10/09/2026
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𝐑𝐎𝐁𝐎𝐋𝐔𝐓𝐈𝐎𝐍 𝐑𝐄𝐆𝐈𝐎𝐍𝐀𝐋 – 𝐈𝐋𝐎𝐂𝐎𝐒 𝐑𝐄𝐆𝐈𝐎𝐍 𝟐𝟎𝟐𝟔 🔥
𝐅𝐫𝐞𝐞𝐬𝐭𝐲𝐥𝐞 𝐂𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐨𝐧 (𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 & 𝐈𝐧𝐯𝐞𝐧𝐭𝐢𝐨𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬)
PADANUM - A Plant-Adaptive Data-Driven AI Network for Unified Moisture Management On Papaya Farming
Researchers: Guian Rexter M. Basuel, Zac Ezekiel N. Cabalo, and John Patrick C. Refugia
Research Adviser: Noli A. Esmeña
School: Tayug National High School
PADANUM, a Plant‑Adaptive Data‑Driven AI Network for Unified Moisture Management, was developed as a low‑cost ESP32‑based precision‑irrigation platform that integrates soil‑moisture and temperature sensing, reservoir monitoring, variable‑output pump control, a local web dashboard, irrigation‑cycle logging, Ridge Regression, and Dynamic Root‑Zone Irrigation Response Fingerprinting (DRIRF). The evaluation covered sensor/reference validation, web plotting and data‑integrity checks, irrigation‑response records, Ridge Regression at 20‑, 30‑, and 40‑cycle checkpoints, and 40 DRIRF fingerprint runs, with the verification dataset containing 80 sensor‑bearing records across 40 days and 40 irrigation cycles. Results showed 98.33% accuracy in temperature testing
against a 30 °C reference, 100% category agreement under Dry+ and Wet+ conditions, and 99.08% overall accuracy in reservoir checks at 1, 7, 15, and 30 L, while soil‑moisture and temperature plotting achieved 100% accuracy and completeness. At 40 cycles, the tuned Ridge model reached R² = 0.97, MAE = 0.27 pp, and RMSE = 0.37 pp, and across 40 DRIRF runs the system demonstrated a mean response delay of 9.975 s, moisture gain of 4.857 pp, rise rate of 0.1024 pp/s, stabilization time of 57.975 s, and response efficiency of 5.261 pp/L. Pump calibration confirmed that the system consistently delivered predicted
water volumes relative to supply, validating its reliability. Overall, PADANUM proved that affordable ESP32 hardware combined with data‑driven algorithms can achieve highly accurate sensing, predictive irrigation analytics, and efficient water delivery for sustainable agriculture, with pump calibration refinement identified as the next improvement area.
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