Introduction
The agriculture industry globally is undergoing a big transformation due to the innovation of smart farming solutions such as the deployment of drones. Smart farming makes agriculture profitable because the operation is autonomous. The introduction of smart farming has been necessitated by the rising demand for food, diminishing resources, and the reduction of manual labor in the agriculture sector. Agriculture has quickly become a high technology sector and new companies, professionals, and investment bodies are quickly joining the sector (Bartsch et al., 2016). The advancement in technology will increase agricultural production.
The introduction of drones in agriculture is expected to solve the problem of the shifting structure of the workforce in agriculture. The aging demography globally has also triggered the introduction of drones to supplement the dwindling workforce. The consumption of agricultural products is expected to rise tremendously due to the rapid increase in the world population. The only solution to this imminent challenge is the increase in agricultural production, which will have to be effectively achieved through the adoption of the latest technology. The solution is, therefore, the introduction of drones that have advanced data analytics that are capable of providing real-time images from the agricultural field (Coppola et al., 2020). Individuals, organizations, and governments have realized the need for smart agriculture due to its economic benefits.
Automated Drone Technology
Unmanned aerial vehicles or drones are growing in popularity at a tremendous rate. Although they are still in the infancy state in mass usage and adoption, they have already broken the rigid traditional barriers in areas that seemed difficult to penetrate through such technological innovations. For the past few years, drones have been playing a major role in the operation of different organizations and have managed to transform the industries that were either lagging or stagnant. They are proving useful because they can perform duties in an efficient and timely manner. Besides efficiency, drones have improved productivity and reduced the workload and the cost of production (Ehrenberg, 2018). Furthermore, drones have improved accuracy and refined service delivery.
Drone technology is a great innovation that has continued to have huge positive effects on today’s society. Technology has transformed the way business is done. Drones have been in existence for slightly more than two decades, and their origin can be traced to the First World War when the United States and France tried to develop unmanned aircraft. However, the last ten years have been important in the adoption of drones in different fields. Whether drones are controlled, accessed through an app on a smartphone, or controlled by a remote, they have the capability of reaching areas that would be very difficult for people to reach. They require a little amount of effort, energy, and time (Gonzalez-Aguilera & Rodriguez-Gonzalvez, 2017). This is one of the reasons why they are being embraced and adopted in the agricultural field worldwide.
Agricultural Drones
The penetration of drones in the agricultural sector has increased crop production, agricultural operations, efficiency in water and chemical sprinkling, and monitoring crops in case of ill-health. The digital imaging capabilities and sensors of the drones give farmers a good picture of their agricultural fields. The drones gather information such as crop diseases, therefore making the farmers aware of the crops that require attention. Through reliance on timely information about their crops, the farmers can take action immediately hence remedying the situation which in turn improves efficiency and crop yields in farming (Gonzalez-Aguilera & Rodriguez-Gonzalvez, 2017). Since in the agricultural sector information is power, most problems will be prevented.
The agricultural drones enable the farmers to monitor their agricultural fields from the sky. The bird’s eye view of the fields from the drones, allows farmers to meet the water needs of the crops and spot fungal and pest infestations, therefore, mitigating imminent problems before they cause havoc. The multispectral images from drones display a near-infrared and visual spectrum view of the plants. The combination of the two views, allows farmers to see the difference between healthy plants and the ones which are not. This kind of view would not be possible through the use of the naked eye. Thus, the images from drones are important in assessing how t crops grow as well as their production. The drones can be programmed so that they can survey the crops hourly, daily, or even weekly depending on the preferences of the farmers. The images relayed from drones show any possible changes in the crops, which might indicate possible trouble-spots (Hentschke et al., 2018). After identifying where the trouble is, the farmers will improve the production and management of the crops
A sprinkling of Water and Chemicals by Drones
Drones are sent to map out the agricultural field using the XAG’s X Mission surveying tools to see any issues that appear unique in the field. When the drone spots plants that are not healthy, the drone used for spraying is sent in. The spraying drone is programmed in such a way that it follows a particular course and can continuously spray over specific areas that had been identified. The drone can spray better than the way human beings do (Hentschke et al., 2018). It provides the crops with good coverage because it can easily reach the canopy of plants, better than the machines operating from the ground can do.
