Precision farming using autonomous data analysis cycles for integrated cotton managemen

Abstract

Precision farming (PF) allows the efficient use of resources such as water, and fertilizers, among others; as well, it helps to analyze the behavior of insect pests, in order to increase production and decrease the cost of crop management. This paper introduces an innovative approach to integrated cotton management, involving the implementation of an Autonomous Cycle of Data Analysis Tasks (ACODAT). The proposed autonomous cycle is composed of a classification task of the population of pests (boll weevil) (based on eXtreme Gradient Boosting-XGBoost), a diagnosis-prediction task of cotton yield (based on a fuzzy system), and a prescription task of strategies for the adequate management of the crop (based on genetic algorithms). The proposed system can evaluate several variables according to the conditions of the crop, and recommend the best strategy for increasing the cotton yield. In particular, the classification task has an accuracy of 88%, the diagnosis/prediction task obtained an accuracy of 98 %, and the genetic algorithm recommends the best strategy for the context analyzed. Focused on integrated cotton management, our system offers flexibility and adaptability, which facilitates the incorporation of new tasks.