RSIP Vision creates forestry image processing and analysis software by using aerial images taken by drone, plane or satellite. Our algorithms enable to efficiently determine:

  • Planted rows detection

Planted row detection is performed automatically for straight and curved rows. The detected rows is used in many applications. In case of trees with connected canopies (crowns), rows detection may be a substitute for stock evaluation based on tree detection – In this case a typical even planting distance is used to derive the number of trees.

row of trees

Row detection

Row detection

  • Special purpose rows detection

    A forest may have special purpose rows. Such rows may be planted or un-planted. The automatic detection here gives the extra information. For example, special purpose rows are thinned rows, unplanted rows over debris leftovers and more. When un-planted rows are detected, the missing trees from such empty rows are subtract from the total – giving accurate figure.

Rows without trees

Rows without trees detection - due to debris leftovers

Rows without trees detection – due to debris leftovers

  • Non planted row segments detection (Stocked and un-stocked)

    In the cases where rows are used to evaluate the tree amount (stock evaluation). It is important to detect, in advance, non-planted row segments. Such row segments, without trees, are defined as Un-stocked. Stocked segments, on the other hand are populated with trees. The total trees amount is the sum of the stocked segments (Taking into account even planting distance).

Un-stocked rows

Un-stocked rows detection (Inside yellow polygons)

Un-stocked rows detection (inside yellow polygons). Detected stocked rows in blue

  • Forest maintenance objects related – detection and planning

    One of the major objective when using aerial imaging is provide an accurate stock figure. Forest maintenance operation may affect rows (and their trees) partly or fully. RSIP Vision can detect missing rows (Thinned rows) and rows with missing trees gaps (Outcome of Skidding ways). In addition we can plan and optimize such operations.

  • Yield estimation from rows

    At any time, RSIP Vision fast and accurate algorithms and methods for row detection can derive the yield from an aerial image.

  • Supporting RGB and CIR images for row detection

    RSIP Vision algorithms can work with any set or subset of gray and color channels.

Natural forest segment and rows

Natural forest segment and rows detected out of CIR image

Natural forest segment and rows detected out of CIR image

Automatic detection and recognition in forestry enables a more reliable decision making, saving time and costs, particularly in hard-to-reach areas. RSIP Vision is the expert in this domain and can help you optimize all forest management decisions.

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