This paper describes a new IoT-based system that can keep an eye on the weather in real time. The system uses the
internet to connect many sensors so that it can give you constant weather updates. All of the data that is collected is sent to the
cloud, where users can get to it from anywhere.
This makes it easier to keep an eye on the weather, especially in rural or agricultural areas. When the weather is bad, traditional
wired and analog devices often don't work. The system uses wireless sensors to collect data reliably to fix this problem. These
sensors keep track of important things like light, temperature, humidity, and rainfall. The wireless setup makes monitoring
easier and more accurate by cutting down on manual work.
The system is very helpful for farmers who need to know about the weather right away. It also backs ideas like vertical farming,
which is when crops are grown in small spaces. Keeping an eye on the environment all the time helps keep farming conditions
perfect. The answer is cheap, easy to use, and works well in rural areas. It helps people make better choices by giving them real
time data.
In general, the IoT-enabled approach makes it easier to plan for farming and keep an eye on the weather. It shows a modern and
effective way to watch the environment.
[1] ahman A.-u., Abbas S., Gollapalli M., Ahmed R., Aftab S., Ahmad M., Khan M. A. & Mosavi A. 2022,
Rainfall Prediction System Using Machine Learning Fusion for Smart Cities. In Sensors, 22(9):3504. MDPI. A framework
integrating ML models for real-time rainfall forecasting using sensor data.
[2] J. Nithyashri, R. K. Poluru, S. Balakrishnan, M. A. Kumar, P. Prabu & amp; S. Nandhini 2023, IoT based prediction of
rainfall forecast in coastal regions using deep reinforcement model. In Measurement: Sensors,100877.
[3] I. Jagadeesan & amp; R. Nagarajan 2025, An IoT-Driven Federated Learning Method for Rainfall Prediction Employing A
CRNN GJO. In Engineering, Technology & Applied Science Research (ETASR).
[4] V. N. Kukre, S. Pawar& S. Tapse 2025, Integrated IoT Based Weather Monitoring and Machine Learning Weather
Prediction System. In IJRASET Journal for Research in Applied Science and Engineering Technology
[5] Integration of IoT-AI Powered Local Weather Forecasting” arXiv preprint 2025. Framework for integrating low-cost IoT
and AI for local weather and rainfall forecasting .
[6] D. D. Pandya, S. Degadwala, D. Vyas, S. V. Sureshbhai, L. Ainapurapu and N. S. Bhavsar, “Advancing Erythemato
Squamous Disease Classification with Multi-class Machine Learning”; 2023 7th International 8 Conference on I-SMAC (IoT in
Social,
Mobile, Analytics and Cloud) (I-SMAC), Kirtipur, Nepal, 2023, pp. 542-547, doi: 10.1109/I
SMAC58438.2023.10290599.
[7] D. D. Pandya, P. A. Patel, H. H. Patel, A. J. Goswami, S. Degadwala and D. Vyas, Unveiling the Power of Collective
Intelligence: A Voting-based Approach for Dementia Classification,& 7th International Conference on I-SMAC (IoT in Social,
Mobile, Analytics and Cloud) (I-SMAC), Kirtipur, Nepal, 2023, pp. 478-482, doi: 10.1109/I-SMAC58438.2023.10290165.
[8] Pandya, D.D., Jadeja, A., Trivedi, S., Patel, P.A., Tamhankar, I., Dhanesha, P.S. (2026).Internet of Things (IoT)’s
Transformative Power Enhancing Wireless Networks and Sensing. In: Rathore, V.S., Piuri, V., Babo, R., Karthik, S. (eds)
Universal Threats in Expert Applications and Solutions. UNI-TEAS 2025. Lecture Notes in Networks and Systems, vol 1452.
Springer, Singapore. https://doi.org/10.1007/978-981-96-7292-9_20
[9] Pandya, D.D., Jadeja, A., Trivedi, S., Patel, P.A., Tamhankar, I., Dhanesha, P.S. (2026). Internet of Things (IoT)’s
Transformative Power Enhancing Wireless Networks and Sensing. In: Rathore, V.S., Piuri, V., Babo, R., Karthik, S. (eds)
Universal Threats in Expert Applications and Solutions. UNI-TEAS 2025.
[10] Pandya, Darshanaben Dipakkumar, Borate Pooja Satish, and Khushbu. “Improving Data Mining Reliability Through
Applied C-B Techniques for Addressing Misplaced Values.” Universal Threats in Expert Applications and Solutions (UNI
TEAS 2025).
