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AI Integrated Smart Irrigation and Plant Disease Detection using Raspberry Pi Camera
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 103 |
| Year of Publication: 2026 |
| Authors: Ashwini Vilas Waghmare, Y.S. Angal |
10.5120/ijca71bb0ff3c209
|
Ashwini Vilas Waghmare, Y.S. Angal . AI Integrated Smart Irrigation and Plant Disease Detection using Raspberry Pi Camera. International Journal of Computer Applications. 187, 103 ( May 2026), 39-47. DOI=10.5120/ijca71bb0ff3c209
Abstract
Water is very important in farming because it is becoming scarcer. This study suggests a Smart Irrigation System that is powered by AI. This system uses Artificial Intelligence, the Internet of Things, and satellite technology to monitor farming activities and resources. This system will use many distributed IoT devices that will monitor the various soil and environmental conditions. DHT11 will monitor the moisture and temperature, and other sensors will monitor rain, NPK nutrients, and PIR. These sensors will use LoRa technology to cover large fields and monitor soil and environmental conditions. These sensors will be used to relay data, stream sensor data, and process data in real time. This data will be used to capture and analyze satellite and camera imagery. This monitoring will help to pinpoint and analyze stress, diseased crops, pests, animals, and other problems. Seasonal and predictive machine learning models will analyze weather data to create irrigation schedules. Smart Irrigation uses sensors to monitor the irrigation levels of crops and in turns manage the irrigation system. Energy and water diversion will be controlled by the smart irrigation system to conserve resources. Farmers can get alerts and system controls through a web-based system. They will have access to real-time data, as well as historical and predictive reports. Experimental assessment shows better irrigation and water savings as well as additional crop yields over the standard method. The proposed framework provides a scalable, affordable, and climate-smart solution for smart farming, aiding in data-driven agricultural management, and promoting sustainability in the long term.
References
- KS, Syam Kishor, and Binson VA. "An IoT-enabled real-time crop prediction system using soil fertility analysis." Eng 5.4 (2024): 2496-2510.
- Durai, Senthil Kumar Swami, and Mary Divya Shamili. "Smart farming using machine learning and deep learning techniques." Decision Analytics Journal 3 (2022): 100041
- Sarangi, Ashutosh, et al. "Enhancing Process Control in Agriculture: Leveraging Machine Learning for Soil Fertility Assessment." Engineering Proceedings 67.1 (2024): 31.
- Afzal, Muhammad, et al. "IoT-enabled adaptive watering system with ARIMA-based soil moisture prediction for smart agriculture." IEEE Access (2025).
- Laskar, Ahad Ahmed, et al. "Exploring the Role of Smart Systems in Farm Machinery for Soil Fertility and Crop Productivity." Int. J. Res. Appl. Sci. Eng. Technol 12.12 (2024): 2063-2075.
- Khaliq, Ayesha, et al. "Ai-driven smart agriculture: An integrated approach for soil analysis, irrigation, and crop-fertilizer recommendations." IEEE Access (2025).
- Luo, Xu, et al. "AIoT-Enabled Data Management for Smart Agriculture: A Comprehensive Review on Emerging Technologies." IEEE Access (2025).
- Dutta, Monica, et al. "Internet of Things-based smart precision farming in soilless agriculture: Opportunities and challenges for global food security." IEEE Access (2025).
- Aldhahri, Eman Ali, et al. "Smart Farming: Enhancing Urban Agriculture through Predictive Analytics and Resource Optimization." IEEE Access (2025).
- Gunasekaran, Kanimozhi, Karmel A, and Pemmareddy Sreevardhan. "Real-time soil fertility analysis, crop prediction, and insights using machine learning and deep learning algorithms." Frontiers in Soil Science 5 (2025): 165205
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