Agriculture, Vol. 32 No. 1, 2026.
Original scientific paper
https://doi.org/10.18047/poljo.32.1.1
Comparison of Various Water-Stress Monitoring Methods in Soybean (Glycine max (L.) Merr.)
Stela Rotim
; Ilok High School, Matije Gupca 168, 32236, Ilok, Croatia
*
Monika Marković
; Josip Juraj Strossmayer University of Osijek, Faculty of Agrobiotechnical Sciences Osijek, Vladimira Preloga 1, 31 000, Osijek, Croatia
Marija Spišić
; Josip Juraj Strossmayer University of Osijek, Faculty of Agrobiotechnical Sciences Osijek, Vladimira Preloga 1, 31 000, Osijek, Croatia
Nina Cvenić
; Agricultural Institute Osijek, Južno predgrađe 17, 31000, Osijek, Croatia
Maja Matoša Kočar
; Agricultural Institute Osijek, Južno predgrađe 17, 31000, Osijek, Croatia
Tihana Marček
; Josip Juraj Strossmayer University of Osijek, Faculty of Food Technology Osijek, Franje Kuhača 18, 31 000 Osijek, Croatia
Josip Spišić
; Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Kneza Trpimira 2B, 31 000 Osijek, Croatia
* Corresponding author.
Abstract
The research was conducted in a greenhouse at the Agricultural Institute Osijek in 2022. The study aimed to evaluate and compare different methods for detecting soil water deficit and plant water‐stress responses in soybean (Glycine max (L.) Merr.).
The evaluated methods included soil water content sensors, physiological parameters, and machine learning models. Two irrigation treatments were implemented: optimal soil water content (control treatment, n1, 100 % of field capacity, FC) and water stress (n2, 50% FC) applied during the flowering (f1) and grain-filling (f2) stages. TDR300 and AT sensors exhibited the most rapid response to changes in soil water content (% vol.), indicating high sensitivity to early water deficit. Among physiological indicators, LRCC and RC/CS were the most sensitive during flowering, while PIABS and PITOTAL showed the highest responsiveness during grain filling. The k-Nearest Neighbors (kNN) model demonstrated the highest performance, characterized by high classification accuracy (CA = 0.921) and AUC (0.976). The results emphasize the importance of selecting stage-specific indicators for water stress detection and provide a basis for the development of future integrative monitoring frameworks in soybean production.
Keywords
soybean; water stress; soil moisture sensors; physiological indicators; machine learning models
Hrčak ID:
348725
URI
Publication date:
30.6.2026.
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