Genetic Algorithm in Optimization Problems for Greenhouse Facilities

Every year, farms face the problem of ensuring the necessary development and growth of field crops due to the high probability of field crops being affected by certain types of pests. Pests can significantly impair the development of crops if their population is not controlled. This will reduce the harvest. To ensure a certain level of field crop production, it is necessary to take a series of measures to reduce the risk of harvest losses and optimize the costs of protecting plant growth. A key element of effective farmland management is the reliable prediction of the number of pests using artificial neural networks and their appropriate configuration. This approach will reduce harvest losses and preserve the ecosystem of a particular region. Reliable forecasting of pest numbers is guaranteed to create conditions for minimizing the cost of growing crops. However, machine learning can only be implemented if there are relevant results of monitoring the number of pests and the factors that influence changes. These factors include solar activity, temperature, and humidity. Such studies were conducted and samples were formed. Neural networks of different structures were used for forecasting, such as the radial basis function and the multilayer perceptron. The results of the forecasting show a sufficiently high accuracy, which will significantly improve production efficiency.

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A model of the decision support system for managing the process of growing vegetables in a greenhouse

Lendiel,

Lendiel

2024

Energy and automation

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The developed of automatic control systems provide constant monitoring of technological indicators in greenhouses, as well as reporting on the current state in real time and conducting analysis based on available data. Having this data in the system, the manufacturer can analyze all key indicators, their changes and impact over time and make appropriate decisions for their enterprise. However, the created systems expand over time, and accordingly the information in them also expands, so it is necessary to effectively analyze previously entered data. In this case, there is a need to create a system that will analyze indicators based on accumulated data. It is proposed to carry out analysis using OLAP and Data Mining technologies. The purpose of the research is to implement a data warehouse of a decision support system using Data Mining technology to increase the efficiency of growing vegetables in closed soil structures. In the process of developing an automated control system, a storage model of these decision support systems was developed. In the work, the structure of the dynamic database was developed using the time series algorithm. At the same time, data input, storage and analysis modules were created. The use of Data Mining technology for the analysis of large volumes of information was proposed. The obtained results of the system can be used in the process of forming management decisions for managing technological processes in the greenhouse economy. This will allow you to direct the management strategy of individual business processes in such a way as to increase the yield in greenhouses and, accordingly, the profitability of the farm as a whole.

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Genetic Algorithm in Optimization Problems for Greenhouse Facilities

Cited by 3 publications

References 26 publications

Algorithm of the Autonomy of a Phytomonitoring Mobile Robot in a Greenhouse Facility

Algorithm of the Autonomy of a Phytomonitoring Mobile Robot in a Greenhouse Facility

Artificial Neural Networks for Predicting the Number of Field Crop Pests

A model of the decision support system for managing the process of growing vegetables in a greenhouse

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