Data Fusion in Agriculture: Comparison
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Please note this is a comparison between Version 2 by Conner Chen and Version 1 by Jayme Barbedo.

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”. 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.

  • data fusion
  • sensors
  • digital images
  • agriculture
  • crops

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 Bleiholder and Naumann[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].
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 Ouhami 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).
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Figure 1. Categorization of data fusion approaches adopted in this work.
Categorization of data fusion approaches.
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], 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].
Table 1.
Categories adopted for the data fusion techniques and the data being fused.
Fused Data Mean Accuracy
[99] Root zone soil moisture estimation NN (M), DRF (M), GBM (M), GLM (M) 2,11 0.90–0.95
[100] Gramineae weed detection in rice fields Haar wavelet transformation (L) 1, 2 0.70–0.85
[65] Monitoring agricultural terraces Coregistering and information extraction (L) 5 N/A
[66] Spectral–temporal response surfaces Bayesian data imputation (L) 2, 3 0.77–0.83
[101] Phenotyping of soybean PLSR (L), SVR (L), ELR (L) 1, 2, 4 0.83–0.90
[39] Soybean yield prediction PLSR (M), RF (M), SVR (M), 2 types of DNN (M) 1, 2, 4 0.72
[52] Crop monitoring PLSR (M), RF (M), SVR (M), ELR (M) 1, 2 0.60–0.93
[40] Evapotranspiration estimation MSDF-ET (L) 1, 2, 4
[
94
]
Rapid determination of soil class
Outer product analysis (L)
7
0.65
[
16
]
Navigation of autonomous vehicle
MSPI algorithm with Bayesian estimator (L)
11, 12
N/A
[
38
]
Detection of cotton plants
Discriminant analysis (M)
2, 7
0.97
0.68–0.77
[10] Review: IoT and data fusion for crop disease N/A N/A N/A 75] Delineation of homogeneous management zones Kriging (L), Gaussian anamorphosis (L) 9, 15 N/A
[102] Arid and semi-arid land vegetation monitoring Decision tree (L/M) 3, 5 0.84–0.89 [27]
[41] Biomass and leaf nitrogen content in sugarcane
[
95
]
Map-based variable-rate manure application
K-means clustering (L) 2, 9 0.60–0.93
[17] Navigation of autonomous vehicles Kalman filter (L) 11, 12 N/A
[96] Robust tomato recognition for robotic harvesting Wavelet transform (L) 1 0.93
[97] Navigation of autonomous vehicle Self-adaptive PCA, dynamic time warping (L) 1, 11 N/A
[98] Recognition of wheat spikes Gram–Schmidt fusion algorithm (L) 1, 2 0.60–0.79
 

3. Aerial Scale

3. Aerial Scale

Studies employing UAVs to solve agricultural problems are growing in number, but they are still outnumbered by proximal and orbital approaches. Most studies are dedicated to crop monitoring and object detection (weed, crops, etc.), although applications, such as phenotyping and water management, are also present. Almost all techniques are based on some kind of digital image (RGB, multispectral, thermal, hyperspectral). Many approaches explore the complementarity of aerial images with proximal (four articles) and orbital (six articles) data. Only eight studies employed the aerial data alone (Table 3).
Table 3. RAeferences considered in this study–aeriarial scale. L, M, and H mean low-, mid-, and high-level data fusion, respectively. The numbers in the fourth column are those adopted in Table 1 for each “fused data” class.
Ref. Application Fusion Technique
PCA and linear regression (L)
2, 5
0.57
[
70
]
Review: data fusion in agricultural systems N/A N/A N/A
[103] Navigation system for UAV EKF (L) 11, 12 0.98
[38] Detection of cotton plants Discriminant analysis (M) 2 0.97
[71] Vineyard monitoring PLSR (M), SVR (M), RFR (M), ELR (M) 2 0.98
 

