# Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps
## 2 Literature Review
### Proximal Scale
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Reference** & **Application** & **Fusion Technique** & **Fused Data** & **Mean Accuracy** \\ \hline \hline \([30]\) & Estimation of soil indices & SF (L), MOA (H) & 7 & 0.80-0.90 \\ \hline \([74]\) & Sustainable greenhouse management & Decision rules (L) & 10 & N/A \\ \hline \([73]\) & Human—robot interaction & LSTM-NN (L) & 11 & 0.71-0.97 \\ \hline \([25]\) & 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 \\ \hline \([51]\)\({}^{*}\) & Estimation of crop phenological states & Particle filter scheme (L) & 2, 6, 10 & 0.93-0.96 \\ \hline \([18]\) & Fruit detection & LPT (L) and fuzzy logic (L) & 1, 4 & 0.80-0.95 \\ \hline \([31]\)\({}^{*}\) & In-field estimation of soil properties & RK (L), PLSR (L) & 3, 9 & >0.5 \\ \hline \([75]\) & Delineation of homogeneous management zones & Kriging (L), Gaussian anamorphosis (L) & 9, 15 & 0.66 \\ \hline \([76]\) & Delineation of homogeneous management zones & Kriging (L), Gaussian anamorphosis (L) & 9, 15 & N/A \\ \hline \([27]\) & Delineation of homogeneous management zones & Kriging (L), Gaussian anamorphosis (L) & 9, 15 & N/A \\ \hline \([77]\) & Crop nutritional status determination & PCA (L) & 7, 8 & 0.7-0.9 \\ \hline \([22]\) & Detection of olive quick decline syndrome & CNN (M) & 1 & 0.986 \\ \hline \([65]\)\({}^{*}\) & Monitoring Agricultural Terraces & Coregistering and information extraction (L/M) & 5 & N/A \\ \hline \([78]\) & Prediction of canopy water content of rice & BPNN (M), RF (M), PLSR (M) & 2 & 0.98-1.00 \\ \hline \([11]\) & Localization of a wheeled mobile robot & Dempster-Shafer (L) and Kalman filter (L) & 11, 12 & 0.97 \\ \hline \([19]\) & Immature green citrus fruit detection & Color-thermal probability algorithm (H) & 1, 4 & 0.90-0.95 \\ \hline \([28]\)\({}^{*}\) & Delineation of management zones & K-means clustering (L) & 2, 9, 14 & N/A \\ \hline \([79]\) & Segmentation for targeted application of products & Discrete wavelets transform (M) & 1 & 0.92 \\ \hline \([12]\) & System for agricultural vehicle positioning & Kalman filter (L) & 11, 12 & N/A \\ \hline \([13]\) & System for agricultural vehicle positioning & Kalman filter (L) & 11, 12 & N/A \\ \hline \([67]\)\({}^{*}\) & Yield gap attribution in maize & Empirical equations (L) & 15 & 0.37-0.74 \\ \hline \([32]\) & Soil environmental quality assessment & Analytic hierarchy process, weighted average (L) & 15 & N/A \\ \hline \([33]\) & Predict soil properties & PLSR (L) & 7, 9, 13 & 0.80-0.96 \\ \hline \([14]\) & System for agricultural vehicle positioning & Discrete Kalman filter (L) & 11, 13 & N/A \\ \hline \([34]\) & Estimating soil macronutrients & PLSR (L) & 7, 9 & 0.70-0.95 \\ \hline \([20]\) & Citrus fruit detection and localization & Daubechies wavelet transform (L) & 1, 2 & 0.91 \\ \hline \([15]\) & Estimation of agricultural equipment roll angle & Kalman filtering (L) & 11 & N/A \\ \hline \([80]\) & Predicting toxic elements in the soil & PLSR, PCA, and SPA (L/M) & 7, 8 & 0.93-0.98 \\ \hline \([68]\)\({}^{*}\) & Review: image fusion technology in agriculture & N/A & N/A \\ \hline \([81]\) & Heterogeneous sensor data fusion & Deep multimodal encoder (L) & 10 & N/A \\ \hline \([82]\) & Agricultural vulnerability assessments & Binary relevance (L), RF (L), and XGBost (L) & 10, 14 & 0.67–0.98 \\ \hline \([35]\) & Prediction of multiple soil properties & SMLR (L), PLSR (L), PCA/SMLR combination (L) & 7, 9 & 0.60-0.95 \\ \hline \([83]\) & Prediction of environment variables & Sparse model (L), LR (L), SVM (L), ELM (L) & 10 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: References considered in this study–proximal 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 2 for each “fused data” class.