Agriculture

Benefits to Agriculture

The increasing commercial and regulatory demand placed on agricultural land represents a challenge to farmers. Being faced with the need to maximise the return fromscarce arable land while minimising the level of consumables applied is an ongoing challenge. The use of Unmanned Aerial Systems, in addition to providing the full range of Mapping, Inspection and Search functions , facilitates a new era of Precision Agriculture.

UAVs are playing a key role in improving the efficiency of large farming operations and multispectral sensors are a clear example. The data captured by drone-based multispectral sensors can make operations more efficient by providing information that allows for better use of resources and more localized applications.

Leveraging Multispectral sensors farmers can:

Map Vegetation Health using Vegetation Indices such as Normalised Difference Vegetation Index, Optimised Soil Adjusted Vegetation Index and the Normalised Difference Red Edge.

Detect Disease by using Multispectral Sensors to measure small changes in crops over time.

Determine the Adequacy of Irrigation and Water Management strategies.

Classify Plants and Differentiate Species; using size, species, chlorophyll content, and canopy closure.

Count Plants using back office analytics fed from captured multispectral data.

Determine Crop Damage and assist with preparing Insurance claims.

Detect and Map Weed infestation.

Understand Drainage Patterns across fields using the Normalised Difference Red Edge Vegetation Index (NDVI) in conjunction with terrain modelling.

Reduce the Cost of Labour by capturing full field information rather than having to walk fields for the same intelligence.

Monitor Fertiliser Requirements and reduce crop cost by focusing on Precision Agriculture techniques to apply only what is necessary.

Perform Analysis Over Time producing artefacts which facilitate analytical workflows rather than relying on memory.

Monitor and Detect Nutrient Deficiencies before they impact crop value.

EMLID RS2 GNSS Survey Equipment

Micasense Altum-PT Multispectral Camera

Precision Agriculture

CSIRO presents the challenge of Precision Agriculture as a "Need to produce more with less". Whether it is determining the volume of pumpkins that you will need to transport to market, minimising herbicide and pesticide usage by accurately identifying areas of poor growth, or mapping drainage across fields, UAV technology is able to assist and PHIRE-1 Solutions is able to help you on that journey.

Our Integrated approach is tailored to your specific needs. Typically :

We start by Building Maps of the relevant fields using Orthomosaic photography leveraging the P1 Camera or in cases where there is significant Vegetation, the L1 Lidar.
From that information we will map out the specific field locations and enter that information into our Geospatial Information System along with a Digital Elevation Model We will use the DEM when flying the fields in future to ensure maximum accuracy of data captured. From the DEM we can generate Topographic Maps should they be required or Perform Drainage Analysis.
Determine the set of Vegetation Indices that are needed to achieve your outcomes.
Fly our Multispectral Sensors over the relevant fields to capture vegetation performance information in Near Infra Red and Red Edge.
Generate Vegetation Indices to meet your field use needs and to map areas of poor performance.
Perform Ground Truthing of areas of poor performance to confirm our assessment.
Perform Vegetation Remediation by the selective use of herbicides, pesticides or fertilisers etc to only those areas that require remediation. For this activity we will leverag a spray drone such as the DJI Agras Range.
Repeat the multispectral and analytics cycles as required over time to track progress.

Modelling Land Surfaces

A Digital Elevation Model (DEM) Represents the bare earth surface, removing all natural and built features. Because DSMs represent the bare-Earth without any ground features, their use is widely applied in fields such as agricultural land planning, soil science, hydrology and flow-direction studies.
A Digital Surface Model (DSM) captures both the natural and built/artificial features of the environment, so it includes the tops of trees and building. Because DSMs represent the bare-Earth and all of its above-ground features, their use is widely applied in fields such as urban planning, power line corridor inspections and aviation planning.
A Digital Terrain Model (DTM) typically augments a DEM, by including vector features of the natural terrain, such as rivers and ridges. A DTM may be interpolated to generate a DEM, but not vice versa.
A Canopy Height Model (CHM) is the difference between the DSM and the DEM where the delta is purely vegetation.

Vegetation Indices

NDVI - Normalised Difference Vegetation Index

NDVI is used to measure the difference between visible and near-infrared light reflectance from crops and other vegetation to provide a standardised view of the strength of photosynthetic activity.

NDRE - Normalised Difference Red Edge

NDRE is sensitive to chlorophyll content in leaves, variability in leaf area, and soil background effects. High values of NDRE represent higher levels of leaf chlorophyll content than lower values. Soil typically has the lowest values, unhealthy plants have intermediate values, and healthy plants have the highest values. Consider using NDRE if you are interested in mapping variability in fertilizer requirements or foliar Nitrogen, not necessarily Nitrogen availability in the soil.

OSAVI - Optimised Soil-Adjusted VegetationIndex

OSAVI is a variant of the Soil Adjusted Vegetation Index (SAVI). OSAVI has a soil-adjustment factor of 0.16; which results in an improved index for use where soils are highly reflective of Near Infra-Red light (For example, soils that are high in silica). OSAVI is particularly useful for stand counting of young trees in areas where there is significant soil exposure.

TGI – Triangular Greenness Index

RGB index for chlorophyll sensitivity. TGI index relies on reflectance values at visible wavelengths. It is a good proxy for chlorophyll content in areas of high leaf cover.

VARI – Visible Atmospherically Resistant Index

RGB index for leaf coverage. This index is used to estimate the fraction of vegetation in an image with low sensitivity to atmospheric effects.

BNDVI – Blue Normalized Difference Vegetation Index

BNDVI is an index without red channel availability that uses the visible blue, for areas sensitive to chlorophyll content.

GNDVI – Green Normalized Difference Vegetation Index

GNDVI index uses visible green instead of visible red and near infrared. Useful for measuring rates of photosynthesis and monitoring the plant stress.

MCARI – Modified Chlorophyll Absorption in Reflective Index

MCARI is an index used to measure chlorophyll concentrations including variations in the Leaf Area Index.

Masked Custom Indices

We can construct custom index masks for Soil or Vegetation from our maps for more focused results. These can also be used for counting specific items such as plants, trees, fruit etc.