The use of drones is also effective in killing the weeds which grow around the water systems because it does not damage the other crops in the process of spraying. The spraying of crops by using drones is done autonomously because the drones are pilotless and they are programmed. The drones are equipped with all the parts of the ordinary sprayer. These parts include a pump that pushes the liquid through the pipes, a tank, the pressure gauge, and the filters. However, when compared with the other sprayers, there are certain limitations that the drone sprayers have. Such limitations include the size of the components because of the power that is required to keep the drone in the airborne mode for a reasonable period (Krishna, 2017). Although their flying capacity is limited, they have revolutionized the sector.
The drone technology has been undergoing testing because the drones used for spraying possess unique characteristics since of using liquid products. The application used by drones is different from the one that is used by the machines that are based on the ground. They are equally different from the spraying that is done by the dusters or the helicopters. The drones are flown at an average of 3 to 10 feet above the targeted area with its rotors creating turbulence which is commonly called vortices. The turbulence helps in the penetration of the chemical droplets into the canopy of the crop, providing good coverage of the bottom and top of the leaves (Hentschke et al., 2018). These unique characteristics make the drones effective in spraying the crops.
Other distinctive features of the drones used in spraying include the adjustment of altitude and the autonomous control of swarms which are the current standards for the commercial systems. The drones which have several rotors have a flight span of an average of 10 minutes which is a good period to allow a tank to disperse water or chemicals before landing to refill the tank and charge the battery for the next session. The drones can spray one acre within 3-4 minutes which would typically take hours if the work is done without the use of drones (Krishna, 2017). It would be better if they were able to spray crops planted in rows.
Effectiveness of Drones in Identifying Trouble-spots in Plants
Drones can be effectively used in the spraying of spots that have been identified, cleaning up of fields, or controlling the weeds that are crop resistant. For the drones to effectively conduct a spot or small area spray, the area needs to be mapped before the exercise. Prior planning is important for supporting path planning and automated systems. Stress mapping will make it possible for the spraying drone to fly to these areas, which will eventually maximize the payload capacities of the drones and their limited flight times (Krishna, 2017). Currently, the process of mapping is easier, efficient, and accurate because of the development of artificial intelligence which is an essential step in the advancement of technology.
The agricultural industry is increasingly using drones to improve the efficiency of watering crops and in detecting any possible leaks in irrigation. The drones that are fitted with thermal cameras are best suited in this industry because they can detect and see from the sky what would be difficult to see from the ground. As the need for the prudent use of water increases, drones have become essential in the monitoring of irrigation and sprinkling of crops with chemicals. With the use of conventional and thermal cameras, drones can spot water pooling on a farm. On large farms, the drones can monitor what is happening on the ground through the bird’s eye view. They can identify what crops need to be watered and the time for watering (Krishna, 2017). The information allows the farmers to monitor their crops effectively and use the water resources in an organized manner.
According to the report by the Food and Agriculture Organization (FAO), the use of drones for agriculture is expected to skyrocket to $4.8 billion in 2024 (Krishna, 2017). The information that drones gather on farming fields is used to enable farmers to make wise agronomic decisions (Krishna, 2017). In most parts of the world, the use of drones will become essential for precision farming. The information collected by the drones helps the farmers in planning their crop treatment and planting so that they can achieve the highest possible yields. Studies show that if the precision farming system is used the yields will increase by 5% within the first year (Toro & Tsourdos, 2018). The main use of the drones is monitoring the health of the plants so that if trouble spots are detected, the necessary measures on spraying can be instituted.
Operation of Agricultural Drones
The drones have special imaging equipment referred to as Normalized Difference Vegetation Index (NDVI) which uses highly detailed color information to spot the crops that are not in good health. This technology enables the farmers to monitor the crops and they are, therefore, able to point out any health problems that plants have as they grow. The problems are then handled fast to save the plants from any further damage. Because the drones fly close to the crops, the poor light conditions and the cloud cover do not matter when using drones. Additionally, the images from the drones are so accurate to the location of a millimeter (Krishna, 2017). It, therefore, means that after planting, any area that has problems such as the infestation of pests can immediately be spotted and treated right away.
