Drones
In recent years, drones have experienced a tremendous leap in both development and commercialization, gaining popularity worldwide. They certainly deserve a closer look, so let’s delve into the history of their development, exemplified by the brand DJI (Da-Jiang Innovations), which has forever changed the world of drones. To clarify, a drone, often defined as an Unmanned Aerial Vehicle (UAV), is a flying device that can be remotely controlled or fly autonomously using built-in flight control and navigation systems. Drones vary in size, shape, capabilities, and purposes for which they are designed. Drone applications are incredibly diverse, including aerial photography and filming, inspections, land monitoring, agricultural work, scientific research, rescue operations, and military applications. They are typically equipped with image and flight stabilizers, high-resolution cameras with real-time image transmission capabilities, thermal imaging options, GPS systems, and obstacle avoidance sensors. These devices, packed with amazing technology, can often fit in the palm of your hand and weigh under 250 grams.
DJI, currently one of the world’s leading manufacturers of commercial and consumer drones, began its journey in 2006 in Shenzhen, China. The company was founded by Frank Wang (Wang Tao), who began working on his drone project while a student at the Hong Kong University of Science and Technology. Wang proved to be a visionary, dreaming of creating the world’s best UAVs and revolutionizing how people view this technology. His passion for aviation and robotics was driven by the desire to create innovative, accessible, high-quality drones for both enthusiasts and professionals in various fields. The establishment of DJI was a key step in realizing this dream. Wang’s main goals included contributing to advancements in aviation and technology by offering products that combine advanced technical solutions with ease of use. From the beginning, he focused on innovation and continuous development to create drones that were not only efficient and reliable but also accessible to a broader user base. In 2006, Wang began building prototypes in his dorm room, focusing on creating stable and reliable UAV flight systems. Between 2008-2010, DJI released its first products, including flight controllers, which quickly gained popularity among amateur and professional aviation modelers. In 2013, another breakthrough occurred with the introduction of the Phantom model, an advanced UAV with a camera, which rapidly gained market popularity, especially among photography and filming enthusiasts. Post-2014, DJI’s brand, fueled by its initial successes, continued to develop its product line, introducing more advanced drones like the Inspire, Mavic, and Spark series, as well as solutions for specialized applications in agriculture, filmmaking, rescue, and research, gaining recognition in international markets and becoming an undeniable leader among drone manufacturers. DJI didn’t just produce drones; it also developed technologies such as obstacle avoidance systems, advanced image stabilization systems, and mobile applications for video control and editing. Today, the brand is also a leader in the production of gimbals, action cameras competing directly with the GoPro series, and sound recording devices. DJI’s current flagship drone models include the Mini, Mavic, Air, Avata, FPV, Matrice, Agras, Inspire, and Enterprise series.
At the same time, FPV (First Person View) systems have gained popularity. With special goggles worn on the head, we see the image transmitted from a camera mounted on a drone, providing a first-person view as if we were in the airspace. These drones are primarily used for the pleasure derived from their unlimited aerial maneuverability, capturing breathtaking footage, and unfortunately, in military applications as an ideal tool for reconnaissance and for carrying and precisely delivering explosive payloads. An FPV set typically consists of goggles for transmitting the drone’s camera feed, the drone itself, and the control system. This market involves a range of manufacturers, with the DJI O3 system currently being the most popular for real-time image transmission from the drone to the goggles. However, the situation is different regarding the communication system connecting the drone to the control apparatus. One of the leading systems is ExpressLRS (ELRS), a long-range radio communication system specifically designed for drones and other remotely controlled models. Its efficiency, reliability, and flexibility have earned recognition and popularity in the related community. Its biggest competitor at present is the TBS Crossfire communication system. Thanks to the receiver in the drone, it can connect to a control apparatus also based on the same system. Leading manufacturers of control apparatus include brands like RadioMaster and TBS. Among the leaders in FPV drones, it may not be surprising that all have their origins in China, including brands such as DJI, BetaFPV, iFlight, and GEPRC. Each of these manufacturers offers something unique for different needs and preferences of FPV users, from drones for beginners to advanced systems for racing, freestyle, long-distance flights, and capturing footage with professional cameras. The choice of the right FPV drone depends on personal preferences, flying style, and the pilot’s experience level.
After this extended introduction, we can move on to the practical application of drones in plant production, which is very broad. Initially, it’s important to note the potential use of drones for their primary purpose for which they started being created, namely the capability of photography and video recording. Running a farm is increasingly about creating a brand, and attention focused on this aspect is growing. Even DJI models from the Mini series allow us to create amazing shots of our crops, enabling us to promote our farms on social media, gaining new customers with minimal effort and investment.
