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Using Multispectral Data to Enhance Early-Stage Crop Management with the Sentera 6X Multispectral Sensor

November 19th, 2024

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The IF800 Tomcat equipped with the Sentera 6X Multispectral sensor preparing to take off on its autonomous mission
The IF800 Tomcat equipped with the Sentera 6X Multispectral sensor preparing to take off on its autonomous mission

Using Multispectral Data to Enhance Early-Stage Crop Management with the Sentera 6X Multispectral Sensor

Overview
In modern precision agriculture, timely and accurate data is critical to ensure crop health and to optimize resource use. Multispectral imaging, combined with advanced analytics, provides farmers with insights into crop conditions that would be difficult to obtain through traditional means. This white paper outlines the results from a drone flight mission over an agricultural field of baby lettuce, utilizing the Sentera 6X Multispectral sensor to assess chlorophyll content and overall plant health. The flight covered 8.47 acres and lasted just over 7 minutes, delivering actionable data to improve crop management practices.

Field Mission Details

The mission was conducted to capture data early in the lettuce crop’s growth cycle. By utilizing multispectral imaging, particularly focusing on the Chlorophyll Index Green (CIG), we were able to gain an in-depth understanding of the crop's health, providing valuable insights into chlorophyll content, nitrogen levels, and early signs of stress.

Orthomosaic of the surveyed field of baby lettuce
Orthomosaic of the surveyed field of baby lettuce
Closeup view captured by the 20 MP RGB camera on the 6X Multispectral, revealing clear visual detail across crop rows. This high-resolution image enables farmers to detect subtle variations and spot areas with gaps or irregular growth that might be missed during traditional scouting due to the field’s large scale.

Using the 6X Multispectral’s RGB imagery, customers gain an aerial perspective that makes it easier to identify issues like plant gaps, uneven crop development, and other areas of concern across expansive fields. This capability allows for more precise field management and timely interventions that might be challenging to spot from ground level.

Understanding the Chlorophyll Index Green (CIG)

The Chlorophyll Index Green (CIG) is a key metric in multispectral imaging that focuses on comparing the near-infrared and green band of light to provide insights into chlorophyll levels in plants. Chlorophyll is essential for photosynthesis, and its concentration reflects a plant’s ability to produce energy and grow. The CIG is particularly effective in assessing early-stage crops, where green reflectance is a more sensitive indicator of chlorophyll content.

Key Benefits of CIG Data:

CIG heat map indicating plant health

Did you know? Even though red and purple pigments in some lettuce varieties mask the green chlorophyll, indices like the Chlorophyll Index Green (CIG) can still detect underlying chlorophyll levels, providing valuable insights into plant health.

NDVI: Evaluating Crop Health

The Normalized Difference Vegetation Index (NDVI) is a powerful tool for assessing plant health across agricultural fields. NDVI compares the amount of reflected light in the near-infrared band to the amount of red light that is absorbed by the plant. When more near-infrared light is reflected by the plant than red light, it indicates a healthier plant, and thus a higher NDVI value.

NDVI Crop health
NDVI Crop health

In our recent mission over Babe Farms, covering 8.47 acres, the NDVI data showed a field with nearly all plants displaying as healthy. However, NDVI can be used to estimate more than just plant health. It can be used to estimate the amount of healthy biomass area. This can be accomplished by reducing the binning of the NDVI histogram to 1 and by adjusting the displayed NDVI value range from 0 to 1. This will effectively create a masking of the area that only displays only the vegetation. Then that area can be summed, which gives the area of biomass. In this data set the covered area was approximately 8.47 acres and the NDVI biomass analysis shows that 5.72 acres is populated with plants displaying an NDVI value of 0 or greater.

Using NDVI to quantify healthy vegetation
Using NDVI to quantify healthy vegetation


This indicates a strong overall health status for the plants in this region, as well as the biomass that can be harvested, underscoring the effectiveness of our monitoring efforts.

The NDVI scale allows us to quantify vegetation health based on reflectance data captured by the Sentera 6X Multispectral sensor. Higher NDVI values correlate with denser, healthier vegetation, making it an essential metric for precision agriculture. This data enables farmers to make informed decisions about resource allocation, pest control, and fertilization practices, ultimately optimizing crop yields and sustainability.

Data Analysis and Results

Flight Data Summary

During the flight, we captured high-resolution multispectral imagery using the Sentera 6X Multispectral sensor. The data was processed to create a CIG index map, which was then overlaid onto the RGB mosaic of the field. The results provided a clear understanding of the crop’s health across the entire 8.47 acres.

Chlorophyll Distribution

The overlay of the multispectral imagery with the RGB mosaic revealed patterns of variability within the field, showing clear distinctions between thriving and stressed sections of the crop. This data was invaluable in guiding on-the-ground decision-making for resource application, such as fertilizer or water, and for identifying areas that may require pest or disease management.

Why the IF800 Tomcat is the Ideal Drone for Flying the Sentera 6X Multispectral Sensor

The IF800 Tomcat is a trusted drone platform that excels in demanding professional applications, making it the perfect choice for deploying the Sentera 6X Multispectral sensor. Its lightweight and robust design ensures exceptional flight stability and versatility for various agricultural missions.

Key Advantages of the IF800 for Multispectral Imaging:

The IF800 Tomcat seamlessly integrates with the Sentera 6X Multispectral sensor, delivering the speed, reliability, and payload capacity necessary for effective agricultural missions. This powerful combination empowers operators to obtain high-quality, actionable insights that significantly enhance crop management practices.

Applications in Precision Agriculture

The data collected from this mission demonstrates how multispectral imaging can be an essential tool in precision agriculture. By providing early detection of potential issues and real-time insights into crop health, the use of the Sentera 6X Multispectral sensor can help farmers make more informed, data-driven decisions.

Key Applications:

  1. Targeted Fertilization: With CIG data indicating nitrogen levels, farmers can apply fertilizers only where needed, reducing costs and minimizing environmental impact.
  2. Irrigation Management: By identifying water-stressed areas, farmers can optimize irrigation schedules, ensuring that water is delivered precisely where and when it is needed.
  3. Early Pest and Disease Detection: Low chlorophyll areas can reveal early signs of pest infestations or diseases, allowing for quick interventions before these issues spread.

Conclusion

The successful 7-minute flight mission over the 8.47 acres of baby lettuce at Babe Farms highlights the value of multispectral imaging in modern agriculture. The Sentera 6X Multispectral sensor provided detailed insights into crop health through the Chlorophyll Index Green, offering early detection of potential issues and enabling precise, efficient crop management. By leveraging this data, farmers can enhance productivity, reduce input costs, and minimize environmental impact—ultimately improving both yields and sustainability.

In the competitive world of agriculture, integrating multispectral drone data into routine crop monitoring processes ensures that farmers stay ahead of potential problems, optimizing their resources and maximizing returns on investment.



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