Effects of agrotechnical factors on the quality and quantity of yield in winter wheat production

The present study was conducted to determine the effect of basic agrotechnical factors on the yield and quality of winter wheat. Two experiments were set in 2017/2018 growing season, where we studied the influence of different forecrops, fertilizing treatments and cultivars. 204 samples were measured with Single Kernel Characterization System and NIR grain analyser to determine protein (NIR-P), wet gluten (NIR-WG), Hardness Index (HI), kernel weight (KW) and kernel diameter (KD). Fertilizing had a significant effect on yield, KW, HI, NIR-P and NIR-WG, except KD. N90PK dosage was enough to realize yield potential for 6 out of 9 cultivars, but considering protein content N150PK dosage was needed. The forecrop had no significant influence on yield, KW, KD or HI, however sweet corn as previous crop had significant improving effect on NIR-P and NIR-WG compared to sunflower as forecrop. According to our data of correlation analysis, no negative relationship was found between yield and NIR-P, however HI was in medium positive correlation with NIR-P. The variety Vyckor had the highest yield, but in quality aspect, the highest NIR-P and NIR-WG values belonged to KG Kunhalom variety.

Neural Visual Detection of Grain Weevil (Sitophilus granarius L.)

A significant part of cereal production is intended for agri-food processing, which implies a necessity to search for and implement modern storage systems for this product. Stored grain is exposed to many unfavorable factors, particularly caryopsis macro-damage caused mainly by grain weevil (Sitophilus granarius L.). This triggers a substantial decrease in the value of the stored material, thus resulting in serious economic losses. Due to this fact, it is necessary to take steps to effectively detect this pest’s presence when grain is delivered to storage facilities. The purpose of this work was to identify the representative physical characteristics of wheat caryopsis affected by grain weevil. An automated visual system was developed to ease the detection of damaged kernels and adult weevils. In order to obtain the empirical data, a decision was made to take advance of SKCS 4100 (the Perten Single Kernel Characterization System). The measurements obtained were used to build the training sets necessary in the process of ANN (artificial neural network) learning with digital neural classifiers. Next, a set of identifying neural models was created and verified, and then the optimal topology was selected. The utilitarian goal of the research was to support the decision-making process taking place during grain storage.