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The study presents the results of a three-year experiment (2013–2015) that was carried out to determine a relationship between the soybean yield components and the seed yield under different irrigation treatments. The results indicated that the study year had the greatest effect on the number of nodes per plant (NNP), while an interaction between the irrigation and experiment year was also statistically significant. The highest average NNP was observed in 2015, being 33% higher when compared to the year 2014. The highest number of seeds per plant (NSP) was observed in 2015, being 20% and 31% higher when compared to 2013 and 2014. An abundant irrigation resulted in the highest NSP when compared to a rational and control treatment. Irrigation, study year, and their interaction did not have a statistically significant effect on the thousand seed weight (TSW) (g), but the lowest average TSW (g) was obtained in the control treatment of each study year. Regression models pertaining to the seed yield prediction in the control treatment and rational irrigation were not statistically significant. However, in the abundant irrigation, the regression model based on the TSW (g), NNP, and the NSP as the predictors provided for a statistically significant model seed yield prediction, but only the NSP was identified as a highly significant seed yield predictor.

Monitoring of crop properties using UAV is a novel procedure in agricultural practice, allowing the detection of anomalies and the adjustment of crop management. In this research, five cyclic imaging repetitions were conducted for soybean agricultural parcel located in Donji Miholjac. Monitored soybean growth stages were a reference stage before sowing, fourth trifoliolate, beginning pod and beginning maturity stage. Images collected in the field were processed by Structure-from- Motion algorithm for the creation of digital orthophoto. Four complementary vegetation indices calculated from red, green and blue spectral channels of digital orthophoto were used for the interpretation of crop properties. The low-cost and effectiveness of UAV in crop monitoring showed potential for implementation in agricultural practice.

Soja (Glycine max (L.) Merr.) je jedna od najstarijih i najvažnijih kulturnih biljaka u svijetu. Uvođenjem GIS i GNSS tehnologija u poljoprivrednoj mehanizaciji počela se razvijati precizna poljoprivreda čiji je cilj približiti se svakoj biljci i stvoriti joj optimalne uvjete za rast i razvoj. Cilj ovog rada je cikličko snimanje s ciljem istraživanja praćenja usjeva tako da se odredi u kojim fenološkim fazama soje je potrebno provesti snimanje. Tlo, svijetlo, vlaga i temperatura su primarni vanjski činitelji koji utječu na urod soje. Tijekom snimanja zadane površine upotrebljavana je bespilotna letjelica Phantom 4 2.0 Pro. Ovakva vrsta bespilotne letjelice je izuzetno pametna te niskobudžetna jer sa svojom kamerom može pratiti predmet bez zasebnoga uređaja i izbjegavati prepreke koje se nalaze na putu. Podatci koji su u istraživanju prikupljeni bespilotnom letjelicom uneseni su u računalne programe. Pomoću QGIS-a izrađen je kartografski prikaz, dok su u SAGA GIS-u izračunati vegetacijski indeksi VARI, NGRDI, GLI te ExG. Vegetacijski indeksi koji su izrađeni pokazali su različita odstupanja u određenim fazama vegetacije. To pokazuje da bi se za neke faze, kao što je u ovom istraživanju bilo praćenje soje, trebali koristiti određeni vegetacijski indeksi., Soybean (Glycine max (L.) Merr.) is one of the oldest and most important cultivated plants in the world. Precise agriculture, whose goal is to get closer to any plant and to create optimal conditions for growth and development of these plants, started to develop by implementing GIS and GNSS technology in agricultural mechanization. The goal of the paper is monitoring crops to see in which phenological phase is recording necessary, and that is possible with cyclic monitoring. Ground, light, humidity and temperature are primal external factors that effect the soy harvest. Drone Phantom 4 2.0 Pro was being used during the recording of provided area. This kind of drone is very sophisticated and low-budget because it can follow the object without additional device and also avoid obstacles that are on its way. Collected data is imported in computer software. Cartographic display is made in QGIS, while vegetal indexes VARI, NGRDI, GLI, ExG etc. are made in SAGA GIS. Mentioned vegetal indexes showed different deviations in certain phases of vegetation. That implicates that for some phases (e.g. tracking growth phases of soy) certain vegetal indexes should be used.