Agricultural Unmanned Aerial Vehicle Research Articles

Novel Architecture for Wireless Power Transfer in Unmanned Arial Vehicles

There has been a rapid growth in research towards developing smart, low-cost, and high-performance unmanned aerial vehicles (UAVs) for enabling wireless data transfer and communication. The practical applications of UAVs in healthcare, surveillance, agriculture and other domains is increasing at a faster pace. Mass production of these UAVs is essential to meet the growing communication requirements. Plans towards standardization and global deployment of Internet-of-Things (IoT) technologies and fifth generation (5G) wireless communication are in progress. Functional diversity is established by the UAVs in enabling wireless communication with IoT and 5G technologies. The different UAVs used in wireless communication is studied and a hierarchical architecture is proposed to facilitate integrating these technologies into next-generation networks. The principles of aerodynamics, wireless communication, power transfer, area of coverage, delay, meteorological impacts and other design parameters and their tradeoffs is analyzed.

SP2F: A secured privacy-preserving framework for smart agricultural Unmanned Aerial Vehicles

The current advancement in Unmanned Aerial Vehicles (UAVs) and the proliferation of the Internet of Things (IoT) devices is revolutionizing conventional farming operations into precision agriculture. The agricultural UAVs combined with IoT use an open channel i.e., the Internet to assist cultivators with data collection, processing, monitoring, and making correct decisions on the farm. However, the use of the Internet opens up a wide range of challenges such as security (e.g., performing cyber-attacks), risk of data privacy (e.g., data poisoning and inference attacks), etc. The usage of current conventional centralized security measures has limitations in terms of a single point of failure, verifiability, traceability, and scalability. Motivated from the aforementioned challenges, we propose a Secured Privacy-Preserving Framework (SP2F) for smart agricultural UAVs. The proposed SP2F framework has two main engines, a two-level privacy engine, and a deep learning-based anomaly detection engine. In the two-level privacy engine, a blockchain, and smart contract-based enhanced Proof of Work (ePoW) is designed for data authentication, and to mitigate data poisoning attacks. A Sparse AutoEncoder (SAE) is applied for transforming data into a new encoded format for preventing inference attacks. In the anomaly detection engine, a Stacked Long-Short-Term Memory (SLSTM) is used to train and evaluate the results of the proposed two-level privacy engine using two publicly accessible IoT-based datasets, namely ToN-IoT and IoT Botnet. Finally, based on thorough analysis, and comparison, we identify that the SP2F framework outperforms several state-of-the-art techniques in both non-blockchain and blockchain frameworks.

Методика кластеризации сельскохозяйственных полей по RGB-изображениям беспилотных летательных аппаратов

The Earth remote sensing (ERS) technologies, that are being developed on the basis of satellite sensing and, more recently, unmanned aerial vehicles (UAVs), provide a significant potential for smart (precision) farming applications. Sensors (for example, digital RGB cameras) provide real-time data about the environment of the study area in the form of images. The sensors installed on unmanned aerial vehicles can be used in various applications related to assessing the quality of plowing, winter wheat seedlings, growing crops, etc. It becomes possible because they capture large areas with a set of images with a low time (several hours per 1000 hectares), but with a high spatial resolution (several centimeters). The remote sensing technology is expected to revolutionize the agriculture by enabling faster decision-making limited by few days only, as well as reducing costs and increasing yields. Despite the significant development, one of the areas of use of UAVs in smart agriculture is not yet as reliable and accurate as expected, mainly due to problems arising in the collection, processing and analysis of images. The main problem is that there is still no standardized workflow that includes the steps from collection to visualization of results when using UAVs in agricultural applications. One of the weak points of many image processing technologies is the insufficient quality of clustering, where each cluster found is associated with a certain type of surface. This article discusses the use of images obtained using UAVs for solving the challenges of smart agriculture. A clustering technique related to the parametric representation of histograms of brightness of color spaces and Greenness Indexes is discussed. The results of image processing are presented.

