Optimizing Variable-Rate Linear Move Irrigation Systems Using Edge-AI and Hydrological Modeling: A Case Study of the Danube-Tisa-Danube Canal Basin in Vojvodina (Aintelia Science Notes, 2024)
Vojvodina, Serbia's principal agricultural region, faces escalating irrigation demand driven by climate change-induced summer droughts. The 929 km Danube-Tisa-Danube (DTD) canal network, supplying up to 510,000 ha of arable land, is under pressure to improve water allocation precision. This paper evaluates an integrated optimization framework for variable-rate irrigation (VRI) linear move systems supplied by the DTD network, combining three components: (i)A SWAT+ hydrological model of the DTD basin calibrated on 2011–2022 discharge and water-level records. (ii) A field-scale, LSTM-based soil moisture and reference evapotranspiration ($ET_0$) prediction module driven by IoT sensors and near-real-time weather data. (iii)An Edge-AI prescription engine deployed on an NVIDIA Jetson Nano unit mounted on the irrigation machine. In validation, the SWAT+ model achieved a Nash-Sutcliffe Efficiency (NSE) of 0.81 and $R^2$ of 0.87. The LSTM module predicted 48-hour soil moisture with an RMSE of 0.028 $m^3/m^3$. Field trials on maize (Zea mays L.) and sunflower (Helianthus annuus L.) across four sites in Bačka demonstrated a 22.4% reduction in seasonal water application compared to uniform-rate management. Meanwhile, grain yields improved (maize +3.1%, sunflower +1.8%), confirming the operational viability of data-driven VRI optimization.