CASE STUDY

Government of Canada

The Canadian Cannabis Act came into force on October 17, 2019, allowing for significant outdoor production of Cannabis. the Government of Canada is responsible for inspecting the production process to ensure licensed producers are meeting the proper regulatory requirements.

Cannabis production moving from pure greenhouse operations to include large outdoor fields, puts pressure on Canadian Inspectors simply due to the sheer growth in scale of the Cannabis crop. Moreover, as producers vary widely (large facilities, industrial, and even residences), inspections occur where information about a producer site may not readily available, leaving inspectors open to personal safety and security risks.

Robotics Centre took on this challenge in 2019 to assist the Government of Canada in exploring opportunities to enhance and improve inspections through the use of unmanned systems; while reducing cost and risk to Canadians.

The use of air-based robotics (i.e. drones) is by far the fastest growing integration of robotics into the agricultural landscape. The rapid adoption is directly related to the ability for such robotics to leverage existing technologies in consumer electronics such as smart phones, gaming consoles, and other mobile computing platforms (e.g. laptops, tablets, and etc.) to achieve low-cost, light-weight, and smart solutions. What drone technology offers is efficient and effective access to “current condition” data. Current condition data is one of the most valuable pieces of information in precision agriculture allowing farmers to spot problems early and quickly action appropriate interventions. Unlike spot-checking (especially in large fields), drone-based monitoring is able to capture conditions across an entire field, revealing issues deep within a field in a very short period of time when compared to manual spot checking.

The use of air-based robotics (i.e. drones) is by far the fastest growing integration of robotics into the agricultural landscape. The rapid adoption is directly related to the ability for such robotics to leverage existing technologies in consumer electronics such as smartphones, gaming consoles, and other mobile computing platforms (e.g. laptops, tablets, and etc.) to achieve low-cost, light-weight, and smart solutions.
What drone technology offers is efficient and effective access to “current condition” data. Current condition data is one of the most valuable pieces of information in precision agriculture allowing farmers to spot problems early and quickly action appropriate interventions. Unlike spot-checking (especially in large fields), drone-based monitoring is able to capture conditions across an entire field, revealing issues deep within a field in a very short period of time when compared to manual spot checking.

Multispectral Imaging

 

The current most efficient and effective sensory method for drone agriculture is that of multispectral imaging, which collects information based on the light reflected by the crop below.

Hyperspectral imaging is used in the food industry to monitor the quality of fruits and vegetables by commercial firms. With suitable frequencies, multispectral imaging from UAV may be an effective means of inspecting large cannabis fields. Symptoms of diseases caused by pests, such as nematodes, may also be visible as changes in canopy height, density, and width, or altered leaf morphology. Similar changes are visible in ground surveys of corn, cowpea, cotton, and soybean. Such changes could be detected directly in 3D or by altered absorption and reflectance.

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Multispectral Imaging

The current most efficient and effective sensory method for drone agriculture is that of multispectral imaging, which collects information based on the light reflected by the crop below.

Hyperspectral imaging is used in the food industry to monitor the quality of fruits and vegetables by commercial firms. With suitable frequencies, multispectral imaging from UAV may be an effective means of inspecting large cannabis fields. Symptoms of diseases caused by pests, such as nematodes, may also be visible as changes in canopy height, density, and width, or altered leaf morphology. Similar changes are visible in ground surveys of corn, cowpea, cotton, and soybean. Such changes could be detected directly in 3D or by altered absorption and reflectance.

Although the taxonomy of cannabis species are a bit more complex, they are still subject to afflictions similar to traditional crops. Afflictions include fungal diseases, viruses, and bacteria. All such abnormalities create spectral variations, which can be identified through spectral imaging. Moreover further spectral imaging experimentation be utilized to determine to determine spectral signatures for:

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Presence of heavy metals;

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Presence of non-approved pesticides;

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Plant count and biomass estimates.

Although the taxonomy of cannabis species are a bit more complex, they are still subject to afflictions similar to traditional crops. Afflictions include fungal diseases, viruses, and bacteria. All such abnormalities create spectral variations, which can be identified through spectral imaging. Moreover further spectral imaging experimentation be utilized to determine to determine spectral signatures for:

5

Presence of heavy metals;

5

Presence of non-approved pesticides;

5

Plant count and biomass estimates.

The ability to obtain visual surveillance and awareness prior to entering a site is imperative to minimize personal safety and security risks. This can be achieved by sending a drone ahead equipped with an EO/IR (Electro-Optical/Infra-Red) system. EO/IR are imaging systems used for military or law enforcement applications which include both visible and infrared sensors. Because they span both visible and infrared wavelengths, EO/IR systems provide total situational awareness both day and night and in low light conditions.