The eutrophication of surface waters is a natural process; however, anthropogenic activities significantly accelerate degradation processes. Most lakes in Poland and in the world belong to the poor and unsatisfactory water quality class. It is therefore necessary to limit negative anthropogenic impacts and introduce restoration methods, in particular those that are safe for the aquatic ecosystem. One of these is a pulverizing aeration Podsiadłowski method that uses only wind energy. The method allows for the moderate oxygenation of hypolimnion water, which maintains the oxygen conditions in the overlying water zone in the range of 0–1 mg O2·dm-1. The purpose of the work was to develop a new method of determining the efficiency of the aerator pulverization unit in the windy conditions of the lake. The method consists in determining the volumetric flow rates of water in the aerator pulverization unit, based on maximum hourly wind speeds. The pulverization efficiency in the conditions of Góreckie Lake was determined based on 6600 maximum hourly wind speeds in 2018. Based on the determined model, the theoretical performance of the machine was calculated, which in the conditions of Góreckie Lake in 2018 amounted to less than 79,000 m3 per year (nine months of the effective aerator operation).
Samołęskie Lake is situated in the Poznań Lakeland in Greater Poland Voivodeship, Szamotuły County, Wronki District. The lake adjoins a little village of Samołęż of about 500 residents. The glacial waterbody of over 30ha acreage is a typical tunnelvalley lake having a maximum depth of over 22 meters. It predominantly serves fishing and recreation purposes offering a beach and a sailing center. Near the coastline (not in the direct vicinity) there is a farmland. The objective of the dissertation was to assess the quality of Samołęskie Lake waters that was delivered based on the studies carried out in spring and summer, when the waterbody demonstrates excessive fertility. The studies covered the analysis of the basic physical and chemical parameters of the lake water. The measurement was carried out on a fortnight basis by means of a measurement apparatus such as the photometer and the Secchi disc. The collected results are presented with the use of figures later in this paper, whereas their in-depth analysis allowed to compile and formulate conclusions. The results of studies and analyses lay the foundations to state that the quality of Samołęskie Lake waters requires continuous monitoring and application of remedial and rehabilitation measures.
An important factor along with the availability of food is its quality. It depends, among other things, on the type of plant protection products used and the method of their application. This manuscript presents research on the possibility of using a shielded band sprayer in field onion cultivation. The shielded band spraying technology presented in this article is the subject of a patent application (application number P.428494-The prototype of the machine was produced in Poland in cooperation with the University of Life Sciences in Poznań). The research consisted in comparing the quantity and quality of the obtained crop, based on various methods of reducing the weed population. The research results indicate that the proposed shielded band spraying technology may affect the food quality (the active substance is not sprayed on onion plants) and profitability of farms (less use of plant protection products).
The main objective of this study is to develop an automatic classification model for winter rapeseed varieties, to assess seed maturity and damage based on seed colour using a convolutional neural network (CNN). A CNN with a fixed architecture was built, consisting of an alternating arrangement of five classes Conv2D, MaxPooling2D and Dropout, for which a computational algorithm was developed in the Python 3.9 programming language, creating six models depending on the type of input data. Seeds of three winter rapeseed varieties were used for the research. Each imaged sample was 20.000 g. For each variety, 125 weight groups of 20 samples were prepared, with the weight of damaged or immature seeds increasing by 0.161 g. Each of the 20 samples in each weight group was marked by a different seed distribution. The accuracy of the models’ validation ranged from 80.20 to 85.60%, with an average of 82.50%. Higher accuracy was obtained when classifying mature seed varieties (average of 84.24%) than when classifying the degree of maturity (average of 80.76%). It can be stated that classifying such fine seeds as rapeseed seeds is a complex process, creating major problems and constraints, as there is a distinct distribution of seeds belonging to the same weight groups, which causes the CNN model to treat them as different.
This paper describes the research aimed at developing an effective quality assessment method for potato tubers using neural image analysis techniques. Nowadays, the methods used to identify damage and diseases are time-consuming, require specialized knowledge, and often rely on subjective judgment. This study showed the use of the developed neural model as a tool supporting the evaluation of potato tubers during the sorting process in the storage room.
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