A Comparative Study on Application of Unmanned Aerial Vehicle Systems in Agriculture

Presently in agriculture, there is much ample scope for drone and UAS (Unmanned Aircraft System) development. Because of their low cost and small size, these devices have the ability to help many developing countries with economic prosperity. The entire aggregation of financial investments in the agricultural area has increased appreciably in recent years. Sooth to say, agriculture remains a massive part of the world’s commercial growth, and due to some complications, the agriculture fields withstand massive losses. Pets and destructive insects seem to be the primary reasons for certain degenerative diseases. It minimizes the potential productivity of the crops. For increasing the quality of the plants, fertilizers and pesticides are appropriately applied. Using UAVs (Unmanned Aerial Vehicles) for spraying pesticides and fertilizing materials is an exuberant contraption. It adequately reduces the rate of health dilemma and the number of workers, which is quite an impressive landmark. Willing producers are also adopting UAVs in agriculture to soil and field analysis, seed sowing, lessen the time and costs correlated with crop scouting, and field mapping. It is rapid, and it can sensibly diminish a farmer’s workload, which is significantly a part of the agricultural revolution. This article aims to proportionally represent the concept of agricultural purposed UAV clear to the neophytes. First, this paper outlines the harmonic framework of the agricultural UAV, and then it abundantly illustrates the methods and materials. Finally, the article portrays the outcome.

Application of Agricultural Unmanned Aerial Vehicle for Processing Agricultural Crops

The paper considers a new method of processing all the necessary surfaces of crops through the use of an unmanned aerial vehicle (UAV). At the moment, the processing of all surfaces of tall shrubs and individual sections of trees with spot spraying of a chemical liquid on a large agro-industrial scale is not possible due to the imperfection of modern methods of processing agricultural crops. The proposed agricultural unmanned aerial vehicle, due to spot cultivation of crops, is able to increase yields and bring additional profits to agricultural farmers. Ease of operation is the most important advantage of the proposed UAV, for the processing of crops using this UAV does not require special skills, as, for example, when operating agricultural aircraft and ground equipment. Depending on the type of crops and the characteristics of the local landscape, the proposed UAV for agricultural purposes will be spraying in the vertical direction (top to bottom) or at a given angle by changing the position of the lever and its further fixation on the boom, as well as processing in the horizontal plane. The degree of direct human participation in the control and management of the UAV is determined based on the choice of the mode of differential application of fertilizers and pesticides for a given area - stationary or dynamic. In an idealized system, a programmed electronic computer (ECM) in the form of a computer, capable of adjusting the flight and the introduction of chemical reagents in a constant mode, will assume the main role in controlling the movement by analyzing the readings of the instrument sensors. All this can be implemented in practice at the proper level with appropriate funding, and the results of such a project in the future will open a new stage in the sectoral processing of crops and pole crops.

Satellite monitoring problems in the aerospace complex

Задачі підсуNowadays with the rapid development of information technologies, UAV-based Remote Sensing (drone remote sensing) gives a new opportunities for conducting scientific research in a much more detail way. UAVs (unmanned aerial vehicles) give the opportunity to acquire data at sufficiently low cost. They also provide remote data more rapidly than piloted aerial vehicles. Nowadays drones are often used, because application of piloted aerial vehicles can be dangerous, difficult and expensive for some territories. Application of low altitude UAVs give a possibility to achieve images with a very high resolution and sufficient precision. In this article structure and main details of drones were considered. It also was noted, that technologies of UAV-based Remote Sensing are used in different areas.Agricultural drones help to analyze crops, make decisions on how to use the crop information and take the necessary actions to correct the problems. These unmanned aerial vehicles let to see fields from the sky. Agricultural drones are used to help increase crop production and monitor crop growth. Drones and sensors give a detail picture of fields. They can survey the fields periodically. Agricultural drones can reveal many issues such as soil variation, pest infestations and changes in the crops over time. They also show differences between healthy and unhealthy plants. Drones are flied over the crops and help to make decisions on how to proceed given the crop information. Nowadays there is a large capacity for growth in the area of agricultural unmanned aerial vehicles. With technology constantly improving, imaging of the crops will need to improve as well.Drones are used for exploring for minerals and mapping deposit sites, they are used in the oil and gas industry for remote monitoring. Drones can provide information of nature disasters and give help to assess property damage. They help to conduct forest monitoring and to assess plant health. Unmanned aerial vehicles are also used in a military capacity and ecological monitoring. It also was noted, that there is a large capacity for development and improvement of unmanned aerial vehicles.путникового моніторингу в аерокосмічному комплексі

