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Honey is one of the most significant ingredients in conventional food production in different regions of the world. Honey is commonly used as an ingredient in ethnic food. Beekeeping is performed in various locations as part of the local food culture and an occupation related to pollinator production. It is important to conduct beekeeping so that it generates food culture and helps regulate the regional environment in an integrated manner in preserving and improving local food culture. This study analyzes different types of environmental factors of a smart bee farm. The major goal of this study is to determine the best prediction model between the linear regression model (LM) and the support vector regression model (SVR) based on the environmental factors of a smart bee farm. The performance of prediction models is measured by R2 value, root mean squared error (RMSE), and mean absolute error (MAE). From all analysis reports, the best prediction model is the support vector regression model (SVR) with a low coefficient of variation, and the R2 values for Farm inside temperature, bee box inside temperature, and Farm inside humidity are 0.97, 0.96, and 0.44.

There are various types of pathogens that occur in plants, due to the fact of climate changes, weather changes, seasons changes and the significance of environmental (temperature, humidity, rainfall, etc.) changes. The consequence of plant disease affects our agriculture industry and agriculture sector. It affects our plant growth, production growth, and economic growth throughout the world. So, to prevent the diseases, necessary to understand weather conditions and also identify corresponding environmental factors in plant diseases. Therefore, in this study, analysis of the different types of plant diseases and identification of corresponding environmental factors in plum data using the artificial neural network. Using neural network model to identify the environmental factors and the purpose of the correlation method is to find out the relationship between two variables (the actual value of diseases and the predicted value of diseases). Finally, in result explained detailed to identify the environmental factors in plum data.

Environmental food and nutritional protection primarily depend on pollination from bees. Historically, beekeeping has been performed in different locations as part of the local food community. Beekeeping is increasing rapidly these days due to the high demand for honey and farmers are taking various forms of beekeeping methods to achieve high yield. Honey production also depends on different types of environmental factors. The main principle of this study is to show the analysis results of various types of environmental factors for three different bee farms by the linear regression model to figure out the best farm among all three farms. To improve the production of honey, farmers have to consider different types of environmental factors and this is the elevated time to support farmers by technology. This study analyzed different types of environmental factors like farm outside temperature, farm inside temperature, farm humidity for three different smart bee farms by using a linear regression model to know about their environmental conditions. The performance of prediction models is measured by R 2 error, Root Mean Squared Error (RMSE), Standard Error values (SE), and Mean Absolute Error (MAE). Based on the outcome, it is observed that the best results giving farm is farm 3 that has been able to give R 2 value 0.95,0.95, and 0.72 for the farm outside temperature, inside temperature, and farm humidity.