Crop variety testing using drones to measure canopy cover

About the project

Success in farming has always been largely dependent on a farmer’s knowledge of the latest research and the successful adoption of new technologies, including new crop varieties. However, it is also important to understand just how the technology works at the farm level. New varieties and plant breeding have contributed much to an increase in agricultural production. Still, to make informed decisions on the adoption of new varieties, it is crucial to grasp how new seed types perform on the soil types and conditions specific to an individual farm.

Rob was a soybean grower who wanted to estimate the quality of two different new seed varieties so he decided to undertake a farm level test. To do this, he planted, in the same field, areas of two different soybean varieties with the same seeding rates (150,000 seeds/acre). His aim was to compare canopy development over the growing season.

Canopy coverage is highly correlated with yield [1] and digital imagery. Coupled with appropriate software, it offers a simple and effective method of determining canopy coverage and LI (Leaf Index) [2]. Rob, therefore, decided to use drones to assess the performance and crop conditions in different parts of his field and he considered using a drone. However, being new to this kind of precision agriculture technique, he approached Agremo for guidance.

Customer requirements and challenges

To compare the varieties, Rob needed accurate information on the canopy cover for each variety throughout the growing season. However, to make accurate and robust comparisons, the seeds of each variety that were tested need to be grown under similar conditions, with similar soil types, and ideally within the same field.

Client needs

  • To make an effective comparison, the two varieties needed to be sown in representative plots within the same field, at the same rates.

Field conditions / Existing processes

Previously, when he tried to compare varieties, Rob simply viewed the crop with a naked eye throughout the season. This, however, was based on guesswork and lacked a real scientific basis.

How Agremo approached the project and challenge

Agremo advised Rob to carry out the Canopy Cover analysis. The analysis determines the percentage and the exact location of the canopy/leaf area and ground cover area, and it helps compare the performance of the two varieties at different growth stages.


Several studies with soybeans have demonstrated that attaining full canopy coverage, and thus maximum light interception (LI), during vegetative and early reproductive periods is responsible for yield increases in narrow-row culture due to enhanced early growth [1].

It was decided to carry out a canopy cover analysis at the v5 (growing) and r3 (the start of podding) growth stages. This would provide a measurement of the canopy cover during the vegetative (v), and reproductive (r) stages of the crop [3].

Design and Plan

Collect data

Analyze data

Deliver and Apply

The Process and the Solution

What had happened in the field

Summarized Canopy Cover analysis results:
At the V5 stage variety, one had reached 61% cover, while the other was only at 47%. By the R3 stage, the gap had closed to 97% and 91%, which means that at the beginning of podding, variety 1 has achieved the greatest level of cover. Low crop cover can mean bare patches of soil and indicate that a crop is more susceptible to weed infestations.

What went as planned

The analysis was able to clearly distinguish between the different varieties in the field. It indicated different levels of canopy development between the two varieties at the key growth stages.

What was unplanned

The germination rate was very similar for both varieties. However, as the season progressed, the crops developed differently and there was a notable difference in canopy cover between the two varieties.

Where there any issues

After reviewing the analysis, Rob concluded that variety 1 had developed more fully than variety 2, with a difference in canopy cover of 6% at the R3 stage. This was something he was not able to spot by simple visual observation and would have a significant impact on seed development and yield. Variety 2 did not develop as thick a canopy as variety 1, and would thus be more vulnerable to weed infestation.

Try Agremo Analytics

Free demo - no credit card required

What were the decisions informed by Agremo analysis

The analysis allowed for a direct comparison between the two varieties under field conditions and it provided Rob with the data he needs to choose appropriate varieties in the future. It would also help him make more informed choices as the season progresses regarding issues such as nutrition, irrigation, and crop protection, using information that is specific to the grown variety and soil type. Canopy analysis enables identification and investigation of bare soil and patches of poor crop development, giving input on the most appropriate action that should be taken.

This allows for more efficient use of resources, including chemical inputs as well as managerial time, and can help a farmer produce crops more sustainably and profitably.

Return On Investment

Rob was able to effectively evaluate the two varieties under field conditions on his own land. These results would help to inform future planting and make various choices. In this case, variety 1 had performed better than variety 2 under his specific farming system and soil conditions.

Farmers should aim to establish 90% to 95% canopy coverage by flower and pod formation to capture the light necessary for maximum yield potential [4]. If this is not achieved then, in the future, they should consider methods geared towards improving leaf area development, such as earlier planting, later relative maturity, narrower rows, and/or greater seeding rates.

The data provided Rob with an accurate means of assessing the progress of his crop over a season. The report also allowed him to eliminate any reliance on subjective measurements and replace them with a more precise and scientific assessment.

More and more farmers are looking for “actionable intelligence” for precision farming [5] and Agremo reports provide this. Leaf Area Index (LAI) and biomass are important indicators of crop development and the availability of this information during the growing season can support farmer’s decision-making [6] for a wide range of issues, including crop nutrition and protection, and the timing of operations. Leaf Area Index (LAI) is a measure of foliage density that plays a major role in photosynthesis, groundwater-surface water interactions through evapotranspiration (ET).  Soybean LAI can be measured as a function of canopy coverage from images taken from above the plot using a digital camera [2].

It is not only farmers who can benefit – drones are also of use to plant breeders. Soybean average canopy coverage (ACC), measured by unmanned aerial systems (UAS), is a highly heritable trait with a high genetic correlation with yield. This can be used in the early stages of plant breeding programs to screen varieties [1] and help speed up the development of high yielding varieties.

Thus, the canopy analysis provides a useful measure of a crop’s progress and yield potential during the season which can inform a wide range of decisions through the season, and for subsequent crops.


  • F. F. Moreira, A. A. Hearst, K. A. Cherkauer, and K. M. Rainey, “Improving the efficiency of soybean breeding with high-throughput canopy phenotyping,” Plant Methods, vol. 15, no. 1, p. 139, 2019, doi: 10.1186/s13007-019-0519-4.
  • L. C. Purcell, “Soybean canopy coverage and light interception measurements using digital imagery,” Crop Sci., 2000, doi: 10.2135/cropsci2000.403834x.
  • Iowa State University, “Soybean Growth Stages,” Iowa State University: Extension and Outreach, Integrated Crop Management., 2020. (accessed Jul. 01, 2020).
  • I. Ciampitti, “Soyabean Growth and Development,” 2017. [Online]. Available:
  • FAO and ITU, E-agriculture in action: drones for agriculture. 2018.
  • A. Kross, H. McNairn, D. Lapen, M. Sunohara, and C. Champagne, “Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops,” Int. J. Appl. Earth Obs. Geoinf., 2015, doi: 10.1016/j.jag.2014.08.002.
Analyze your field

Free demo - no credit card required