The drones help farmers to maximize agricultural production. This is achieved through the application of fertilizers, control of pests, and spraying when necessary. The drones help the farmers to optimize the use of pests and water. When drones fly over an agricultural field, they capture a picture with high resolution using a camera or a sensor. Premised on the measured parameters, the captured images are of different bands ranging from infrared spectrum to visible colors. The images that are collected are taken as raw data which needs further interpretation. Immediately after the images are captured, they are directed to the cloud software, where, depending on the operation the farmer wants, prescription maps are created (Krishna, 2017). The maps are then uploaded to particular farm equipment which will adjust the number of pesticides or water that is required to be applied to the field.
Studies show that drones will bring one of the biggest data analytics in the agricultural field. It, therefore, means that agriculture will be one of the largest markets of drones. Its use is not mainly limited to spraying, although its main function in the agricultural industry is to provide clear and detailed maps of the agricultural farms enabling the farmers to make data-driven decisions. The drones are loaded with small sensors that are multi-spectral in nature that measure the key indicators on the health of plants, the levels of water stress, and the plant yields. The robotic implements that are vision-enabled are also important in organic farming (Toro & Tsourdos, 2018). The implements follow how the crops grow; they identify the weeds and assist in tilling the land.
The agricultural drones which are fitted with high technology sensors can increase efficiency in particular aspects of farming. They range from monitoring the crops, irrigating, or sprinkling water to spraying crops with chemicals to kill pests and mitigate fungal infections. Drone technology will enable the farmers to achieve precision agriculture where efficiency and cost reduction are the key factors. The technology in farming is based on observation, measurement, and taking action in real-time based on the data relayed by the drones (Toro & Tsourdos, 2018). It, therefore, eradicates the guesswork method which is used in farming and gives the farmers the platform to increase their crop yields and run their farming fields more efficiently.
The drones have advanced technology such as infrared cameras, propulsion systems, and programmable controllers. PS navigation systems and automated flight plans. They also have data collection and processing software that is custom-made that puts any data collected into use. The data helps in making better management decisions, hence helping to mitigate or prevent problems in the agricultural fields. When farmers implement drone technology, they save time that they would have used to survey the agricultural field manually (Sandvik & Jumbert, 2016). The technology helps in making prudent land management decisions that have positive long-term implications.
Normalized Difference Vegetation Index (NDVI)
Most of the application in drone technology is based on mobile aerial platforms for highly advanced data acquisition images. Drones can be fitted with different image data sensors based on the requirements of a particular agricultural project. The current conventional application based on acquired image data in drones is used to assess whether crops are healthy or not so that action can be taken. The NDVI view of particular areas within an agricultural field makes it possible to analyze the absorption of solar radiation, which, therefore, gives a clear picture of the condition of crops being monitored. This method has been in use for several decades using helicopters or plane-borne cameras but the resolution of the images has not been good enough. However, drone technology has NDVI mapping technology which has unmatched capability and accuracy (Pradeep et al., 2019). Therefore, it is possible to monitor the condition of not only the targeted plants but also the different parts of them.
The information obtained enables the farmers to identify diseases and pests early enough before they cause havoc to the plants. The areas that are precisely mapped by the drones can then be addressed through precision spraying of chemicals by drones to kill the pests, treat the diseases, weeds, or sprinkle water to the plants that require them. The modern farmer has a complex of issues that influence the success of their agricultural practices. They range from the quality of soil, access to water, variable seasons for growing crops, and the presence of insects and weeds. Due to such uncertainty, the farmers have turned to sophisticated technology to help in solving these problems and provide efficient and faster solutions that can make their agricultural business viable (Toro & Tsourdos, 2018). The drones’ technology will enable the farmers to obtain massive data that can improve profitability and help in making better management decisions.
The drone spraying technology was first introduced in Japan in the 1980s using unmanned helicopters with pesticide tanks and spraying pesticides. A modern drone can spray one hectare in ten minutes if it has a capacity of 10 liters and its discharge rate is one liter per minute. The spraying is synchronized and paired with the above explained automated analytics capabilities and imaging so that the affected areas can be addressed with precision (Pradeep et al., 2019). This method leads to the effective use of chemicals and improves dosage in the areas affected.
