# Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps
## 1 Introduction  
The number (and quality) of sensors used to collect data in different contexts have been steadily growing. Even complex environments, such as agricultural areas, are now being \"sensed\" via a wide variety of equipment, generating vast amounts of data that can be explored to provide useful information about the area being observed. As a result, the number of studies attempting to explore the wealth of information contained in the sensed data have increased [1; 2; 3]. However, it is often challenging to translate the advancements achieved in experiments to the conditions found in practice. There are two main reasons for this. First, the studies described in scientific texts are usually limited in scope, because the data used in these experiments usually do not cover all of the variabilities associated with the problem at hand. As a result, while the results reported in those articles may seem encouraging, they often reveal nothing about the performance of the proposed technique under real, unconstrained conditions. Second, even if the data adequately cover the variable conditions found in practice, the adopted sensing technology may not be capable of acquiring enough information to unambiguously resolve the data and provide enough information. For example, even powerful artificial intelligence models fed with RGB digital images are often unsuccessful in recognizing plant diseases from their symptoms, because different disorders can produce similar visual signs [4]. One way to reduce the gaps caused by data limitations is to apply data fusion techniques. The term \"data fusion\" can be defined as \"the process of combining data from multiple sources to produce more accurate, consistent, and concise information than that provided by any individual data source\" [5]. Other stricter definitions do exist to better fit narrower contexts. This type of approach has been applied to agricultural problems since the first half of the 1990s [6], and there has been an increase in the use of this approach. Arguably, the main challenge involved in the use of data fusion techniques involves finding the best approach to fully explore the synergy and complementarities that potentially exist between different types of data and data sources. This is particularly true with data having significantly disparate characteristics (for example, digital images and meteorological data). It is difficult to find a formalization for the data fusion process that fits all agricultural applications, given the variety of data sources and approaches. The formalization presented by [PERSON] and [PERSON] [7], although derived in a slightly different context, adopts a three-step view of the data fusion process that is applicable in most cases. In the first step, the corresponding attributes that are used to describe the information in different sources need to be identified. Such a correspondence can be easily identified if the data sources are similar, but it can be challenging as the different types of data are being used. This is one of the main reasons for the existence of the three types of data fusion described in the following paragraph. In the second step, the different objects that are described in the data sources need to be identified and aligned. This step is particularly important when data sources are images, because misalignments can lead to inconsistent representations and, as a result, to unreliable answers. Once the data are properly identified and consistent, the actual data fusion can be applied in the third step. In practice, coping with existing data inconsistencies is often ignored [7]. This situation can be (at least partially) remedied by auxiliary tools, such as data profile techniques, which can reduce inconsistencies by extracting and exploring the metadata associated to the data being fused [8].