Research on vision navigation and position system of agricultural unmanned aerial vehicle

ABSTRACT Precision agriculture requires the allocation of the minimum amount of resources to each piece of land in order to maximize the benefit while having a minimal economic and environmental impact. Based on this principle, precision pesticide spraying for farmland pests requires precise identification of plant features and in-flight management. The spraying rate spray needs to be accurate and uniform and targeted at the plant regions that suffer from disease and pests. The intelligent spraying technology is also used. In this paper, the navigation needs of a UAV (unmanned aerial vehicle) are studied. Hyperspectral imaging is used for field crops to combine plant location data with visual data for precision management of the crop spraying process. GPS (Global Positioning System) positioning and hyperspectral image features such as crop growth status and nutritional status are combined to achieve human-computer intelligent spraying visual servo control.

Farmer awareness, perceptions and adoption of unmanned aerial vehicles: evidence from Missouri

Unmanned Aerial Vehicles (UAVs) are expected to play an important role in the future of farming. Because UAVs can provide precise, real-time information on biotic and abiotic stressors in agricultural production while they can also carry out autonomous operations to counter them, they can enhance farm profitability while reducing the environmental footprint of agriculture. Yet little is known about the current adoption of UAVs in agriculture or about the profile of the adopters. In this study we report actual and expected adoption of UAVs for a rich cross section of crop farmers and examine the factors that shape such adoption. In our empirical analysis we describe the inherent farmer heterogeneity – as shaped by differential awareness of UAV applications, perceptions of technical complexities, expectations of economic and environmental benefits and various socioeconomic factors – and analyze which of all these factors shape individual farmer adoption of UAVs. We also estimate and describe a small number of farmer segments that might adequately describe general population tendencies in the adoption of UAVs.

New Fusion Algorithm-Reinforced Pilot Control for an Agricultural Tricopter UAV

Currently, fuzzy proportional integral derivative (PID) controller schemes, which include simplified fuzzy reasoning decision methodologies and PID parameters, are broadly and efficaciously practiced in various fields from industrial applications, military service, to rescue operations, civilian information and also horticultural observation and agricultural surveillance. A fusion particle swarm optimization (PSO)–evolutionary programming (EP) algorithm, which is an improved version of the stochastic optimization strategy PSO, was presented for designing and optimizing controller gains in this study. The mathematical calculations of this study include the reproduction of EP with PSO. By minimizing the integral of the absolute error (IAE) criterion that is used for estimating the system response as a fitness function, the obtained integrated design of the fusion PSO–EP algorithm generated and updated the new elite parameters for proposed controller schemes. This progression was used for the complicated non-linear systems of the attitude-control pilot models of a tricopter unmanned aerial vehicle (UAV) to demonstrate an improvement on the performance in terms of rapid response, precision, reliability, and stability.

Agricultural UAV Path Planning Based on Improved A* and Gravity Search Mixed Algorithm

In order to ensure the accuracy, efficiency and energy saving of Unmanned Aerial Vehicles (UAVs) in irregular working areas, this paper has carried out research on its path planning methods. Taking the heading angle and return point of agricultural UAVs as the optimization objectives, the plan was made by improving the A* and gravitational search algorithm to plan the optimal path. First, the execution process of the algorithm is analysed, the application model is solved, and then the simulation experiment is performed through MATLAB. The path calculated by the proposed method was also compared with the unplanned and gravitational search algorithm planned path respectively, which showed the non-spraying distance of the proposed method was reduced by 23.18% and 5.83%, while the excess coverage of pesticide spraying was reduced by 64.47% and 64.47%. The study indicated that the proposed method which could produce paths with less working time was a reasonable, feasible and useful solution for the path planning problem of the plant protection UAV.

