Prediction of Weights during Growth Stages of Onion Using Agricultural Data Analysis Method

Abstract

In this study, we propose a new agricultural data analysis method that can predict the weight during the growth stages of the field onion using a functional regression model. We have used onion weight on growth stages as the response variable and six environmental factors such as average temperature, average ground temperature, rainfall, wind speed, sunshine, and humidity as the explanatory variables in the functional regression model. We then define a least minimum integral squared residual (LMISE) measure to obtain an estimate of the function regression coefficient. In addition, a principal component regression analysis was applied to derive the estimates that minimize the defined measures. Next, to evaluate the performance of the proposed model, data were collected, and the following results were identified through analyses of the collected data. First, through graphical and correlation analysis, the ground temperature, mean temperature, and humidity have a very significant effect on the onion weights, but environmental factors such as wind speed, sunshine, and rainfall have a small negative effect on onion weights. Second, through functional regression analysis, we can determine that the ground temperature, sunshine, and precipitation have a significant effect on onion growth and are essential in the goodness-of-fit test. On the other hand, wind speed, mean temperature, and humidity did not significantly affect onion growth. In conclusion, to promote onion growth, the appropriate ground temperature and amount of sunshine are essential, the rainfall and the humidity must be low, and the appropriate wind or mean temperature must be maintained.

Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea

“…Accordingly, knowing the price of agricultural commodities in advance provides market participants (i.e., governments, farmers, consumers, and others) with advantages, such as providing a clearer understanding of the market and allowing the planning of business strategies and the adjustment of personal finances, among others. Thus, there have been many efforts to predict the future prices based on historical factors, such as earlier prices [11], product quality levels [12], climate change [13], seasonality factors [14], agricultural disasters [15], and other economic effects [16].…”