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precision-agriculture

precision agriculture

John Deere

SystemTechniques · 12Primitives · 33 precision-agriculture Kriging spatial interpolationgeometry · medium: Variogram-based kriging and IDW to interpolate sampled field values onto gridsObject detectionmachine-learning · medium: A network turns an image into scored, classified boxes in one pass; anchor-based, anchor-free and set-prediction heads differ in how they assign labels and remove duplicates.›Coverage path planningrobotics · medium: Boustrophedon and cell-decomposition paths that cover a field or area with minimal overlapModel predictive controlrobotics · medium: Each tick, solve a small optimization over a predicted horizon, apply only the first input, repeat. Handles constraints and multiple inputs that a PID loop cannot.›Rtk ambiguity resolutionsignal-processing · medium: RTK carrier-phase differencing and integer ambiguity resolution (LAMBDA) for cm positioningPolygon clippinggeometry · medium: Polygon intersection/union/difference (Weiler-Atherton, Martinez) for spatial predicatesQuadratic programmingoptimization · medium: Convex QP solvers (active-set, interior-point, ADMM) for constrained optimizationConvolutional networksml-training · low: Layers that slide a small shared kernel over a grid, giving translation equivariance and few parameters; still the most compute-efficient choice for many vision tasks.›Extended Kalman filterrobotics · low: Kalman filter for nonlinear motion and measurement models: linearize f and h with Jacobians at the current estimate each step. The default estimator in robot navigation.›Point in polygongeometry · low: Ray-casting/winding tests and indexed polygon lookup for reverse geocodingCan bus framingembedded · low: CAN / CAN FD frames, bitwise arbitration, bit stuffing, CRC and error confinementGeodesic distance (haversine, Vince…geometry · low: Shortest-path distance between two lat/lon points on the Earth: haversine on a sphere (about 0.5% error), Vincenty or Karney on the WGS84 ellipsoid (sub-millimetre).›R-treegeometry · low: A balanced tree of nested bounding boxes that indexes rectangles and points, answering window and nearest-neighbour queries by pruning whole subtrees.›Image warping remapgraphics · low: Resampling with lookup maps: resize, lens undistortion, homographies, bilinear/bicubicWeight quantizationml-training · low: Store weights as 8/4-bit integers with per-group scales; decode is memory-bound, so 4-bit weights give about 3-4x tokens/s and fit 4x larger models.›Pure pursuit path trackingrobotics · low: Geometric path following by steering to a look-ahead point (pure pursuit, L1, Stanley)k-d treegeometry · low: A binary tree that splits k-dimensional points by one coordinate per level, giving fast nearest-neighbour and box queries on static low-dimensional data.›Gaussian processesmachine-learning · low: GP regression with kernels: posterior mean/variance via Cholesky, hyperparameters by marginal likelihoodLU and Cholesky factorizationnumerics · low: Factor A = PLU (any square matrix, partial pivoting) or A = LLᵀ (SPD) once in O(n³), then solve each right-hand side in O(n²).›Data augmentationmachine-learning · low: Generate label-preserving variants of each training example on the fly to enlarge the effective dataset and bake in invariances.›Loss functionsmachine-learning · low: The scalar a model minimizes: cross-entropy for classes and tokens, MSE or Huber for values, each with a stable form and simple gradient.›Non-maximum suppressionmachine-learning · low: Keep the best-scoring candidate in each neighbourhood and drop the rest; greedy NMS, Soft-NMS and 1-D peak picking share one loop.›A* searchgraphs · low: Dijkstra ordered by cost-so-far plus an admissible estimate of the remaining cost; optimal paths while exploring far fewer nodes toward the goal.›+ 5 more techniques+ 17 more primitives

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