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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM43876
Volume:
9 |
4 |
Apr 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Small farmers usually have to guess when to water or harvest, mostly because professional sensors cost too much and are way too complicated. That’s where my project steps in. I built a Smart IoT Irrigation and Prediction System that’s affordable, tough enough for the field, and actually smart. Instead of filling a farm with expensive devices, I just used a single 30cm pipe, an ESP32, and an ultrasonic sensor to keep tabs on water levels all the time. Rural areas like Peddapally have another problem—spotty internet. So, I gave the system a backup plan. It saves every bit of data to an SD card, right there on the farm. If the Wi-Fi goes out for days, nothing gets lost. Once the internet’s back, everything syncs up automatically to Google Sheets. But the real magic? That’s in the system’s brain. I went way past basic calculations. I trained Deep Learning models—LSTM and 1D-CNN. With these two models working together, the system tells farmers what they really need: how many days until harvest, and how many tons they can expect. And I know most farmers don’t want to stare at a screen all day, so I added a bilingual voice interface. The system actually talks, giving updates in both English and Telugu. With 96% accuracy, this project proves world-class tech doesn’t have to come with a huge price tag.
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Publication Date
2/19/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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