Wheat is one of the main grain species as well as one of the most important crops, being the basic food ingredient of people and livestock. Due to the importance of wheat production scale, it is advisable to predict its yield before harvesting. However, the current models are built solely on the basis of quantitative data. Therefore, the aim of the work was to create three multicriteria models for the prediction and simulation of winter wheat yield, which were made on the basis of extended quantitative and qualitative variables from field research in the year period 2008–2015. Neural networks with MLP (multi-layer perceptron) topology were used to build the following models, which can predict and simulate the yield on three dates: 15 April, 31 May, and 30 June. For this reason, they were designated as follows: QQWW15_4, QQWW31_5, and QQWW30_6. Each model is based on a different number of independent features, which ranges from 19 to 25. As a result of the conducted analyses, a MAPE (mean absolute percentage error) forecast error from 6.63% to 6.92% was achieved. This is equivalent of an error ranging from 0.521 to 0.547 t·ha−1, with an average yield of 6.57 ton per hectare of cultivated area. In addition, the most important quantitative and qualitative factors influencing the yield were also indicated. In the first predictive range (15 April), it is the average air temperature from 1 September to 31 December of the previous year (T9-12_PY). In the second predictive range (31 May) it is the sum of precipitation from 1 May to 31 May, and in the third (30 June) is the average air temperature from 1 January to 15 April of the year (T1-4_CY). In addition, one of the qualitative factors had a significant impact on the yield in the first phase-the type of forecrop in the previous year (TF_PY). The presented neural modeling method is a specific extension of the previously used predicting methods. An element of innovation of the presented concept of yield modeling is the possibility of performing a simulation before harvest, in the current agrotechnical season. The presented models can be used in large-area agriculture, especially in precision agriculture as an important element of decision-making support systems.

Remote sensing is a very useful method for data collection in open spaces, especially in precision agriculture and has been widely used over centennial. This paper presents the development of methodologies and identification of a surface model grasslands and pastures based on of chosen guidelines and properties. The model will be used to automate the process of monitoring the grasslands based on the analysis of spatial data and computer analysis of aerial photographs obtained automatically.

A complex research project was undertaken by the authors to develop a method for the automatic identification of grasslands using the neural analysis of aerial photographs made from relative low altitude. The development of such method requires the collection of large amount of various data. To control them and also to automate the process of their acquisition, an appropriate information system was developed in this study with the use of a variety of commercial and free technologies. Technologies for processing and storage of data in the form of raster and vector graphics were pivotal in the development of the research tool.

Most IT systems rely on dedicated databases, and most of these databases are relational. The advantages of such databases are well known and widely reported in literature. Unfortunately, attempts to identify the topology of links in the relational model produced by iterative development or administrative enhancements are often hampered by the large number of tables that make up the database and the lack of comprehensive technical documentation. Analysis of the model by someone other than its designer requires substantial effort. The aim of the presented work is therefore to develop an application for effective presentation of the database structure in the form of a directed graph. The main assumption was that a graph-oriented database environment would be used. This paper presents the RELATIONS-Graph application developed by the authors. This application automatically generates a directed graph which presents links between tables and attributes which constitute a relational database. The RELATIONS-Graph application can also scan the generated graph in order to discover links between selected tables and columns. This solution has been applied to SQL Server 2014 SP1 DBMS using the Microsoft .NET technology and the Neo4j graph database, also by .NET API. The RELATIONS-Graph application was developed in C#, an object-oriented programming language.

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