Prospects of Improving Agricultural and Water Productivity through Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) are an alternative to costly and time-consuming traditional methods to improve agricultural water management and crop productivity through the acquisition, processing, and analyses of high-resolution spatial and temporal crop data at field scale. UAVs mounted with multispectral and thermal cameras facilitate the monitoring of crops throughout the crop growing cycle, allowing for timely detection and intervention in case of any anomalies. The use of UAVs in smallholder agriculture is poised to ensure food security at household level and improve agricultural water management in developing countries. This review synthesises the use of UAVs in smallholder agriculture in the smallholder agriculture sector in developing countries. The review highlights the role of UAV derived normalised difference vegetation index (NDVI) in assessing crop health, evapotranspiration, water stress and disaster risk reduction. The focus is to provide more accurate statistics on irrigated areas, crop water requirements and to improve water productivity and crop yield. UAVs facilitate access to agro-meteorological information at field scale and in near real-time, important information for irrigation scheduling and other on-field decision-making. The technology improves smallholder agriculture by facilitating access to information on crop biophysical parameters in near real-time for improved preparedness and operational decision-making. Coupled with accurate meteorological data, the technology allows for precise estimations of crop water requirements and crop evapotranspiration at high spatial resolution. Timely access to crop health information helps inform operational decisions at the farm level, and thus, enhancing rural livelihoods and wellbeing.

Precision Landing Test and Simulation of the Agricultural UAV on Apron.

Unmanned aerial vehicle (UAV) has been used to assist agricultural production. Precision landing control of UAV is critical for application of it in some specific areas such as greenhouses or livestock/poultry houses. For controlling UAV landing on a fixed or mobile apron/platform accurately, this study proposed an automatic method and tested it under three scenarios: (1) UAV landing at high operating altitude based on the GPS signal of the mobile apron; (2) UAV landing at low operating altitude based on the image recognition on the mobile apron; and (3) UAV landing progress control based on the fixed landing device and image detection to achieve a stable landing action. To verify the effectiveness of the proposed control method, apron at both stationary and mobile (e.g., 3 km/h moving speed) statuses were tested. Besides, a simulation was conducted for the UAV landing on a fixed apron by using a commercial poultry house as a model (135 L × 15 W × 3 H m). Results show that the average landing errors in high altitude and low altitude can be controlled within 6.78 cm and 13.29 cm, respectively. For the poultry house simulation, the landing errors were 6.22 ± 2.59 cm, 6.79 ± 3.26 cm, and 7.14 ± 2.41cm at the running speed of 2 km/h, 3 km/h, and 4 km/h, respectively. This study provides the basis for applying the UAV in agricultural facilities such as poultry or animal houses where requires a stricter landing control than open fields.

Field evaluation of spray drift and environmental impact using an agricultural unmanned aerial vehicle (UAV) sprayer

Unmanned Aerial Vehicle (UAV) applications at low-volume using fine and very fine size droplets have been adopted in several commercial spray scenarios allowing water-saving and high-efficiency operation in delivery of pesticides. However, spray drift associated with UAV applications, especially for fine droplets generated from spinning disk nozzles, has not been fully understood, raising environmental and regulatory concerns. The objectives of this study were to compare the drift potential of three different volume median diameter (VMD, or Dv0.5) of 100, 150 and 200 μm from a commercial quadcopter equipped with centrifugal nozzles exposed to different wind speeds under field conditions. Prior to field test, the droplet size of the centrifugal nozzle was measured by a laser-diffraction particle-size analyzer. The results showed that the relationship between rotation speed and Dv0.5 agrees with the negative power function. Field tests found that the deposition at 12 m downwind direction decreased by an order of magnitude compared with the average deposition within the in-swath zone. The deposition of almost all the treatments at 50 m downwind is lower than the detection limits of 0.0002 μL/cm2. Based on the results from this study, the drift distance of this specific very popular UAV model is much less than that of manned aerial applicators. Based on the predicted equation (R2 = 0.83), the detected drift amount increased with increasing wind speed and decreasing Dv0.5. This work provides basic information to quantify the effect of wind speeds and droplet sizes on UAV spray drift potential which supports on-going regulatory guideline development for spray buffer zone and drift risk assessment protocols.

