Multispectral Imagery
Precision Ag Multsipectral imagery
Mr Grower,
Georgia GEO is grateful for the opportunity to offer to the Middle Georgia Farmer the newest and most exciting Precision Ag process available. It is our goal to help the farm and farmers produce the best yields possible.
To help our clients derive the maximum understanding and benefit of this data, this detailed explanation accompanies your report(s).
Much of the data which will be presented is color-coded giving the grower instant identification of areas which are thriving and which areas are experiencing difficulty. Red is bad. Green is good. A color legend is included on each page giving the significance of all other colors representing present plant states or conditions. For example, BLUE in a Plant Size Survey means plants are doing exceptionally well.
The analysis presented in the Georgia GEO SlantView Statistics Report is divided into 3 broad categories (listed below) featuring several types of data, depending on the analysis requested. One or more categories may be present in each report.
These 3 categories are:
- Population Data Set, or “Population Index”
- Stress Data Set, or “Stress Analysis” and
- Vegetation Fraction Data Set, or “Canopy Analysis”
This is where the fun begins!
Population Data Set
Our clients tell us that this important information about early season emergence and population density is the most valuable to the farmer. Issues can be resolved, adjustments can be made, and expectations cemented. Here, early in the growing season, is where a significant return-of-investment (ROI) can be achieved.
A Plant Population Data Set incudes the following sections:
- Plant Population Count
- Plant Size Survey
- Weed Detection
Plant Population Count: Georgia GEO can detect individual plants when they are immediately post-emergent, calculate actual plant counts, plant density per unit area and reveal where gaps may exist in rows.
Georgia GEO’s SlantRange multispectral camera detects EACH PLANT in the field (as a result of our very low flight altitudes) and renders a precise measurement of the crop stand.
Georgia GEO can accurately show plant counts per acre or per field for comparison to grower’s expectations. The data also reveals areas where planter equipment, seed size variation, etc may have caused malfunctions resulting in skipped rows or unplanted areas. The data also identifies areas of the crop, which may have not emerged.
The Takeaway:
Skipped or bare areas can be identified for replanting before the rest of the field is inaccessible. Observed plant counts can be compared to pre-planting estimates and later-season per acre yield expectations.
Plant Size Survey: SlantRange can resolve plant heights to within ¼” of each other.
Growers tell us plant height is the single most important bit of information they can use early on in the growing season, while the plants are still “individuals.” The Plant Size Surveys identifies plants in the 75% percentile as the standard size from which all plants in the survey are compared. This data gives the grower information regarding lagging growth rates as well as the identification of areas exhibiting exceptional growth rates.
The Takeaway:
Lagging plant growth can be addressed while plant size still allows access to the field for remedy. Soil sampling or other considerations can help identify factors in the “good areas” which are resulting in accelerated growth rates.
Weed Detection: We can identify weeds in row crops as well as grain fields. If Georgia GEO imagery detects a plant outside of established rows (even within double rows and skip-rows) it will be considered a weed. Growers can use weed information to attack the weed infestations as soon as practical. Georgia GEO can also detect weeds in grain stands through the identification of even a small sample weed patch. We can instruct our computer to search the imagery for the same spectral signature as the offending weed to reveal the extent of the weed intrusion, even if the weed is sporadic throughout the field.
The Takeaway:
Weed problems can be identified for eradication even in close-row grain fields.
Stress Data Set
As plants mature, information regarding plant health and photosynthetic processes can be determined. The reflection of sunlight, as well as the absence of reflected sunlight are termed “Reflectance Index” and together, can tell us a great deal.
A Stress Data Set or “Stress Analysis” includes the following sections:
- Green NDVI
- Red NDVI
- Red Edge NDVI
- Stress
- Chlorophyll Content (Index)
Green NDVI: A measure of the average biomass of each plant.
The more robust a plant’s leaf structure, the more it is revealed in Green NDVI. Greater amounts of plant matter should translate to more chlorophyll, more photosynthesis, and therefore, a more productive plant.
The takeaway:
If lower average plant biomass is indicated, plant development may be compromised.
Red NDVI: A measure of the chlorophyll present in the plants.
Since chlorophyll absorbs light in the Red spectrum of sunlight, we can compare the amount of sunlight available vs. how much of the sun’s red light is being absorbed by the plant wherein an indirect measure of chlorophyll content can be calculated. If chlorophyll concentration decreases, less light in the red region is absorbed.
The takeaway:
This could indicate an N2, or other, nutrient deficiency.
Red Edge NDVI: A measure of invisible near-infra-red (NIR), or Red Edge light reflected by the internal cellular structure of the leaf.
Healthy leaves with healthy cellular structures reflect this invisible light, much like an automobile’s reflector. If a plant is unhealthy, the cellular structure begins to break down and alter, reflecting less NIR light. In the same way that an oil slick on water reflects light in differing colors, so too, does a leaf. By analyzing NIR, an estimation of the integrity of the internal leaf cellular structure can be estimated.
The takeaway:
A compromised internal leaf structure could be due to the effects of Pathogens, Pests, or Irrigation issues.
Stress: Red Edge reflectance values are detected by the multispectral camera and used by the SlantRange Software to compute stress levels.
As described above, healthy leaves reflect Red Edge light like an automotive reflector. The software assigns a mathematical value to the reflectance and presents it as a color-coded image. The level of stress is directly related to the light being reflected by the leaves. LESS Red Edge light reflected=MORE stress.
The takeaway:
Stress can be detected by the SlantRange Sensor earlier than that which eventually becomes apparent to the human eye. A “Ground Truth” observer can be dispatched quickly to the precise spot in the field to determine the actual cause of the stress.
Chlorophyll Index: A measure of the amount of chlorophyll present in a stand.
Chlorophyll Index (also known as Canopy Chlorophyll Content Index, or CCCI) is directly related to nutrient availability and uptake, which affects the plant’s ability to photosynthesize. These values can be used in a variable-rate application program to generate rates throughout the fields.
The takeaway:
A low chlorophyll index means there is a lack of Nitrogen or some mechanism exists which prevents nutrient uptake.
The combination of these “reflectance indexes” can tell the grower: 1) the maturity of the leaves in the field; 2) the quantity of chlorophyll present in those leaves; and 3) the quality of the leaves in the field.
Vegetation Fraction Data Set
Vegetation Fraction and Yield Potential analysis
Vegetation Fraction: A measure of the area of ground compared with how much leaf matter obscures the ground.
In the early part of the growing season, ground is exposed between young plants. As the growing season progresses, growing plants create more foliage which obscures the ground and the vegetation fraction (the ratio of leaf vs. soil) increases. As more aerial surveys are taken over time, a grower can compare the growth rate and foliage properties of a crop stand against expectations. Also referred to as “canopy closure.”
The takeaway:
If growth in not within normal ranges, intervention may be necessary to determine the cause of lack of plant vigor.
Yield Potential: A compilation of all the factors observed by the multispectral sensor to render an estimation of yield as the crop presently exists.
In-field trials by the SlantRange Staff have verified that the accuracy of yield estimates from the software is very close to the actual yield achieved by the grower at harvest.
The takeaway:
Yield estimates can provide growers with profit expectations.
We at Georgia GEO see this as an integral partnership. Now that we have introduced you to the core subject matter related to reading and understanding multispectral imagery analysis we have the opportunity to communicate effectively. This information gives both Grower and Georgia GEO the ability to: 1) verify what you have observed in the field, or 2) alert you to previously unknown conditions which can now be addressed. As time goes on, we look forward to tracking this trend data and updating your success.