Environmental factors, soil, crop, pests and diseases have the potential to influence crop productivity. Treating this complex system as homogeneous is not in the farmer's best interest and can result in low productivity, profitability and quality in their products. Image analysis and georeferencing are important tools for more efficient management of the production process. One of the quickest ways to understand what is happening to plants in the field in a timely manner is to capture satellite and UAV images associated with correct processing and interpretation.
This technology, when applied in a timely manner, helps in decision-making in plant management and prevents production losses. Technologies such as drones will provide a “reform” to the agricultural industry, through planning and strategy based on real-time data collection and processing. Nathan Oman, from Sky View 3D, believes that multispectral data collection and analysis through drones helps farmers of all types to accurately visualize and predict disease (resistance), pest and nutrient problems early, so they can take action in advance. This helps protect crops, allowing farmers to produce higher quality, high-yield products, and reduce environmental impact. This technology is the future of all agricultural applications. PWC estimates the market for drone-powered solutions in agriculture at $32,4 billion.
But what are drones? This term is becoming increasingly popular to refer to the small helicopter-like devices (quadcopters) that are being used by millions of people around the world. However, there are several other terms used to describe them, which can make things a little more confusing. It is likely that some of these definitions will change in the near future by the FAA (Federal Aviation Administration – North America) and ANAC (National Civil Aviation Agency). But for now, we still have several terminologies that define different models, as we will see below.
Historically, the term drone was given to a radio-controlled military aircraft created in the 1950s, the Queen Bee. While most people think of drones as an unmanned aerial vehicle that can fly autonomously without the need for a human at the controls, the term can also be applied to a wide variety of land and underwater vehicles. For example, there are navigational boats (such as the landing platform for SpaceX's Falcon 9 space rocket), submarines, and autonomous vehicles (cars and trucks such as the Tesla Semi), which also fall under the definition of drone. The term UAV, which means an unmanned aerial vehicle, is related to devices that are capable of flying remotely with controllers, tablets, cell phones, or autonomously.
So, all UAVs are drones, but not vice versa. In the not-so-distant past, there were few quadcopter options, and the main limitations on flight time were batteries, speed, range, camera mounts, and price. The advent of smaller, cheaper drones was due to remarkable technological advances such as tiny sensors, incredibly powerful processors, and a variety of digital radios. With the introduction of fixed-wing drones such as Parrot’s Disco in 2016 and Quantum Systems’ Tron and Trinity in 2017, the industry gained new momentum.
Each device has inherent characteristics that may or may not be advantageous for certain tasks and objectives. In terms of agility and the advantage of hovering in the air, quad/octacopters win in this regard. If the objective is to take photos or keep an eye on things while hovering in the air or more detailed analyses that require low altitudes and the application of fungicides, herbicides or insecticides, propeller-driven equipment has a great advantage due to its stability.
In terms of operating speed, fixed wing drones generally outperform rotary wing aircraft. Fixed wing drones can reach speeds of 75 km/h compared to the 15 km/h achieved by propeller or rotary wing drones. In terms of payload and flight hours, fixed wing drones are more efficient, for the same reasons that we do not have intercontinental jumbo helicopters. These devices are generally more efficient in terms of battery usage to achieve greater speed and reach longer distances.
Finally, durability and takeoff are very important when we think about the work regime in which these devices will be challenged in agricultural environments, since a quadcopter has more moving parts than a fixed-wing drone. The comparative robustness of a fixed-wing device (or the ease of replacing a foam wing) makes this type of equipment attractive for field work, such as biological or agricultural research.
Therefore, a producer of 50 hectares and one of XNUMX hectares need to evaluate what type of equipment to purchase to meet their needs. However, even smaller-capacity equipment such as quadcopters can meet the needs of a large producer, under certain circumstances. This equipment can be used to capture, spray, irrigate, evaluate and accurately monitor the development of the crop cycle in real time. We can literally relate this process to a “scanning” of the plantation, which can reduce losses and increase productivity. This equipment is capable of generating three-dimensional maps that can be superimposed on fertility maps (soil chemistry and physics), allowing the interpretation of patterns in fertility, soil stains, incidence of diseases, insects, nematodes and weeds in the production areas.
However, of all the applications of drones, one of the most important is the generation of multispectral images associated with crop health. It is essential to assess crop health and detect bacterial or fungal infections in plants. By analyzing a crop using visible and near-infrared light, drone-borne devices can identify which plants reflect different amounts of green light and NIR light. This information can produce multispectral images that track changes in plants and indicate their health. A quick response can save an orchard or plantation.
Additionally, once a disease is discovered, farmers can monitor and apply pesticides more accurately. Both of these capabilities increase the plant’s ability to overcome the disease. And in the event of a crop failure, farmers will be able to document losses more efficiently for insurance claims. Multispectral camera remote sensing imaging technology spans Green, Red, Red, Near Infrared (NIR) and Thermal bands to capture visible and invisible images of crops and vegetation.
