Barkley & Son Drone Works operates with Pix4Dfields, the ultimate drone software platform for the agriculture industry. Our services are for growers, agronomists, and ag professionals that manage larger, more challenging fields, vineyards and orchards that need to make quick decisions based on real data but do not have the time or resources to manually walk their entire field. Our customers need to count plants, detect diseases, problem areas, and field stresses. Pix4Dfields provides advanced, ag-focused analytics tools to give ag professionals a comprehensive look at stand loss, weed pressure, and sowing quality. With the power of Pix4Dfields and the multispectral imaging hardware we incorporate, Barkley & Son can help growers make data-driven decisions that take the guesswork out of late-season course corrections.

• Field Intelligence: spot variations and problem areas with NDVI, VARI and more

• Actionable Information: Minimize crop loss and boost yield with rapid intervention

• Real-Time Insights: Crop scout and ground truth without leaving the field

• Ag Processing Engine: Image stitching solution to map homogeneous sections of farmland

• Services Offered: Depending on your needs, we can custom tailor a single mapping or a year-round schedule

 

 

 

 

Capture

I collect hundreds of images with my DJI UAV and MicaSense RedEdge multispectral camera.

 

 

 

 

Process

I process imagery with a new, industry-leading instant processing engine and produce your maps 10x faster.

 

 

 

 

Scout

I can generate orthomosaic maps, digital surface models, index maps, zones and prescription maps. I am able to trim your fields to the desired field boundary to create more targeted outputs.

 

 

 

 

Analyze

We visualize and understand your crop stages and stress levels. We compare different layers of information for a full insight into your crop performance.

 

 

 

 

Integrate

I am able to download all outputs and import into the Farming Management Platform of your choice in various industry standard formats or present in a stand-alone action report.

.

processing_results-Copy

 

NDRE (Normalized Difference Red Edge)

Uses:

  • leaf chlorophyll content
  • plant vigor
  • stress detection
  • fertilizer demand
  • Nitrogen uptake

 

Description:

NDRE is an index that can only be formulated when the Red-edge band is available in a sensor. It is sensitive to chlorophyll content in leaves (how green a leaf appears), 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.

Chlorophyll has maximum absorption in the red waveband and therefore red light does not penetrate very far past a few leaf layers. On the other hand, light in the green and red-edge edge can penetrate a leaf much more deeply than blue or red light so a pure red-edge waveband will be more sensitive to medium to high levels of chlorophyll content, and hence leaf nitrogen, than a broad waveband that encompasses blue light, red light, or a mixture of visible and NIR light (e.g. a modified single-imager camera).

NDRE is a better indicator of vegetation health/vigor than NDVI for mid to late season crops that have accumulated high levels of chlorophyll in their leaves because red-edge light is more translucent to leaves than red light and so it is less likely to be completely absorbed by a canopy. It is more suitable than NDVI for intensive management applications throughout the growing season because NDVI often loses sensitivity after plants accumulate a critical level of leaf cover or chlorophyll content.

 

NDVI (Normalized Difference Vegetation Index)

Uses:

  • plant vigor
  • differences in soil water availability
  • foliar nutrient content (when water is not limiting)
  • yield potential

 

Summary:

As plants become healthier, the intensity of reflectance increases in the NIR and decreases in the Red, which is the physical basis for most vegetation indices. NDVI values can be a maximum value of 1, with lower values indicating lower plant vigor. Therefore, 0.5 typically indicates low vigor whereas 0.9 indicates very high vigor. NDVI is also effective for distinguishing vegetation from the soil. NDVI is recommended when looking for differences in above-ground biomass in time or across space. NDVI is most effective at portraying variation in canopy density during early and mid-development stages but tends to lose sensitivity at high levels of canopy density.

 

Chlorophyll Map

Uses:

  • detect chlorotic crops
  • stress detection
  • identify vigorous, healthy crops
  • estimate chlorophyll content
  • estimate N content if you know that N is limiting

 

Description:

The Chlorophyll Map is a layer that is less sensitive to leaf area than NDRE. This layer isolates the chlorophyll signal from variability in leaf area as a function of changes in canopy cover. It has a physiological basis which takes into account the relationship between canopy cover and canopy nutrient content.

