![Reference
1] An intelligent water management system based on wireless sensor networks for agriculture,
Li, S.; Xu, W.; Tao, H., Agricultural Water Management, 2018.
[2] Optimal Irrigation Management Model Based on Artificial Intelligence Algorithm in
Greenhouse, Wu, Y.; Zhou, Z.; Yang, W., Computers and Electronics in Agriculture, 2020.
[3] Optimization of irrigation water management using artificial intelligence techniques in arid
regions: A review, Rahman, M. M.; Hasan, M. K., Environmental Monitoring and Assessment,
2021.
[4] An intelligent decision support system for irrigation management, Zhang, J.; Chen, Q.; Kang,
Y., Water Resources Management, 2019.
[5] Precision agriculture technologies for irrigation management: a review, Geng, X.; Zhou, Y.;
Chen, J., Precision Agriculture, 2018.](https://image.slidesharecdn.com/finalyearprojectreview-iipptnew15-250429050326-c7a253a4/85/Final-Year-Project-Review-II-PPT-new-1-5-ppt-14-320.jpg)
Final Year Project Review -II PPT new (1) (5).ppt
- 1. AI-Driven Solution for Precise Water Management Final Year Project Review Presented By Siranjeevini S 912220106012 Rajabdullah M 912220106306 Vishwa R 912220106309 Under Guidance of Mr.R.Ramesh kumar.AP/ECE Associate Professor Department of Electronics and Communication Engineering , Solamalai College of Engineering -Madurai
- 2. Outline • Nowadays, population growth has led to water scarcity in most parts of the world. • A large amount of water is wasted in agriculture. This project proposes an automatic plant watering system that uses an ARM controller, temperature, humidity, Soil moisture sensor and DC pump respectively. • It automatically detects soil moisture and decides if watering is needed. The automatic watering system, a soil moisture sensor, checks the moisture level in the soil, and if the moisture level is low, the ARM turns on a water pump to provide the water. • The water pump automatically shuts off when the system detects sufficient moisture in the soil. • Whenever the system turns the pump on or off, the status of the water pump, temperature, humidity and soil moisture is updated. • This system is fully automated and does not require human intervention. This system is useful for gardens, Farming, etc.
- 3. Introduction • We use a lot of water for agriculture. In most cases, this water is used inefficiently, and a significant amount of water is wasted. • Introduce modern irrigation methods that are simple, easy to use, and increase water use efficiency. • The automatic Water Controlling System is the most efficient technique of supplying water into the land. • The most significant advantage is that the water is supplied to the root zone in a drop-by-drop manner, thereby saving a huge amount of water. • Farmers in India are now using manual irrigation techniques. This process sometimes requires more water, and sometimes the water is late, which causes the crops to dry out
- 4. Literature Survey S.No Journal Title Year Observation 1 IEEE Sensors Journal Integrating Arduino-Based Soil Moisture Sensing Systems with Artificial Intelligence for Precision Irrigation in Agriculture 2020 This paper explores the integration of Arduino-based soil moisture sensing systems with artificial intelligence algorithms for precision irrigation in agriculture. It discusses how AI techniques can optimize irrigation schedules based on real-time soil moisture data obtained from sensors, thereby improving water management efficiency. 2 Computers and Electronics in Agriculture An Intelligent System for Real- Time Water Management using Arduino and Soil Moisture Sensors 2019 This paper presents an intelligent system that utilizes Arduino microcontrollers and soil moisture sensors for real-time water management. It discusses the implementation of AI algorithms to analyze soil moisture data and control the operation of a DC pump for precise irrigation, minimizing water wastage.
- 5. Literature Survey S.No Journal Title Year Observation 3 Journal of Water Resources Planning and Management Application of Arduino-Based Smart Irrigation Systems with Artificial Intelligence for Efficient Water Resource Management 2021 This paper examines the application of Arduino-based smart irrigation systems integrated with artificial intelligence for efficient water resource management. It discusses the development of algorithms that utilize soil moisture sensor data to dynamically adjust irrigation schedules, optimizing water usage while maintaining soil moisture levels. 4 Sensors Real-Time Water Management System using Arduino and IoT for Smart Agriculture 2018 This paper presents a real-time water management system for smart agriculture, incorporating Arduino microcontrollers and IoT technology. It discusses the implementation of AI algorithms to analyze soil moisture data and trigger actions such as activating the DC pump or providing alerts through the LCD display and buzzer, enabling precise water management.
