Corn Pests and Diseases Prediction Using Linear Regression and Natural Spline Methods

Food self-sufficiency is a strategic plan that has been set by the Indonesian government to achieve zero hunger for all Indonesian people. One way to achieve it is by improving the productivity of crops cultivation. However, a data from the Indonesian Ministry of Agriculture states that Indonesia has experienced a decline in crops harvest area by an average of 1.76% in 2005-2015. A decline in crops production productivity is further backed by a data submitted by the Central Bureau of Statistics. According to them, Indonesia has experienced a consistent decline in the growth of gross domestic product in the field of agricultural business in 2014-2016. In order to achieve food self-sufficiency, we require an information system that can be used to monitor factors which may be detrimental to crops production, in particular, pests and diseases. The emergence of pests and diseases also has high causality with the dynamics of climatic conditions that occur in a particular area. The emergence of pests and diseases that attack crops plants can be analyzed based on the climatic conditions that have occurred. This research is carried out by utilizing a statistical approach to predict the types of diseases that will attack at certain times. This study utilizes data on the areas of corn fields that are attacked by certain pests and diseases based on climatic conditions. We used Linear Regression and Natural Spline as prediction algorithms.