International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

The research was performed in 2019-2020 LAMMC at Rumokai Experimental Station of the Lithuanian Research Centre for Agriculture and Forestry. The soil was calcaric luvisol, the granulometric composition was a silt loam on clay Winter wheat, after resumption of vegetation fertilized UAN-32 (control), Leader 24-6 and KAS-32 + Thio-Sul (N12S26). Nitrogen active substance: 60 + 60 + 30. Mineral nitrogen in the soil was investigated twice: during the grain milk stage of wheat and after harvest. In the grain milk stage in 2019 Leader 24-6 reduced mineral nitrogen by 6.7-47.6 %, UAN-32 + Thio-Sul – 21.6-28.7 %. 2020 – 58.0-42.3 % and 26.3-19.8 % (depending on the number of uses). After harvesting, mineral nitrogen was found in the fields where sulfur fertilizers were used 5.13-49.2 % less than in the control fields. The use of Thio-Sul had a positive effect on protein content.

The research was performed in 2019-2020 LAMMC at Rumokai Experimental Station of the Lithuanian Research Centre for Agriculture and Forestry. The soil was calcaric luvisol, the granulometric composition was a silt loam on clay Winter wheat, after resumption of vegetation fertilized KAS-32 and Leader 24-6. Nitrogen active substance: 60 + 60 + 30. In I and II fertilizations, these liquid fertilizers were used in combination with a urease inhibitor. Mineral nitrogen in the soil was investigated twice: during the graing milk stage of wheat and after harvest. In the grain milk stage in 2019 the urease inhibitor increased mineral nitrogen by 25,8-30.0%. 2020 the effect of a urease inhibitor on the mineral nitrogen content of the soil has not been found. After the harvest in 2019 in the fields where nitrogen fertilization with urease inhibitor was applied in the 0-30 cm layer of nitrate nitrogen was significantly lower than in the control fields. The effect of the urease inhibitor on winter wheat grain yield was not found in the study years

This study aimed to predict the protein content(PC) and canopy spectra in winter wheat were measured based on field test. Key spectral bands were chosen by principal component analysis (PCA) method, and the predicted models were built by Partial Least Squares Regression (PLSR) and Artificial Neural Network (ANN). The performance of the feed forward and cascade forward ANNs was compared with those of PLS regression models using root mean square error (R) and the correlation coefficient (2). The finest consequence by CFBP was related to topology of 8-8-1 with Levenberg-Marquardt (LM) algorithm, threshold function of TANSIG-TANSIG-PURELIN and the initial strategy. This arrangement output was R=0.0289 and 2=0.9881 at 14 epochs. The consequences of estimate for correlation values of PLSR model was 0.9783. The results of prediction for the two models were in order of ANN > PLSR with correlation values of 0.9881 and 0.9783, respectively. Therefore, NIRS shows the potential for predicting protein content with accuracies suitable for process control.