4. Orbital Scale

4. Orbital Scale

A large portion of the articles employing satellite images aimed to either compensate for data gaps present in a primary data source by fusing it with another source of data (for example, combining optical and SAR images)[6][45][47][48][49][51][104][105], or increase the spatial resolution of the relatively coarse images collected by satellites with high revisit frequencies[42][43][44][55][57][58][106][107][108][109]. In the latter, the fused results usually inherit the details of the high spatial resolution images and the temporal revisit the frequencies of their counterparts, although the quality of the fused data usually do not match that obtained through actual missions, especially when surface changes are rapid and subtle[110]. As argued by Tao et al.[111], different sensors and image processing algorithms lead inevitably to data with some level of inconsistency, which can make rapid changes difficult to detect.
Landsat and MODIS images and products still dominate, but other satellite constellations, such as Sentinel, Worldview, GeoEye, and others, are being increasingly adopted. Data fusion has been applied to satellite images for quite some time, and well established techniques, such as STARFM and its variants, are still often used, but the interest for machine learning techniques, especially in the form of deep learning models, has been growing consistently. Water management in its several forms (evapotranspiration estimation, mapping of irrigated areas, drought detection, etc.) is by far the most common application. Yield estimation, crop monitoring, land cover classification, and prediction of soil properties are also common applications.
A major challenge associated with the orbital scale is the existence of highly heterogeneous regions with a high degree of fragmentation[108][112]. Solutions to this problem are not trivial and, as stated by Masiza et al.[113], “…successful mapping of a fragmented agricultural landscape is a function of objectively derived datasets, adapted to geographic context, and an informed optimization of mapping algorithms”. However, there are cases in which target areas can have sizes smaller than the pixel resolution of the satellite images[53]. In theses cases, pairing the images with images or other types of data obtained at higher resolutions (aerial or proximal) may be the only viable solution. Satellite data were fused together with proximal and aerial data in ten and six studies, respectively (Table 4).
Table 4. References consideOred in this study–orbital scale. L, M, and H mean low-, mid-, and high-level data fusion, respectively. The numbers in the fourth column are those adopted in Table 1 for each “fused data” class.
Ref. Application Fusion Technique Fused Data Mean Accuracy
[42] Soil moisture mapping ESTARFM (L) 2 0.70–0.84
[45] Crop type mapping 2D and 3D U-Net (L), SegNet (L), RF (L) 2, 6 0.91–0.99
[43] Estimation of surface soil moisture ESTARFM (L) 2 0.55–0.92
[26] Delineation of homogeneous zones Kriging and other geostatistical tools 2, 9 N/A
[51] Estimation of crop phenological states Particle filter scheme (L/M) 2, 6, 10 0.93–0.96
[53] Evapotranspiration mapping at field scales STARFM (L) 2 0.92–0.95
[31] In-field estimation of soil properties RK (L), PLSR (L) 3, 9 >0.5
[59] Estimation of wheat grain nitrogen uptake BK (L) 2, 3 N/A
[44] Surface soil moisture monitoring Linear regression analysis and Kriging (L/M) 2, 15 0.51–0.84
[46] Crop discrimination and classification Voting system (H) 2, 6 0.96
[9] Review on multimodality and data fusion in RS N/A N/A N/A
[47] Crop Mapping Pixelwise matching (H) 2, 6 0.94
[110] Review on fusion between MODIS and Landsat N/A N/A N/A
[106] Mapping crop progress STARFM (L) 2 0.54–0.86
[66] Generation of spectral–-temporal response Bayesian data imputation (L) 2, 3 0.77–0.83
[28] Delineation of management zones K-means clustering (L) 2, 9, 14 N/A
[114] Mapping irrigated areas Decision tree (L) 2 0.67–0.93
[54] Evapotranspiration mapping Empirical exploration of band relationships (L) 2, 4 0.20–0.97
[28] Delineation of management zones K-means clustering (L) 2, 9, 14 N/A
[67] Yield gap attribution in maize Empirical equations (L) 15 0.37–0.74
[63] Change detection and biomass estimation in rice Graph-based data fusion (L) 2 0.17–0.90
[107] Leaf area index estimation STARFM (L) 2 0.69–0.76
[55] Evapotranspiration estimates STARFM (M) 2 N/A
[115] Classification of agriculture drought Optimal weighting of individual indices (M) 2 0.80–0.92
[56] Mapping daily evapotranspiration STARFM (L) 2 N/A
