Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps

Abstract

Acquiring useful data from agricultural areas has always been somewhat of a challenge, as these are often expansive, remote, and vulnerable to weather events. Despite these challenges, as technologies evolve and prices drop, a surge of new data are being collected. Although a wealth of data are being collected at different scales (i.e., proximal, aerial, satellite, ancillary data), this has been geographically unequal, causing certain areas to be virtually devoid of useful data to help face their specific challenges. However, even in areas with available resources and good infrastructure, data and knowledge gaps are still prevalent, because agricultural environments are mostly uncontrolled and there are vast numbers of factors that need to be taken into account and properly measured for a full characterization of a given area. As a result, data from a single sensor type are frequently unable to provide unambiguous answers, even with very effective algorithms, and even if the problem at hand is well defined and limited in scope. Fusing the information contained in different sensors and in data from different types is one possible solution that has been explored for some decades. The idea behind data fusion involves exploring complementarities and synergies of different kinds of data in order to extract more reliable and useful information about the areas being analyzed. While some success has been achieved, there are still many challenges that prevent a more widespread adoption of this type of approach. This is particularly true for the highly complex environments found in agricultural areas. In this article, we provide a comprehensive overview on the data fusion applied to agricultural problems; we present the main successes, highlight the main challenges that remain, and suggest possible directions for future research.

Digital Mapping of Land Cover Changes Using the Fusion of SAR and MSI Satellite Data

“…The applied image merging techniques also confirm the conclusions of other authors [26][27][28][29][30][31][32][33] that the merging of two different sensors enriches the image information. Moreover, SAR images may compensate for the low cloud content of MSI images, especially if multiple SAR images are used.…”

“…Information collected from different image sensors and properly processed by synthesising satellite images allows data gaps to be filled and more accurate results to be obtained [22]. Various studies on the fusion of different data can be found in the scientific literature [23][24][25][26][27][28][29][30][31][32][33]. Such works began in the 1980s, but some of the processes have not been automated.…”

“…The literature review by Jayme Garcia and Arnal Barbedo highlights the fusion techniques and accuracy. The Convolutional Neural Networks (CNN), Backpropagation Neural Network (BPNN), Random Forest (RF), Partial Least Squares Regression (PLSR), Dempster-Shafer and the Kalman filter and least squares support vectors machine gave the best mean precision of the result [28]. The authors explain that in the case of agriculture, image data fusion has been particularly effective at the orbital level, both for artificially increasing the spatial resolution of sources with revisit frequencies and for compensating cloud cover using the information present in SAR images.…”

Experimenting Agriculture 4.0 with Sensors: A Data Fusion Approach between Remote Sensing, UAVs and Self-Driving Tractors

“…This is especially the case when the data being compared have significantly different characteristics (for example, digital images and meteorological data). Given the wide variety of data sources and methods utilized in agricultural applications [ 40 , 41 ], it can be challenging to find a formalization for the data fusion process that is suitable for all of these applications. A perspective on the data fusion process is given here, broken down into three stages and applicable to the vast majority of situations.…”

“…Recent literature also exists on data fusion techniques for agriculture: on input devices synchronised with microcontrollers and sending data from sensors via IoT (Internet of Things) devices to the cloud [ 40 ] and the challenges and complexity of Agriculture 4.0 [ 41 ].…”