CFD simulation and experimental verification of the spatial and temporal distributions of the downwash airflow of a quad-rotor agricultural UAV in hover

The plant protection effect of an agricultural unmanned aerial vehicle (UAV) with multiple rotors is closely related to its speed and direction and the spatial and temporal distributions of the effective coverage of the downwash airflow. By combining compressible Reynolds-averaged Navier-Stokes (RANS) equations, the shear stress transport (SST) k-ω turbulence model, sliding grid technology and the Semi-Implicit Method for Pressure Linked Equations (SIMPLE) algorithm, this paper establishes a computational fluid dynamics (CFD) model of the downwash airflow of the quad-rotor agricultural UAV in hover and discusses the distribution of the downwash airflow in the spatial and temporal dimensions. The simulation results show that most areas of the downwash airflow tend to be stable within 4.5 s, and due to the downward flow characteristics after the stabilization, the z-direction (perpendicular to the ground) velocity of the downwash airflow directly under the rotor is the highest with a maximum value of −8.96 m/s, which accounts for the main part of the downwash flow. The rotor downwash airflow flows downward in spiral form and the phenomenon of “contraction and expansion” is obvious during the downwash flow. Closer to the ground, the coverage area of the z-direction velocity is larger, the pressure distribution is more uniform, and the airflow exhibits an “upwash” phenomenon due to the “ground effect”. The test and simulation values at test points from 1 m to 2.5 m and 0.75 m exhibit the same variation trend with maximum relative errors of 10% and 15%, and the numerical simulation is accurate.

A Framework for Agricultural Pest and Disease Monitoring Based on Internet-of-Things and Unmanned Aerial Vehicles.

With the development of information technology, Internet-of-Things (IoT) and low-altitude remote-sensing technology represented by Unmanned Aerial Vehicles (UAVs) are widely used in environmental monitoring fields. In agricultural modernization, IoT and UAV can monitor the incidence of crop diseases and pests from the ground micro and air macro perspectives, respectively. IoT technology can collect real-time weather parameters of the crop growth by means of numerous inexpensive sensor nodes. While depending on spectral camera technology, UAVs can capture the images of farmland, and these images can be utilize for analyzing the occurrence of pests and diseases of crops. In this work, we attempt to design an agriculture framework for providing profound insights into the specific relationship between the occurrence of pests/diseases and weather parameters. Firstly, considering that most farms are usually located in remote areas and far away from infrastructure, making it hard to deploy agricultural IoT devices due to limited energy supplement, a sun tracker device is designed to adjust the angle automatically between the solar panel and the sunlight for improving the energy-harvesting rate. Secondly, for resolving the problem of short flight time of UAV, a flight mode is introduced to ensure the maximum utilization of wind force and prolong the fight time. Thirdly, the images captured by UAV are transmitted to the cloud data center for analyzing the degree of damage of pests and diseases based on spectrum analysis technology. Finally, the agriculture framework is deployed in the Yangtze River Zone of China and the results demonstrate that wheat is susceptible to disease when the temperature is between 14 °C and 16 °C, and high rainfall decreases the spread of wheat powdery mildew.

The Role of Drones as an Enabler for the 4th Agricultural Revolution

The domain of agriculture is constantly evolving in terms of means and tools used. The rapid evolution in Unmanned Aerial Vehicles (UAVs) and drone technology has facilitated a series of applications in the field of agriculture. The use of drones, combined with advanced ICT technologies as well as connected analytics methods, provides great potential in supporting and resolving some of the most challenging issues faced by the agricultural domain especially in real time data acquisition, monitoring and decision-making processes, triggering the so-called 4th agricultural revolution. The present paper provides an overview of the application of drones in the agriculture domain worldwide. Initially, the paper presents some introductory information concerning the technological evolution in the agricultural domain with special focus on the use of Unmanned Aerial Vehicles. It then conducts a research concerning different applications of UAVs in agriculture. It proceeds with discussing relevant existing research initiatives, highlighting the relevant advantages and the prospects in using drone technology in agriculture.