The Chlorophyll Map is especially sensitive to well gathered and well-calibrated data. Non-plant pixels are excluded and shown as transparent, which in some cases results in plant pixels also being omitted. This layer is less useful for row crops and more useful for vineyards and orchards, as the dense canopy is better at differentiating the Chlorophyll signal.

 

OSAVI (Optimized Soil-Adjusted Vegetation Index)

Uses:

  • differentiate soil pixels
  • related to LAI at some levels where NDVI saturates
  • accounts for non-linear interactions of light between soil and vegetation
  • used as a structural index for some combined indices designed for chlorophyll detection

 

Description:

OSAVI maps variability in canopy density. In addition, it is not sensitive to soil brightness (when different soil types are present). It is robust to variability in soil brightness and has enhanced sensitivity to vegetation cover greater than 50%. This index is best used in areas with relatively sparse vegetation where the soil is visible through the canopy and where NDVI saturates (high plant density).

OSAVI is a special case of the Soil Adjusted Vegetation Index (SAVI). OSAVI was developed by Rondeaux et al. in 1996 using the reflectance in the near-infrared (NIR) and red (r) bands with an optimized soil adjustment coefficient. The soil adjustment coefficient (0.16) was selected as the optimal value to minimize NDVI’s sensitivity to variation in soil background under a wide range of environmental conditions. OSAVI is a hybrid between ratio-based indices such as NDVI and orthogonal indices such as PVI. SAVI has a default soil-adjustment factor of 0.5; however, it is recommended to use 0.16 as implemented in OSAVI. Like any normalized difference index, OSAVI values can range from -1 to 1. High OSAVI values indicate denser, healthier vegetation whereas lower values indicate less vigor.

 

CIR Composite (Color Infrared)

Uses:

  • assessing plant health
  • identifying water bodies
  • variability in soil moisture
  • assessing soil composition

 

Description:

This layer is a color composite and not an Index. It is referred to as a Color Infrared Composite because instead of combining Red, Green, and Blue bands (which is the standard image display method you are accustomed to) we are combining NIR, Red, and Green bands. NIR light is displayed as red, red light is displayed as green, and green light is displayed as blue (R: NIR, G: RED, B: GREEN). This color composite highlights the response of the Near-infrared band to crop health and water bodies.

Healthy vegetation reflects a high level of NIR and appears red in CIR layers. Unhealthy vegetation will reflect less in the NIR and appear as washed out pink tones, very sick or dormant vegetation is often green or tan, and man-made structures are light blue-green. Soils may also appear light blue, green, or tan depending on how sandy it is, with the sandiest soil appearing light tan and clay soils as dark tan or bluish green. This is also highly useful in identifying water bodies in the imagery, which absorb NIR wavelengths and appear black when water is clear. Since this is not an index, as stated above, there is no color palette to select. The colors you see are a result of an additive mixture of NIR, Red, and Green wavelengths at each image pixel.

 

DSM (Digital Surface Model)

Uses:

  • estimate relative crop volume
  • identify surface properties
  • model water flow & accumulation

 

Description:

DSM is a digital model representation of a terrain’s surface. DSM represents the elevations above sea level of the ground and all features on it. A DSM is a gridded array of elevations. it is a layer symbolized by a gray color ramp, special effects such as hill-shading may be used to simulate relief. DSMs can be used to study surface properties and water flow.

A digital surface model (DSM) is usually constructed using automatic extraction algorithms (i.e. image correlation in stereophotogrammetry). DSM resembles laying a blanket on your imagery. It represents top faces of all objects on the terrain, including vegetation and man-made features, and highlights the different elevations of the features

Typical Datasets provided to clients include:

Tif Files – These can be viewed in open source GIS software like QGIS if you want to apply your own color scheme.

PDF File – An example of these are shown below, NDVI, NDRE & elevation maps for future planning.

KML/KMZ File – Overlay for google earth.