Data Fusion and Its Applications in Agriculture

Abstract

An information revolution is currently occurring in agriculture resulting in the production of massive datasets at different spatial and temporal scales; therefore, efficient techniques for processing and summarizing data will be crucial for effective precision management. With the profusion and wide diversification of data sources provided by modern technology, such as remote and proximal sensing, sensor datasets could be used as auxiliary information to supplement a sparsely sampled target variable. Remote and proximal sensing data are often massive, taken on different spatial and temporal scales, and subject to measurement error biases. Moreover, differences between the instruments are always present; nevertheless, a data fusion approach could take advantage of their complementary features by combining the sensor datasets in a manner that is statistically robust. It would then be ideal to jointly use (fuse) partial information from the diverse today-available sources so efficiently to achieve a more comprehensive view and knowledge of the processes under study. The chapter investigates the data fusion process in agriculture and its connection to artificial intelligence, neural networks, and IoT in agriculture, and introduces the concepts of data fusion with applications in Remote and Proximal sensing.