Multispectral images are integrated with specialized agricultural software, which produces information in meaningful data. These software programs vary according to the processing needs and value, some of which are cloud-based (internet-based), such as the Atlas from the company Micasense, which does not require supercomputers for processing. Software programs such as Agisoft and Pix4D perform the processing using powerful machines with good memory and graphics capacity. This terrestrial, soil and crop telemetry data allows the producer to monitor, plan and manage the farm.
Multispectral drone sensors offer different performances depending on the number of cameras/sensors installed. Therefore, there is a range of price, quality and functionality. The sensors can be integrated into a multitude of drone platforms, allowing any producer to quickly and affordably access crop data. These advanced sensors facilitate the integration of satellite-based index data with drone-based index data. Each device uses filters that can generate NDVI or NDRE information at different times.
A multispectral imaging sensor captures image data at specific frequencies across the electromagnetic spectrum. Wavelengths can be separated by filters or by using instruments that are sensitive to specific wavelengths, including light at frequencies beyond our visible vision, such as infrared. Spectral imaging also allows for the extraction of additional information that the human eye cannot capture. The human eye can see a range of colors ranging from violet to red. However, wavelengths can also be shorter (ultraviolet) or longer (infrared) than our visible vision. The reflectance properties of vegetation are used to derive vegetation indices (NDVI).
Indices are used to analyze various ecologies. Vegetation indices are constructed from reflectance measurements at two or more wavelengths to analyze specific vegetation characteristics, such as total leaf area and water content. The absorption and reflection of solar radiation are the result of many interactions with different plant materials, which vary considerably in wavelength. Water, pigments, nutrients, and carbon are each expressed in the reflected optical spectrum from 400 nm to 2.500 nm, with reflectance behaviors often overlapping but spectrally distinct.
These spectral signatures allow scientists to combine reflectance measurements at different wavelengths to enhance specific vegetation characteristics and reflect some parameters related to defoliation that can provide information on the presence of pests or pathogens. The green wavelength range corresponds to the energy reflected in the 500nm-600nm spectral band and has the highest reflectance of a plant in this range. The peak reflection is around 550nm. It has been shown that this spectral band is strongly correlated with the amount of chlorophyll contained in the plant. The internal structure of healthy plants acts as excellent diffuse reflectors of near-infrared wavelengths.
Measuring and monitoring near-IR reflectance is a way to determine how healthy (or not) vegetation may be (presence of nematodes, fungi and pests). Furthermore, most of the light in the visible spectrum reflected by a stressed plant is in the green range. Therefore, to the naked eye, a stressed plant is indistinguishable from a healthy plant. On the other hand, the difference can be seen in the reflectance of infrared light, which is much lower. Red corresponds to energy reflected in the spectral range of 600nm to 700nm. The strong absorption of chlorophyll in this range results in low reflectance. Reflectance varies significantly in relation to factors such as biomass, LAI (Leaf Area Index), soil history, crop type, moisture and plant stress.
For most crops, this band gives excellent contrast between plants and soil and is used extensively to compile most vegetation indices in agriculture. The RedEgde band is a very narrow band (700nm-730nm) that corresponds to the entry point of the Near Infrared. It is the point of sudden change in reflectance, from strong absorption of Red to substantial reflection of Near Infrared. This band is very sensitive to plant stress and provides information about chlorophyll. The NIR or near infrared corresponds to wavelengths in the range 700nm to 1,3µm.
There is a very strong correlation between this reflectance and the level of chlorophyll in the plant. A highly significant change in reflectance in this range occurs when a plant is under stress. Along with the red spectral range, infrared is widely used to compile most vegetation indices in agriculture. NIR is sensitive to leaf cellular structure and provides critical data for monitoring changes in crop health. Regarding the blue wavelength, healthy vegetation absorbs it to fuel photosynthesis and create chlorophyll. A plant with more chlorophyll will reflect more energy in the NIR than a diseased plant.
The use of thermal sensors also indicates fluctuations in the temperature of leaf tissues, where diseased tissues are differentiated from healthy ones through sudden changes in temperature due to differences in photosynthetic activity. Thermal sensors end up playing an important role in detecting the latent period of diseases such as Asian soybean rust (P.Hakopsora pachyrhizi) and coffee (Hemileia Vastatrix).
Agricultural drones and multispectral imaging are becoming a tool like any other consumer device. We can report that a military-restricted technology is transforming into a socially friendly technology. And we can conclude that future generations will grow up accustomed to flying robots that hover over farms like tiny agricultural “mop mops,” sweeping away problems one by one.
*Per Breno Juliatti, from Juliagro Ltda, Nathan Oman, from Sky View 3D LLC, James Peters, from Sky Flightrobotics, and Fernando Cezar Juliatti, from UFU-MG
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