Smart Agriculture 4.0: Integrating IoT, AI, and Robotics for Precision Farming
Keywords:
Automated decision-making, IoT in agriculture, Precision agriculture, Remote sensing in agriculture, Smart farming, Smart irrigation systems, Sustainable farmingAbstract
This research introduces a smart way to monitor crop health using drones, IoT sensors, and machine learning. The goal is to help farmers detect crop problems early and use resources more efficiently. The system collects data in two ways: IoT sensors placed in the field measure temperature, humidity, and soil moisture in real-time, while drones take multispectral images of crops. These images help calculate a vegetation index (NDVI), which shows how healthy the plants are. A machine learning model then analyzes the data to determine crop health conditions. A major advantage of this approach is the combination of different data sources. IoT sensors provide continuous monitoring, while drone images offer detailed insights at specific times. The researchers also used statistical techniques to fill in missing data and remove errors from sensor readings. The results show that this method is highly accurate in identifying crop stress, allowing farmers to take action quickly before the plants are seriously affected. Machine learning plays a key role in improving decision-making for farm management. In the future, the study plans to add more details about soil conditions and fertilizer usage to further improve crop monitoring. This research helps move farming toward a more automated, efficient, and smart system, making agriculture more productive and sustainable.
References
M. Ayaz, M. Ammad-uddin, Z. Sharif, A. Mansour, and el-Hadi M. Aggoune, “Internet-of-Things (IoT) based Smart Agriculture: Towards Making the Fields Talk,” IEEE Access, vol. 7, pp. 1–1, 2019, doi: https://doi.org/10.1109/access.2019.2932609.
J. Chen and A. Yang, “Intelligent Agriculture and its Key Technologies Based on Internet of Things Architecture,” IEEE Access, vol. 7, pp. 77134–77141, 2019, doi: https://doi.org/10.1109/access.2019.2921391.
M. S. Farooq, S. Riaz, A. Abid, T. Umer, and Y. B. Zikria, “Role of IoT Technology in Agriculture: a Systematic Literature Review,” Electronics, vol. 9, no. 2, p. 319, Feb. 2020, doi: https://doi.org/10.3390/electronics9020319.
V. Saiz-Rubio and F. Rovira-Más, “From Smart Farming towards Agricul-ture 5.0: A Review on Crop Data Man-agement,” Agronomy, vol. 10, no. 2, p. 207, Feb. 2020. Available: https://www.mdpi.com/2073-4395/10/2/207.
U. Shafi et al., “A Multi-Modal Ap-proach for Crop Health Mapping Using Low Altitude Remote Sensing, Internet of Things (IoT) and Machine Learn-ing,” IEEE Access, vol. 8, pp. 112708–112724, 2020, doi: https://doi.org/10.1109/access.2020.3002948.
M. S. Farooq, S. Riaz, A. Abid, T. Umer, and Y. B. Zikria, “Role of IoT Technology in Agriculture: a Systematic Literature Review,” Electronics, vol. 9, no. 2, p. 319, Feb. 2020, doi: https://doi.org/10.3390/electronics9020319.
A. Kamilaris, A. Fonts, and F. X. Prenafeta-Boldύ, “The rise of block-chain technology in agriculture and food supply chains,” Trends in Food Science & Technology, vol. 91, no. 1, pp. 640–652, Sep. 2019, doi: https://doi.org/10.1016/j.tifs.2019.07.034.
M. Gupta, M. Abdelsalam, S. Khorsan-droo, and S. Mittal, “Security and Pri-vacy in Smart Farming: Challenges and Opportunities,” IEEE Access, vol. 8, pp. 1–1, 2020, doi: https://doi.org/10.1109/access.2020.2975142.
V. P. Kour and S. Arora, “Recent De-velopments of the Internet of Things in Agriculture: A Survey,” IEEE Access, vol. 8, pp. 129924–129957, 2020, doi: https://doi.org/10.1109/access.2020.3009298.
M. A. Ferrag, L. Shu, X. Yang, A. Derhab, and L. Maglaras, “Security and Privacy for Green IoT-based Agriculture: Review, Blockchain solutions, and Challenges,” IEEE Access, pp. 1–1, 2020, doi: https://doi.org/10.1109/access.2020.2973178.
