# Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps
## 1 Introduction
The most common categorization divides data fusion techniques into three groups [9]: (a) raw data level (also denoted \"low-level\" and \"early integration\"), in which different types of data (raw or preprocessed) are simply concatenated into a single matrix, being used in cases in which pieces of data are of the same nature and were properly normalized. (b) Feature level (also denoted \"mid-level\" and \"intermediate integration\"), in which features are first extracted from different types of data and then concatenated into a matrix, being mostly used when pieces of data can be treated in such a way they generate features that are compatible and complementary. (c) Decision level (also denoted \"high level\" and \"late integration\"), in which classification and regression algorithms are applied separately to each type of datum and then the outputs generated by each model are combined, being more appropriate when data sources are too distinct to be combined at an earlier stage. An alternative classification of data fusion methods was proposed by [PERSON] et al. [10]: probability-based, evidence-based, and knowledge-based. Although both classifications are useful, the first one is more appropriate in the context of this work (Figure 1). In the specific case of agriculture, data can be collected at three different scales--proximal, aerial, and orbital (satellites) (Figure 1). Applications that use proximal data include navigation systems for autonomous vehicles [11; 12; 13; 14; 15; 16; 17], fruit detection [18; 19; 20; 21], plant disease detection [22; 23; 24; 24], delineation of homogeneous management zones [25; 26; 27; 28; 29], soil analysis [30; 31; 32; 33; 34; 35; 36], plant phenotyping [37], among others. Aerial data (collected using UAVs) is used mostly for detection of certain objects (e.g., certain plant species and fruits) [38] and for estimation of agricultural variables (e.g., soil moisture and nitrogen content) [39; 40; 41]. Satellite data are used for mapping variables as diverse as soil moisture [42; 43; 44], crop type [45; 46; 47; 48; 49; 50], crop phenological states [51; 52], evapotranspiration [40; 53; 54; 55; 56; 57; 58], nitrogen status [59; 60; 61; 62], biomass [63; 64], among others. While most data fusion approaches only use data in the same scale, a few studies have applied data originating from different scales [10; 26; 28; 31; 38; 40; 51; 52; 64; 65; 66; 67; 68; 69; 70; 71]. The objective of this article was to characterize the current state-of-the-art regarding the process of applying data fusion for agricultural applications. First, a comprehensive overview of the literature is provided, with emphasis on articles published after 2010. Then, the aspects involved in the use of data fusion to different types of data are explored in detail, together with some possible solutions for the weaknesses that prevent technologies based on data fusion from being more widely used in practice. Although there have been a few reviews dedicated toward data fusion in agriculture, they focused on specific applications and themes [9; 10; 68; 70; 72], while this one adopts a more general and systemic view of the subject. The remainder of the article is organized as follows. Section 2 describes the literature related to the three different scales--proximal, aerial, and orbital. A discussion on several aspects relevant to data fusion in agriculture is presented in Section 3. Finally, Section 4 offers some final remarks and possible directions for future research.