Drones equipped with LiDAR (Light Detection and Ranging) systems can create accurate three-dimensional maps and models of the environment. Reflected laser pulses create a dense point cloud, representing the surfaces of objects on the ground. These points form the basis for creating three-dimensional models. Although the process of creating these models requires special software and expert knowledge, it is relatively fast and allows for the production of compact, high-quality maps. The diversity of available LiDAR systems allows organizations to replace other measurement methods, such as photogrammetry, or to transfer ground-based LiDAR measurements to the airspace using drones. LiDAR is becoming increasingly accessible, opening up possibilities for its application in new industries and various scenarios in the coming years, including in plant production. Obtaining such maps allows for precise terrain analysis, which is particularly important for identifying frost pockets or areas especially prone to flooding. It also enables more efficient planning of structures for plant cultivation under covers. However, it’s important to note that these three-dimensional images do not provide details similar to photographs. LiDAR does not capture the colors of objects on the ground, meaning such information must be obtained from other sources, such as cameras mounted on drones. DJI drones are also commonly used by surveyors for terrain mapping, but they rely on different systems than LiDAR.
Currently, drones are increasingly appreciated in many fields due to their ability to conduct inspections and surveillance over very large areas, especially those that are difficult to access, whether it’s construction, major engineering projects, locating animal herds, or assessing damage caused by extreme weather events. Drones are often the optimal solution.
DJI drones from the Matrice series are equipped with thermal imaging cameras, offering significant possibilities especially in forestry and animal husbandry-focused agriculture. In forestry, these drones can be used to monitor wildlife, helping in the assessment of their population, tracking migrations, and identifying endangered species. Thanks to thermal imaging cameras, drones allow for the observation of animals in inaccessible areas and at night. Moreover, these drones can be used for patrolling large forest areas to detect and prevent poaching. The thermal imaging camera is particularly useful at night when most poaching occurs. In agriculture, these drones can be used to monitor the health and welfare of livestock. They can quickly survey large herds, identify sick or injured animals, and monitor their behavior and living conditions. Additionally, they can help manage pastures by monitoring the condition of grasses and vegetation, which is important for maintaining a healthy environment for livestock. Similar to forestry, drones can detect potential threats to livestock, especially in terms of predators or unauthorized human activities. In scientific research related to forestry and agriculture, drones allow for the collection of data on biodiversity, animal migration patterns, and their interactions with the environment. UAVs also support the management of natural resources, providing information necessary for the effective planning of the use of forest and agricultural lands in a way that is sustainable and wildlife-friendly.
With the application of advanced cameras and sensors in UAVs (Unmanned Aerial Vehicles), plant growers have the ability to obtain high-resolution photographs and films along with multispectral data, which allow for the collection of information:
➤ Vegetation Indices (e.g., NDVI, SAVI, NDRE): Derived from multispectral images, these indices are crucial for assessing the health, vigor, and biomass of plants. NDVI (Normalized Difference Vegetation Index) and NDRE (Normalized Difference Red Edge) are particularly useful for understanding plant health and stress levels. SAVI (Soil-Adjusted Vegetation Index) is helpful in areas with significant soil background influence.
➤ Plant Stress Detection: Early detection of plant stress caused by pests, diseases, nutritional deficiencies, or water stress is possible due to changes in the spectral signature of plants. This can lead to more effective and timely interventions.
➤ Irrigation Management: Assessing vegetation health and soil condition can aid in optimizing irrigation schedules and identifying areas that are under- or over-irrigated, leading to more efficient water usage.
➤ Yield Forecasting: Multispectral data can be used to estimate yields by analyzing plant health and coverage. This allows for better planning and resource allocation.
➤ Weed Detection: By distinguishing crops from weeds based on their spectral signature, drones can assist in identifying areas requiring weed control, supporting more targeted herbicide application.
➤ Phenology Monitoring: Tracking the growth stages of plants (phenology) is key for optimal timing of fertilization, irrigation, and harvesting. Drones provide a way to monitor these stages across the entire field.
➤ Soil Health Analysis: Although indirect, multispectral data can provide insights into soil health, such as areas of erosion, compaction, or soil type differences.
➤ Plant Counting and Spacing Analysis: Drones can automate the process of counting plants and analyzing their spacing, which is crucial for estimating plant populations and assessing planting efficiency.
➤ Damage Assessment: Following a natural disaster (such as hail or storm), drones can quickly assess damage to crops, aiding prompt response and insurance processes.
➤ Precision Agriculture: Integrating drone data with other technologies, such as GPS and GIS, can enhance precision agriculture practices, enabling highly efficient, localized crop management.
