Data Mining with Neural Networks for Wheat Yield Prediction

Precision agriculture (PA) and information technology (IT) are closely interwoven. The former usually refers to the application of nowadays' technology to agriculture. Due to the use of sensors and GPS technology, in today's agriculture many data are collected. Making use of those data via IT often leads to dramatic improvements in efficiency. For this purpose, the challenge is to change these raw data into useful information. In this paper we deal with neural networks and their usage in mining these data. Our particular focus is whether neural networks can be used for predicting wheat yield from cheaply-available in-season data. Once this prediction is possible, the industrial application is quite straightforward: use data mining with neural networks for, e.g., optimizing fertilizer usage, in economic or environmental terms.