[20] Mapping of cropping cycles STARFM (L) 2 0.88–0.91
[116] Evapotranspiration partitioning at field scales STARFM (L) 2 N/A
[68] Review: image fusion technology in agriculture N/A N/A N/A
[52] Crop monitoring PLSR (M), RF (M), SVR (M), ELR (M) 1, 2, 4 0.60–0.93
[113] Mapping of smallholder crop farming XGBoost (L/M and H), RF (H), SVM (H), ANN (H), NB (H) 2, 6 0.96–0.98
[64] Estimation of biomass in grasslands Simple quadratic combination (L/M) 2, 15 0.66–0.88
[40] Evapotranspiration estimation MSDF-ET (L) 1, 2, 4 0.68–0.77
[117
[
126
]
Impact of tile drainage on evapotranspiration
STARFM (L)
2
0.23–0.91
[
127
]
Estimation of leaf area index
CACAO method (L)
2
0.88
[105] Mapping winter wheat in urban region SVM (M), RF (M) 2, 6 0.98
No. Classes of Data Fusion Technique No. Classes of Data Being Fused
1
].
Table 2. RefePrences considered in this study–prooximal scale. L, M, and H mean low-, mid-, and high-level data fusion, respectively. The numbers in the fourth column are those adopted in Table 1 for each “fused data” class.
Ref. Application Fusion Technique Fused Data Mean Accuracy
[30] Estimation of soil indices SF (L), MOA (H) 7 0.80–0.90
[73] Sustainable greenhouse management Decision rules (L) 10 N/A
[72] Human—robot interaction LSTM-NN (L) 11 0.71–0.97
[25] Delineation of homogeneous zones in viticulture GAN (L), geostatistical tools (L) 2, 9 N/A
[26] Delineation of homogeneous zones Kriging and other geostatistical tools (L) 2, 9 N/A
[51] Estimation of crop phenological states Particle filter scheme (L) 2, 6, 10 0.93–0.96
[18] Fruit detection LPT (L) and fuzzy logic (L) 1, 4 0.80–0.95
[31] In-field estimation of soil properties RK (L), PLSR (L) 3, 9 >0.5
[74] Delineation of homogeneous management zones Kriging (L), Gaussian anamorphosis (L) 9, 15 0.66
[
Delineation of homogeneous management zones Kriging (L),Gaussian anamorphosis (L) 9, 15 N/A
[76] Crop nutritional status determination PCA (L) 7, 8 0.7–0.9
[22] Detection of olive quick decline syndrome CNN (M) 1 0.986
[65] Monitoring Agricultural Terraces Coregistering and information extraction (L/M) 5 N/A
[77] Prediction of canopy water content of rice BPNN (M), RF (M), PLSR (M) 2 0.98–1.00
[11] Localization of a wheeled mobile robot Dempster–Shafer (L) and Kalman filter (L) 11, 12 0.97
[19] Immature green citrus fruit detection Color-thermal probability algorithm (H) 1, 4 0.90–0.95
[28] Delineation of management zones K-means clustering (L) 2, 9, 14 N/A
[78] Segmentation for targeted application of products Discrete wavelets transform (M) 1 0.92
[12] System for agricultural vehicle positioning Kalman filter (L) 11, 12 N/A
[13] System for agricultural vehicle positioning Kalman filter (L) 11, 12 N/A
[67] Yield gap attribution in maize Empirical equations (L) 15 0.37–0.74
[32] Soil environmental quality assessment Analytic hierarchy process, weighted average (L) 15 N/A
[33] Predict soil properties PLSR (L) 7, 9, 13 0.80–0.96
[14] System for agricultural vehicle positioning Discrete Kalman filter (L) 11, 13 N/A
[34] Estimating soil macronutrients PLSR (L) 7, 9 0.70–0.95
[20] Citrus fruit detection and localization Daubechies wavelet transform (L) 1, 2 0.91
[15] Estimation of agricultural equipment roll angle Kalman filtering (L) 11 N/A
[79] Predicting toxic elements in the soil PLSR, PCA, and SPA (L/M) 7, 8 0.93–0.98
[68] Review: image fusion technology in agriculture N/A N/A N/A
[80] Heterogeneous sensor data fusion Deep multimodal encoder (L) 10 N/A
[81] Agricultural vulnerability assessments Binary relevance (L), RF (L), and XGBoost (L) 10,14 0.67–0.98
[35] Prediction of multiple soil properties ] Semantic segmentation of land typesSMLR (L), PLSR (L), PCA/SMLR combination (L) 7, 9 0.60–0.95
Majority rule (H) 2 0.99 [82] Prediction of environment variables Sparse model (L), LR (L), SVM (L), ELM (L) 10 0.96
[
[118] Eucalyptus trees identification Fuzzy information fusion (L) 2 0.98 64]
[Estimation of biomass in grasslands 10Simple quadratic combination (L) ] Review: IoT and data fusion for crop disease N/A2, 15 0.66–0.88
N/A N/A [23]
[69]Plant disease detection Kohonen self-organizing maps (M) 3, 8 0.95
Wheat yield prediction CP-ANN (M), XY-fused networks (M), SKN (M) 2, 7 0.82 [83] Water stress detection
[112]Least squares support vectors machine (M) 3, 8 0.99
Drought monitoring RF (M) 2, 15 0.29–0.77 [84] Delineation of water holding capacity zones ANN (L), MLR (L)
[48]7, 9 0.94–0.97
Crop type classification and mapping RF (L) 2, 6, 13 0.37–0.94 [85] Potential of site-specific seeding (potato)