Let’s revisit DJI drones, which serve as a primary example of using unmanned aerial vehicles for applying plant protection products, fertilizers, and biostimulants. The DJI Agras series drones are advanced, specialized flying devices designed with agricultural applications in mind. They are part of the growing trend of using drone technology to increase efficiency and sustainability in agricultural practices. Among the most advanced drones in this series are the T30 and T40 models. Both models have highly developed spraying systems that allow for efficient and precise application of liquids such as fertilizers or pesticides. These systems are designed to maximize coverage while minimizing the use of substances. The spraying system allows for the regulation of fluid flow, which enables the adjustment of the amount of agent used depending on the needs. The atomization technology in these drones transforms the liquid into fine droplets, increasing the efficiency and uniformity of coverage. Finer droplets adhere better to leaves, enhancing the effectiveness of sprays. Moreover, these systems are capable of automatically adjusting to the drone’s flight speed and changing atmospheric conditions, ensuring even coverage regardless of external factors. They are equipped with intelligent flight planning and navigation functions, allowing for automatic mapping and spraying of large areas with precise coverage. To enhance their safety, they are equipped with advanced safety systems, including obstacle avoidance sensors, which assist in safely conducting tasks in complex agricultural terrain.
Currently, the range of unmanned aerial vehicles (UAVs) available for agricultural operations is much more diverse, so it’s worth mentioning other manufacturers of these extraordinary devices.
XAG, formerly known as XAircraft, is a Chinese tech company that has gained a reputation as one of the leading agricultural drone manufacturers in the world. Founded in 2007, the company has since focused on developing innovative solutions for agriculture. XAG is known for its advanced technologies tailored to the needs of modern agriculture, emphasizing increasing efficiency and sustainable practices. XAG drones are also equipped with sophisticated liquid spraying systems and are used for precise seed sowing. They enable accurate placement of seeds in specific locations, enhancing germination rates and optimizing plant distribution. These drones provide quick and efficient seed dispersal over large areas, significantly more efficient than traditional methods. Equipped with advanced sensors and cameras, these UAVs can collect data on crop conditions, including information on moisture, temperature, and plant health.
Yamaha, primarily known for manufacturing motorcycles, boats, and other vehicles, also holds a significant position in the world of agriculture through its unmanned helicopters. This brand is a pioneer in the field of unmanned aerial vehicles for agriculture. The company began researching their application in the 1980s, making it one of the most experienced manufacturers in this industry. Yamaha has introduced several models of unmanned helicopters to the market, mainly used in agriculture for various tasks, such as spraying and monitoring crops. Unlike multirotor drones more common in agriculture, Yamaha has focused on unmanned helicopters.
PrecisionHawk (UK), though mainly based in the USA, is also active in Europe. They specialize in the development of drones and data analysis for precision agriculture.
SenseFly (Switzerland), part of the Parrot Group, is a well-known drone manufacturer, including models designed for agriculture. They offer drones capable of collecting agricultural data, which can be used for mapping and monitoring crops.
Quantum-Systems (Germany) manufactures VTOL (Vertical Take-Off and Landing) drones, which are ideal for use in agriculture, including mapping and monitoring large crop areas.
ABZ Innovation (Hungary) is a leading manufacturer of agricultural drones, focusing on developing innovative solutions for precision agriculture. This manufacturer primarily focuses on developing technology related to crop spraying.
AeroVironment, Inc. (USA), designs, develops, and produces advanced products and services for government agencies and businesses, including unmanned systems for agriculture.
Microdrones GmbH (Germany) develops, manufactures, and delivers customized and intelligent drone solutions worldwide.
Satellite imagery is also currently used to observe vegetation progress and its moisture. An example of the application of this technology is the ReelView app created by Rivulis. Such technology will likely be developed and commercially offered by more companies, allowing widespread use of satellite data and its AI analysis.
The role of artificial intelligence in the development of drones has two clear objectives. Firstly, the vast amount of data collected needs to be analyzed by „something,” and for these data to be collected, someone has to fly the drone. It’s no surprise that no planter can afford to perform daily flights to collect data simply due to a lack of time. So, can artificial intelligence manage to pilot UAVs? Indeed, it can, as even demonstrated in its most demanding form of piloting, namely FPV drones. Recently, drone racing, requiring incredible piloting skills, exceptional reflexes, and a huge amount of training time by UAV operators, has been gaining popularity worldwide. In the scientific publication „Champion-level drone racing using deep reinforcement learning,” published in 2023 by Kaufmann et al., the authors emphasize that mastering the skill of autonomous drone flight at a level comparable to professional pilots is an extremely difficult task, as it requires the robot to use the full range of its physical capabilities. Such a drone must precisely assess its speed and position in space, based solely on data obtained from onboard sensors. Thanks to the development of the autonomous Swift system, based on deep reinforcement learning and enhanced by data from the physical world, the researchers managed to reach the level of top racing drone operators globally. The Swift system was tested in practice through a real-time race against three champions, including two who boasted world championship titles from two international leagues. The AI managed to win several races against each of the human operators, simultaneously achieving the fastest recorded race time. The authors of the publication emphasize that the achieved results are a milestone in the field of mobile robotics and machine intelligence, which may soon inspire the creation of new hybrid solutions with a foundation in learning in physical systems.
Implementing such solutions will undoubtedly aid in the development of drone autonomy, which, even without this technology, had the capability to plan simple flight paths thanks to special systems. With new solutions, they could gain the ability to move in more complex ways, allowing for more precise task execution.