water distribution, and suitability for various crops and soil types. Additionally, it explores the integration of machine learning and IoT technologies in precision irrigation, emphasizing their role in optimizing irrigation scheduling through data analysis and automation.
Precision Irrigation using IoT and Machine Learning for Drip Irrigation
- 1. Precision Irrigation using IoT and Machine Learning for Drip Irrigation Muhammad Aleem Siddiqui
- 2. What is Precision Irrigation Providing right amount of water, at the right place of field and at the right time, This technique is known as precision irrigation. This can be implemented using Variable Rate Irrigation (VRI) Method using Drips or Sprinklers. VRI technique has the capability to overcome Under- Irrigation and Over-Irrigation Problem by irrigating only that patch where the water is required.
- 3. What is Drip Irrigation? Drip Irrigation is the localized application of irrigating water to the crop. Drip irrigation systems save a lot of water. In drip irrigation systems, water is applied drop by drop through small holes called emitters. It can save up to 30-60% of water and can be used for almost all kind of orchards, row crops and vegetables.
- 4. Advantages of Drip Irrigation Saves 30-60% water Saves electricity and labor Uniform distribution of water Controlled application of soluble fertilizers Controls weeds and soil erosion Enable use of saline water
- 5. Parameters of Using Drip Irrigation Suitable crops Drip irrigation is most suitable for row crops (vegetables, soft fruit), tree and vine crops where one or more emitters can be provided for each plant. Generally, only high value crops are considered for drip irrigation because of the high capital costs of installing a drip system Suitable slopes Drip irrigation is adaptable to any farmable slope. Normally the crop is planted along contour lines and the water supply pipes are laid along the contour also. This is done to minimize changes in emitter discharge as a result of land elevation changes.
- 6. Parameters of Using Drip Irrigation Suitable soils Drip irrigation is suitable for most soils. On clay soils water must be applied slowly to avoid surface water ponding and runoff. On sandy soils higher emitter discharge rates will be needed to ensure adequate lateral wetting of the soil. Suitable irrigation water One of the main problems with drip irrigation is blockage of the emitters. All emitters have very small waterways ranging from 0.2-2.0mm in diameter and these can become blocked if the water is not clean. Thus it is essential for irrigation water to be free of sediments.
- 7. Drip System Layout A typical drip irrigation system consists of the following components: 1. Pump unit 2. Control head 3. Main and sub main lines (PVC) 4. Laterals (PVC) 5. Emitters or drippers
- 8. Using Machine Learning Precision Irrigation After reading few research papers, I have concluded that, Machine Learning can be used in Precision Irrigation to make scheduling pattern for irrigation system, It can predict, when and how much amount of water is required to a specific field or a specific patch of land. Regression Algorithm can able to solve to solve the problem, if we have few parameters i.e. Time_Log, Pump_Status, Soil_Moisture, Water_Flow_Rate. Change_in_Soil_Moisture. A regression algorithm can able to predict the scheduling pattern for a specific field.
- 9. Using Convolutional Neural Network for Precision Irrigation After reading a research paper, “Towards Automating Precision Irrigation: Deep Learning to Infer Local Soil Moisture Conditions from Synthetic Aerial Agricultural Images”, I have concluded that, A CNN can be used to predict the soil moisture level from aerial images captured by a UAV. The predicted data can be used to make the scheduling pattern for irrigating individual patch field due to its variable set of features.
- 10. Using Internet of Things in Precision Irrigation After reading a research papers titled as, “IoT-based Drip Irrigation Monitoring and Controlling System using NodeMCU and Raspberry Pi”, I have concluded that, A wireless sensor network can be deployed to automate the water scheduling in drip irrigation using IoT Nodes containing various environment sensors and central controlling computer.
- 11. Block Diagram For IOT Node : Solar Power Source Relay Water PumpWater Tank FIELD Soil Moisture Sensor Water Flow Sensor & Electric Solenoid Valve Humidity & Temperature Sensor RF24L01 Base-Station RF24L01 Water Flow Sensor & Electric Solenoid Valve Using Internet of Things in Precision Irrigation Cont.
- 12. Block Diagram For Water Reservoir : Solar Power Source Relay Water PumpWater Reservoir Water Tank Water Level Detection Sensor Water Flow SensorpH Value Sensor Field RF24L01 RF24L01 Base-Station Using Internet of Things in Precision Irrigation
- 13. Using Internet of Things in Precision Irrigation Block Diagram For Base Station: Raspberry Pi Base Station MySQL based Local DB Battery Wifi NRF24L01 Google Firebase Apache Server Visual Dashboard Node_1 Node_n Water_Tank
- 14. RESEARCH PAPERS 1. Olutobi Adeyemi, Ivan Grove and Sven Peets, “Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling”, in Sensors, 2018 2. D. Tseng, D. Wang and C. Chen, “Towards Automating Precision Irrigation: Deep Learning to Infer Local Soil Moisture Conditions from Synthetic Aerial Agricultural Images”, in 14th International Conference on Automation Science and Engineering (CASE), IEEE, 2018 3. R. Berenstein, R. Fox, S. McKinley, S. Carpin, and K. Goldberg, “Robustly adjusting indoor drip irrigation emitters with the toyota hsr robot”, in ICRA, IEEE, 2018 4. Nyoman K. Wardana, Padma Nyoman Crisnapati and Komang Agus Ady Aryanto, “IoT-based Drip Irrigation Monitoring and Controlling System using NodeMCU and Raspberry Pi”, in ICST, 2018