- 6. Objective • To design an advanced field water management system for an irrigation. • To improve the plant growth and cultivation • To reduce the manual effort • To process the automatic water pump based on moisture
- 7. Proposed approach • This system investigated the characteristics of the Irrigation and select an appropriate irrigation system. Increase yields through an efficient irrigation system and significantly reduce water demand. • This automatic water controlling system has been designed by consisting temperature sensor, moisture sensor, humidity sensor respectively. • This system is used to continuously measure the moisture level of the soil. Here, if the soil moisture level is low, the ARM turns on the water pump to provide the plant with water. When the system detects enough moisture in the soil, the water pump will automatically shut off.
- 9. Hard Ware or Soft Ware Requried Hardware: ARM controller DHT sensor Soil Moisture Sensor DC pump LCD Software: Keil Embedded C
- 10. Flow Chart
- 11. Challenges Implementing an AI-driven solution for precise water management using Arduino, Soil Moisture Sensor, LCD, and a DC pump presents several significant challenges. Firstly, developing robust AI algorithms capable of accurately analyzing soil moisture data and making informed decisions for irrigation scheduling is complex. Ensuring these algorithms are both efficient and adaptable to varying environmental conditions is crucial for effective water management. Additionally, ensuring the accuracy of soil moisture sensors through calibration and accounting for factors such as sensor drift and soil variability poses a challenge. Integrating multiple hardware components and software modules, including the Arduino microcontroller, sensors, actuators, and AI algorithms, also requires careful consideration to ensure seamless communication and compatibility while optimizing system performance. Real-time processing of soil moisture data and making timely irrigation decisions present computational challenges, particularly in resource-constrained environments like microcontrollers. Balancing computational complexity with real-time responsiveness is crucial. Moreover, managing power efficiently, especially for systems deployed in remote or off-grid locations, is essential for prolonged operation. Adapting the system to handle diverse environmental conditions and unforeseen challenges is also critical for robust performance.
- 12. Out Put – Hard Ware / Video /Image
- 13. Future Work In future work, there are several avenues for advancing the development of AI-driven solutions for precise water management using ARM controller, DHT sensor, Soil Moisture Sensor, LCD and a DC pump. Firstly, researchers could focus on refining and enhancing AI algorithms to improve their accuracy, efficiency, and adaptability to varying environmental conditions. This could involve exploring advanced machine learning techniques such as deep learning and reinforcement learning. Additionally, advancements in sensor technology could be pursued to develop more accurate and reliable soil moisture sensors, with a particular emphasis on reducing sensor drift and improving compatibility with different soil types. Integration with IoT and cloud platforms could enable remote monitoring, data analytics, and predictive maintenance capabilities. Furthermore, expanding the application of the system to precision agriculture practices beyond basic irrigation, such as nutrient management and pest control, presents an exciting area for future research.
- 14. Reference 1] An intelligent water management system based on wireless sensor networks for agriculture, Li, S.; Xu, W.; Tao, H., Agricultural Water Management, 2018. [2] Optimal Irrigation Management Model Based on Artificial Intelligence Algorithm in Greenhouse, Wu, Y.; Zhou, Z.; Yang, W., Computers and Electronics in Agriculture, 2020. [3] Optimization of irrigation water management using artificial intelligence techniques in arid regions: A review, Rahman, M. M.; Hasan, M. K., Environmental Monitoring and Assessment, 2021. [4] An intelligent decision support system for irrigation management, Zhang, J.; Chen, Q.; Kang, Y., Water Resources Management, 2019. [5] Precision agriculture technologies for irrigation management: a review, Geng, X.; Zhou, Y.; Chen, J., Precision Agriculture, 2018.
- 15. THANK YOU