Apr 2, 2015Drones seen as a method to manage fruit production
Attached to an unmanned aerial vehicle (UAV), a thermal camera can record differences in soil heat with the presence of water – one of the major benefits of using visible color imagery in agriculture applications.
That was the message delivered by Hermann Thoennissen, president HTG International Agriculture Consulting, who spoke at the Washington State Horticultural Association’s annual meeting in Kennewick, Washington.
“If you have too much water, like an irrigation leak, it will show up pretty clearly,” Thoennissen said.
Thoennissen works with Farm Cloud, a company that provides growers with advice and service for remote aerial sensing and imaging.
Thoennissen is a lifelong farmer with a master’s degree in pomology, has spent the last 20 years perfecting his skills in reading and interpreting the agronomic properties of infrared imagery of orchards in Washington state.
“The human eye is very sensitive to subtle differences in the color and texture of mostly uniform areas in the field of view such as agricultural plots seen from the air,” he said. “Scanning a field with a normal visible color camera will give the trained eye (the grower, crop advisor, agronomist or horticulturist) an indication of stress in the field, which may be caused by insects, pests, climate, or man. This overview of field conditions will highlight areas that may require deeper scouting or investigation.”
He said some micronutrient deficiencies in the leaves may be detected in visible color scans.
“Color Infrared (CIR) imagery is a way of visualizing the earth using a part of the light spectrum not visible to the human eye (near-infrared), combined with a part of the spectrum that is (red and green),” Thoennissen said. “It appears unnatural to the uninitiated because the colors are shifted to accommodate the near-infrared – red reflected light is displayed green, and green reflected light is displayed blue, allowing the near-infrared to be displayed in red.
“Since healthy vegetation reflects away most near-infrared light, whereas unhealthy vegetation and soil reflect away less, an agronomic specialist trained in CIR imagery interpretation can make sense of the subtle differences in shading and hue in this visualization technique.
“(CIR imagery) allows a rough, relative, non-quantitative assessment of biomass and/or chlorophyll content of plant material, along with plant stress (but not necessarily the cause of the stress). Indirectly, it can indicate areas of under-watering or under-fertilization.”
Thoennissen said a Vegetation Index (VI) is simply a way of mathematically combining two, three, or more reflectance measurements from different parts of the spectrum into a single number.
“If the reflectance measurements are in the form of images, the result is a VI image. VIs tend to suppress image variation that is common to all of their input channels, while accentuating differences. They also convert what appears to a human as color or hue differences into quantitative, computer-understandable values. They tend to be correlated to something measurable, such as chlorophyll content, amount of photosynthetically active biomass, etc.”
He said while hundreds of different VIs have been developed over the years, only a small number such as the NDVI, ENDVI, PRI, and SAVI are in common use.
“The relative merits of different VIs are actively debated, and new ones appear regularly in the scientific literature of agronomy,” he said.
Thoennissen said orchard and vineyard managers spend a lot of time and money managing the canopies of their trees.
“This is not surprising. In the case of apples, canopy density has a big impact on the quality of the fruits: If the canopy is too dense, the fruit color will be insufficient,” he said. “On the other hand, if a canopy is too thin, apples get sunburn. Moreover, canopy density impacts fruit set as well. Non-optimal canopy density causes reduced yield and poor quality, while the operating costs stay the same.
“The much higher ground pixel spacing of the UAV image enables a method of canopy density determination, which is more reliable and simpler than that from either full-scale aerial imagery or satellite imagery,” Thoennissen said. “As each pixel can be classified as ‘canopy’ or ‘not canopy,’ followed by some image processing of the resulting binary image, one can measure canopy density directly as opposed to inferring it from the spectroscopy of the (mixed) pixels.”
The leaf area of trees is very sensitive to the growing conditions, such as rooting depth, water and nutrient availability, Thoennissen said.
The Canopy Density Zone Map is especially suitable to detect and delineate problem areas caused by the inherent spatial variability of soil properties such as water holding capacity, CEC, pH, texture and organic matter content, he said.
A Digital Elevation Model (DEM) is a digital representation of the terrain height over a specific area of the earth, sampled at a fixed grid interval, he said. The denser the grid, the more information the DEM will possess and the more variation in the terrain will be recorded.
A DEM can be edited to generate a Digital Terrain Model (DTM) (representation of the bare earth that contains elevations of natural terrain features where vegetation, buildings and other non-ground objects have been digitally removed).
A Digital Surface Model (DSM) is a 3D representation of the terrain’s surface. Digital Surface Models (DSMs) measure the height values of the first surface on the ground including terrain features, buildings, vegetation and power lines etc.
Accurate digital elevation data produced by Light Detection and Ranging (LiDAR), a laser mapping technique, is more expensive and accurate and will be added at a later date.
“Thermal images are actually visual displays of the emitted thermal IR, which is proportional to temperature (and the emissivity of the material),” Thoennissen said. “It detects both transpiring plants (as their temperature will be somewhat lower than the surrounding air), and any wet or cooler spots on the surface.”
Thermal imaging can assist in visualizing how cold pockets can be shifted or “stacked” in the night when cold air is trapped due to a breeze going against the natural downhill flow of cold air or during those nights when warm air below is being overlaid by cold air above (reverse inversion).
“A sequence of change maps, be it canopy density or soil moisture, can help a grower to precisely address these areas via various horticultural, viticulture or irrigation management practices,” Thoennissen said.
“It further enables the grower to observe the development of the initiated changes in practice and follow these through to the final outcome (harvest). After harvest these geo-referenced maps can aid in precisely installing more permanent modifications to the irrigation system or to apply different sets of horticultural practices to clearly identified zones. These types of activities will enhance the amount of harvested product that falls into the target zone.”