Discovering Patterns with Neural Networks in Agricultural Crop Monitoring

Abstract

neural networks have emerged as practical tools for discovering styles in agricultural crop monitoring. The motive of this take a look at was to research whether or not neural network strategies will be implemented to seize and perceive patterns in various parameters related to crop monitoring. Data was accrued from four corn fields inside the Midwest US from October 2007 to October 2009. Linear and non-linear neural networks were used to analyze the information, to identify enormous styles associated with crop manufacturing. Effects showed that the neural networks have been able to as they should be perceived styles inside the records, with the non-linear network generating satisfactory results. The results confirmed that the most important parameters related to crop production were the width of the kernel, duration of the cob, and precise leaf location. These findings advocate that neural networks may be a promising technique for understanding complicated crop monitoring relationships.