Poljoprivreda, Vol. 32 No. 1, 2026.
Izvorni znanstveni članak
https://doi.org/10.18047/poljo.32.1.1
Comparison of Various Water-Stress Monitoring Methods in Soybean (Glycine max (L.) Merr.)
Stela Rotim
orcid.org/0009-0006-1456-2313
; Srednja škola Ilok, Matije Gupca 168, 32236, Ilok, Hrvatska
*
Monika Marković
orcid.org/0000-0002-8742-6922
; Sveučilište Josipa Jurja Strossmayera u Osijeku Fakultet agrobiotehničkih znanosti Osijek, Vladimira Preloga 1, 31 000, Osijek, Hrvatska
Marija Spišić
; Sveučilište Josipa Jurja Strossmayera u Osijeku Fakultet agrobiotehničkih znanosti Osijek, Vladimira Preloga 1, 31 000, Osijek, Hrvatska
Nina Cvenić
; Poljoprivredni institut Osijek, Južno predgrađe 17, 31000, Osijek, Hrvatska.
Maja Matoša Kočar
; Poljoprivredni institut Osijek, Južno predgrađe 17, 31000, Osijek, Hrvatska.
Tihana Marček
orcid.org/0000-0002-8617-5009
; Sveučilište Josipa Jurja Strossmayera u Osijeku, Prehrambeno tehnološki fakultet h znanosti Osijek, Franje Kuhača 18, Osijek, Hrvatska
Josip Spišić
; Sveučilište Josipa Jurja Strossmayera u Osijeku, Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek Kneza Trpimira 2B, HR-31000 Osijek, HrvatskaOsijek, Hrvatska,
* Dopisni autor.
Sažetak
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.
Ključne riječi
soybean; water stress; soil moisture sensors; physiological indicators; machine learning models
Hrčak ID:
348725
URI
Datum izdavanja:
30.6.2026.
Posjeta: 0 *