# Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps
## 2 Literature Review
### 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 4).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Reference** & **Application** & **Fusion Technique** & **Fused Data** & **Mean Accuracy** \\ \hline
[64] \({}^{a}\) & Estimation of biomass in grasslands & Simple quadratic combination (L) & 2, 15 & 0.66–0.88 \\ \hline
[23] & Plant disease detection & [PERSON] self-organizing maps (M) & 3, 8 & 0.95 \\ \hline
[84] & Water stress detection & Least squares support vectors machine (M) & 3, 8 & 0.99 \\ \hline
[85] & Delineation of water holding capacity zones & ANN (L), MLR (L) & 7, 9 & 0.94–0.97 \\ \hline
[86] & Potential of site-specific seeding (potato) & PLSR (L) & 2, 9 & 0.64–0.90 \\ \hline
[87] & 3D characterization of fruit trees & Pixel level mapping between the images (L) & 4, 5 & N/A \\ \hline
[88] & Measurements of sprayer boom movements & Summations of normalized measurements (L) & 11 & N/A \\ \hline
[10] \({}^{a,b}\) & Review: IoT and data fusion for crop disease & N/A & N/A \\ \hline
[89] & Prediction of wheat yield and protein & Canonical powered partial least-squares (L) & 7, 10 & 0.76–0.94 \\ \hline
[90] \({}^{a}\) & Wheat yield prediction & CP-ANN (L), XY-fused networks (L), SKN (L) & 2, 7 & 0.82 \\ \hline
[90] & Topsoil clay mapping & PLSR (L) and NNN (L) & 7, 9, 13 & 0.94–0.96 \\ \hline
[21] & Fruit detection & CNN (L) scoring system (H) & 1, 2 & 0.84 \\ \hline
[37] & 3D reconstruction for agriculture phenotyping & Linear interpolation (L) & 1, 10 & N/A \\ \hline
[29] & Delineation of site-specific management zones & CoKriging (L) & 2 & 0.55–0.77 \\ \hline
[91] & Orchard mapping and mobile robot localization & Laser data projection onto the RGB images (L) & 1, 5 & 0.97 \\ \hline
[24] & Modelling crop disease severity & 2 ANN architectures (L) & 10, 15 & 0.90–0.98 \\ \hline
[92] & Tropical soil fertility analysis & SVM (L), PLS (L), least squares modeling (L) & 2, 8 & 0.30–0.95 \\ \hline
[93] & Internet of things applied to agriculture & Hydra system (L/M/H) & 9, 10, 15 & 0.93–0.99 \\ \hline
[70] \({}^{a,b}\) & Review: data fusion in agricultural systems & N/A & N/A & N/A \\ \hline
[36] & Soil health assessment & PLSR (L) & 7, 9 & 0.78 \\ \hline
[94] & Prediction of Soil Texture & SMIR (L), PLSR (L) and PCA (L) & 7, 8 & 0.61–0.88 \\ \hline
[95] & Rapid determination of soil class & Outer product analysis (L) & 7 & 0.65 \\ \hline
[16] & Navigation of autonomous vehicle & MSPI algorithm with Bayesian estimator (L) & 11, 12 & N/A \\ \hline
[38] \({}^{b}\) & Detection of cotton plants & Discriminant analysis (M) & 2, 7 & 0.97 \\ \hline
[96] & Map-based variable-rate manure application & K-means clustering (L) & 2, 9 & 0.60–0.93 \\ \hline
[17] & Navigation of autonomous vehicles & Kalman filter (L) & 11, 12 & N/A \\ \hline
[97] & Robust tomato recognition for robotic harvesting & Wavelet transform (L) & 1 & 0.93 \\ \hline
[98] & Navigation of autonomous vehicle & Self-adaptive PCA, dynamic time warping (L) & 1, 11 & N/A \\ \hline
[99] & Recognition of wheat spikes & Gram-Schmidt fusion algorithm (L) & 1, 2 & 0.60–0.79 \\ \hline \hline \end{tabular} \({}^{a}\) Also explores satellite data. \({}^{b}\) Also explores aerial data.  
\end{table}
Table 3: _Cont._