[119PLSR (L) ] Time series data fusion2, 9 0.64–0.90
Environmental data acquisition module 10 N/A [86]
[3D characterization of fruit trees 57Pixel level mapping between the images (L) ] Evapotranspiration prediction in vineyard STARFM (L)4, 5 N/A
2 0.77–0.81 [87]
[108]Measurements of sprayer boom movements Summations of normalized measurements (L) 11 Daily NDVI product at a 30-m spatial resolution GKSFM (M)N/A
2 0.88 [10] Review: IoT and data fusion for crop disease N/A N/A N/A
[49 [88] Prediction of wheat yield and protein Canonical powered partial least-squares (L) 7, 10 0.76–0.94
] Crop classification Committee of MLPs (L) 2, 6 0.65–0.99
[6] Multisource classification of remotely sensed data Bayesian formulation (L) 2, 6 0.74 [69] Wheat yield prediction
[111CP-ANN (L), XY-fused networks (L), SKN (L) ] Fractional vegetation cover estimation2, 7 0.82
Data fusion and vegetation growth models (L) 2 0.83–0.95 [89] Topsoil clay mapping PLSR (L) and kNN (L)
[120] Land cover monitoring FARMA (L)7, 9, 13 0.94–0.96
2, 6 N/A [21]
[121]Fruit detection CNN (L); scoring system (H) 1, 2 Crop ensemble classification mosaicking (L), classifier majority voting (H)0.84
2 [37] 3D reconstruction for agriculture phenotyping Linear interpolation (L) 1, 10 N/A
0.82–0.85
[70] Review: data fusion in agricultural systems N/A N/A N/A [29] Delineation of site-specific management zones CoKriging (L)
[50]2 0.55–0.77
In-season mapping of crop type Classification tree (M) 2 0.93–0.99 [90] Orchard mapping and mobile robot localization
[122Laser data projection onto the RGB images (L) ] Building frequent landsat-like imagery1, 5 0.97
STARFM (L) 2 0.63–0.99 [24] Modelling crop disease severity 2 ANN architectures (L)
[58] Evapotranspiration mapping SADFAET (M)10, 15 0.90–0.98
2 N/A [91]
[123]Tropical soil fertility analysis SVM (L), PLS (L), least squares modeling (L) 2, 8 0.30–0.95
Temporal land use mapping Dynamic decision tree (M) 2 [92] Internet of things applied to agriculture Hydra system (L/M/H) 9, 10, 15 0.93–0.99
0.86–0.96 [70] Review: data fusion in agricultural systems N/A
[124 N/A N/A
] High-resolution leaf area index estimation STDFA (L) 2 0.98
[125] Monitoring cotton root rot ISTDFA (M) 2 0.79–0.97 [36] Soil health assessment PLSR (L) 7, 9 0.78
[
[109] Monitoring crop water content Modified STARFM (L) 2 0.44–0.85 93] Prediction of Soil Texture SMLR (L), PLSR (L) and PCA (L)
[1047, 8 ] Soil moisture content estimation Vector concatenation, followed by ANN (M)0.61–0.88 2, 6 0.39–0.93
[128] Leaf area index estimation ESTARFM (L), linear regression model (M) 2 0.37–0.95
[71] Vineyard monitoring PLSR (M), SVR (M), RFR (M), ELR (M) 2 0.98
Regression methods 1 RGB images
2 STARFM-like statistical methods 2 Multispectral images
3 Geostatistical tools 3 Hyperspectral images
4 PCA and derivatives 4 Thermal images
5 Kalman filter 5 Laser scanning
6 Machine learning 6 SAR images
7 Deep learning 7 Spectroscopy
8 Decision rules 8 Fluorescence images
9 Majority rules 9 Soil measurements
10 Model output averaging 10 Environmental/weather measurements
11 Others 11 Inertial measurements
    12 Position measurements
    13 Topographic records and elevation models
    14 Historical data
    15 Others

2. Proximal Scale

The majority of studies dedicated to the proximal scale are concentrated in three main areas: prediction of soil properties, delineation of homogeneous zones, and robotic navigation and control. Applications, such as disease and fruit detection, prediction of water content and water stress, estimation of phonological state and yield prediction, are also present. Ten of the references also explored satellite data, and five studies combined proximal and aerial data. Data sources included cameras (RGB, multispectral, thermal, hyperspectral) spectrometers, conductance and resistivity sensors, GPS, inertial sensors, weather data, among many others. With such a variety of sensors available for field applications, efforts to explore their complementarities have been steadily growing (Table 2), but most problems still lack reliable solutions[72
Another important challenge is the difficulty of obtaining/collecting reference data for validation of the techniques applied. This problem can be particularly difficult if the reference data need to be gathered in-loco. It is also important to consider that, even if reference data can be collected, differences in granularity and the positions of the sample points can make the comparison with the fused data difficult or even unfeasible[112].

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