IoT-Based Precision Irrigation System for Real-time Calculation of VPD, ETo and Crop Water Requirements in Sustainable Agriculture

This work develops an IoT-based precision irrigation platform for papaya (Carica papaya L., ‘Holland’ variety) in Eastern Thailand where coastal influences and heterogeneous canopies create strong microclimatic gradients. In-orchard sensors (air temperature, relative humidity, solar radiation, wind, rainfall) stream data via MQTT to a backend that computes vapor pressure deficit (V PD), reference evapotranspiration (ETo, FAO Penman–Monteith) and crop evapotranspiration $\left({E{T_c} = {K_c}\widehat {E{T_o}}}\right)$ using a phenology-based constants Kc schedule. A rainfall-effectiveness rule Peff = min{αP, ETc} prevents over-crediting intense showers. A web application converts depth (mm) to liters per tree and runtime given the emitter configuration and provides operational guidance. A case example from Chanthaburi illustrates the end-to-end workflow such as $\widehat {E{T_o}} = 7.025{\text{mm}}$, Kc = 1.72 (Month 6) is ETc = 12.083 mm day−1 with daily rainfall of 3.00 mm and canopy radius r = 1 m (Acanopy = 3.1416 m2), the net irrigation requirement is (12.083 − 3.00) × 3.1416 = 28.54 L tree−1 day−1. The architecture and examples demonstrate an actionable IoT-to-decision pipeline that increases fidelity to orchard microclimate, anchors recommendations in stage-specific papaya physiology and delivers user-facing set points that are simple to apply in the field.

• Publication year

  2025

• Venue

  2025 8th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

• Publication date

  2025-12-11

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/ISRITI68345.2025.11393349
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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