Artificial Intelligence Sensors
With the dawn of Artificial Intelligence (AI) technology, which is mainly focused on machine learning, the software-defined sensor’s capabilities are increasing at a tremendous rate. The AI-defined sensors which are used to analyze data, enable strong classifications and predictions using the sensor signals. This method is far much effective when compared with other methods such as the models which are premised on physics. The drones that are based on AI depend on computer vision whereby they can detect and point objects while they are flying (Lindeen, 2017). This technology allows the information to be recorded and analyzed on the ground where the team that operates the drone is stationed.
Computer Vision
The computer vision functions through onboard high-performance image processing sensors under a neural network. A neural network refers to a layered architecture used in implementing algorithms in machine learning. Through the neural network, the drones detect objects, classify, and track them. All three processes are done in real-time so that the drones can locate and track the targets as they avoid collisions. For the neural networks to be implemented in drones, the operators have to train the machine learning algorithms so that they can recognize and classify objects in different contexts. This is achieved by providing the algorithm with the images which are specially marked. The images command the neural network traits of different objects and transfer them the information on how different one object is from the other (Lindeen, 2017). The neutral networks which are more advanced continue getting fed without being supervised during their operation process, therefore, improving their detection and analysis.
Imagery Sensors
The most important components of the AI are the drone sensors which are used to collect all the data that is processed by the systems in the drone which include the environmental and visual data. The data collected is then delivered to the models of machine learning to determine the object which it should prioritize and which it should not. Furthermore, the data collected determines where the drone should and should not fly to (Pradeep et al., 2019). The sensor data is mostly used after the landing of a drone in analyses that are not flight-related.
Multispectral imaging camera sensors on agricultural drones allow the farmer to manage crops, and irrigation more effectively. There are several benefits for the farmer and the environment as a whole when drone technology is used. One of the most profound benefits is the reduction of production costs, increased crop yields, and avoidance of water and chemical wastage. The multispectral remote sensing camera technology uses red, green, red-edge, and wavebands which are near-infrared to capture both the invisible and visible images of the crops in an agricultural field. The images are integrated with specialized agricultural software which analyses the information and output into data that is usable (Lindeen, 2017). This method of land telemetry and the crop data permit the farmer to plan monitor and manage the agricultural field effectively saving money and time and reducing any wastage of pesticides.
Viewing the health of the plants using the naked eye has significant limitations and it is equally reactionary, therefore, the need to use advanced technology. The multispectral sensors allow the grower to observe further than what the naked eye can do. The most important benefit of this type of imagery is that it can monitor the crops all through their life cycle. The multispectral sensors capture data from the farm at specific frequencies in the electromagnetic spectrum. The wavelengths are separated using instruments or filters which are sensitive to particular wavelengths which include light from frequencies that are beyond human sight such as the infrared (Toro & Tsourdos, 2018). The special imaging also allows the extraction of more information that cannot be seen by the human eye.
Multispectral Camera Imaging for Agriculture
All surfaces reflect some of the light they receive. Objects that have different features on their surfaces absorb or reflect the radiation from the sun in different ways. The reflectance, which is the ratio between the reflected and incident light is usually expressed as a percentage. The reflectance of vegetation is used in getting the vegetation indices. The indices are then used in analyzing different ecologies of plants. The vegetation indices are obtained by measuring two or more wavelengths which are then used to analyze specific characteristics of vegetation such as the water content (Lindeen, 2017). This information is then used by the farmers to make informed choices on the next course of action.
Most of the farming precision tools and crop stress applications are constructed around the indices of vegetation. The tools give complete solutions which include the analysis of the multispectral data. The water curve has a considerably high absorption near the infrared wavelengths and beyond. Due to the absorption property of the water curve, all the features that contain water can easily be located, detected, and delineated through the remote sensing data (Toro & Tsourdos, 2018). This technology enables the drones to target only the affected parts.
Conclusion
The agricultural sector is known to embrace emerging technologies quickly which ensures that their business is running smoothly. The use of drones in the agricultural field is the next technological opportunity that will help the agricultural sector meet the ever-growing and changing demands of the sector. Advanced image data analytics and drone technology tools are vital in the agriculture sector due to the changing human demographics and food consumption needs. If the drone applications are implemented effectively in the agricultural sector, then the problems that the farmers are currently facing, such as the high cost of production and lack of labor, can be eliminated. However, the right setup and strategies are required to fully leverage this technology. With the booming drone industry and sensor technology as well as the availability of analytics and data processing tools, the required solutions need to be prepared and planned with maximum caution.
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