Methodological issues of improving the system of meteorological support of aviation for agricultural purposes

The direction of the agro-industrial complex development is considered through the use of agricultural aviation and unmanned aerial vehicles, in particular, the issues of integrated application of methods for meteorological conditions forecasting for solving problems of meteorological support for agricultural aviation are considered. The analysis of the impact of climate change on the development of dangerous weather events and complex weather conditions in areas located in different geographical zones was carried out, and their regional specificity was revealed. The identified climatic features of the considered areas indicate the priority importance of developing a regional observation network. It is assumed that the development and technical modernization of the meteorological network will significantly increase the efficiency of meteorological support for the agro-industrial complex. The article also discusses the use of various types of meteorological information in the model of regional meteorological support for aircraft flights in agricultural areas proposed by the authors. The model is based on a systematic approach in which the “crew - aircraft” system is considered as a single dynamic system that is continuously influenced by changing environmental factors.

The Identification of the Wind Parameters Based on the Interactive Multi-Models

The wind as a natural phenomenon would cause the derivation of the pesticide drops during the operation of agricultural unmanned aerial vehicles (UAV). In particular, the changeable wind makes it difficult for the precision agriculture. For accurate spraying of pesticide, it is necessary to estimate the real-time wind parameters to provide the correction reference for the UAV path. Most estimation algorithms are model based, and as such, serious errors can arise when the models fail to properly fit the physical wind motions. To address this problem, a robust estimation model is proposed in this paper. Considering the diversity of the wind, three elemental time-related Markov models with carefully designed parameter α are adopted in the interacting multiple model (IMM) algorithm, to accomplish the estimation of the wind parameters. Furthermore, the estimation accuracy is dependent as well on the filtering technique. In that regard, the sparse grid quadrature Kalman filter (SGQKF) is employed to comprise the computation load and high filtering accuracy. Finally, the proposed algorithm is ran using simulation tests which results demonstrate its effectiveness and superiority in tracking the wind change.

Citrus Tree Segmentation from UAV Images Based on Monocular Machine Vision in a Natural Orchard Environment.

The segmentation of citrus trees in a natural orchard environment is a key technology for achieving the fully autonomous operation of agricultural unmanned aerial vehicles (UAVs). Therefore, a tree segmentation method based on monocular machine vision technology and a support vector machine (SVM) algorithm are proposed in this paper to segment citrus trees precisely under different brightness and weed coverage conditions. To reduce the sensitivity to environmental brightness, a selective illumination histogram equalization method was developed to compensate for the illumination, thereby improving the brightness contrast for the foreground without changing its hue and saturation. To accurately differentiate fruit trees from different weed coverage backgrounds, a chromatic aberration segmentation algorithm and the Otsu threshold method were combined to extract potential fruit tree regions. Then, 14 color features, five statistical texture features, and local binary pattern features of those regions were calculated to establish an SVM segmentation model. The proposed method was verified on a dataset with different brightness and weed coverage conditions, and the results show that the citrus tree segmentation accuracy reached 85.27% ± 9.43%; thus, the proposed method achieved better performance than two similar methods.

Monitoring of agricultural land productivity using unmanned aerial vehicles and artificial neural networks

The results of the analytical review of the use of unmanned aerial vehicles (UAVs) and artificial neural networks in agricultural production are presented. They can be used as aerial robots that perform the function of aerial photography, transportation of technological components, such as plant protection products and perform other similar functions. On the aircraft, some other functional equipment can be installed: thermal imagers, multispectral and IR cameras, etc. With the help of the data obtained from the UAV, it is possible to create an orthophotoplan or 3D model of the terrain, create a map of heights, determine the state of the field, crops and determine their vegetation indices NDVI. The multi-level classification of areas of application of UAVs in agricultural production is proposed. Classification involves the ordering of areas of application of UAV in agriculture depending on the composition in use. A conceptual model of a software package designed to obtain and process remote sensing data using UAVs in different parts of the spectrum has been developed. The software package is designed to obtain and process the results of monitoring and subsequent analysis of the totality of the calculated vegetation indices. The main research tasks solved by the developed software, which determine its structure, are formulated. To predict the yield of different crops, a method of applying the results of aerial photography in conjunction with experimental data on the biological development of crops has been developed. For the practical use of the developed methodology, a database for each culture is formed. The obtained results are used to construct regression and matrix mathematical models of the relationship of optical-spectral characteristics with the productivity of crops.