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Unmanned Aerial Vehicles in Smart Agriculture: Applications, Requirements and Challenges

Praveen Kumar Reddy Maddikunta, Saqib Hakak, Mamoun Alazab, Sweta Bhattacharya, Thippa Reddy Gadekallu, Wazir Zada Khan, Quoc-Viet Pham

arXiv:2007.12874v1eess.SPcs.NI

TL;DR

UAV adoption in smart agriculture is constrained by cost, control ease, sensor requirements, and unresolved operational challenges. This paper surveys Bluetooth Smart-enabled UAV architectures, agricultural sensors, applications, requirements, and challenges, identifying low-cost smartphone access and farmer acceptance as central considerations.

  • Problem

    Smart agriculture lacks a consolidated account of suitable agricultural sensors, UAV requirements, applications, and challenges affecting affordable, accessible operation.

  • Method

    The paper explores Bluetooth Smart-enabled UAV architectures and case studies alongside agricultural sensor types, applications, requirements, and challenges.

  • Results

    The paper identifies sensor categories, UAV applications, and enabling requirements including farmer acceptance and result accuracy for smart agriculture.

  • Takeaways & Limitations

    Bluetooth Smart is presented as a low-cost, smartphone-accessible option for UAV-based agricultural applications such as monitoring, spraying, irrigation management, and crop planting.

  • Takeaways & Limitations

    Bluetooth-based UAV operation faces interference risks from shared 2.4 GHz devices, potentially causing disconnections or poor performance as device density rises.

Abstract

from arXiv · show

In the next few years, smart farming will reach each and every nook of the world. The prospects of using unmanned aerial vehicles (UAV) for smart farming are immense. However, the cost and the ease in controlling UAVs for smart farming might play an important role for motivating farmers to use UAVs in farming. Mostly, UAVs are controlled by remote controllers using radio waves. There are several technologies such as WiFi or ZigBee that are also used for controlling UAVs. However, Smart Bluetooth (also referred to as Bluetooth Low Energy) is a wireless technology used to transfer data over short distances. Bluetooth smart is cheaper than other technologies and has the advantage of being available on every smart phone. Farmers can use any smart phone to operate their respective UAVs along with Bluetooth Smart enabled agricultural sensors in the future. However, certain requirements and challenges need to be addressed before UAVs can be operated for smart agriculture-related applications. Hence, in this article, an attempt has been made to explore the types of sensors suitable for smart farming, potential requirements and challenges for operating UAVs in smart agriculture. We have also identified the future applications of using UAVs in smart farming.

I. INTRODUCTION · II. BACKGROUND

The paper frames smart agriculture as an ICT-enabled approach for sustainable food production and examines UAVs, sensors, Bluetooth Smart, requirements, applications, and challenges. It introduces UAV capabilities and agricultural uses, including monitoring, spraying, weed and disease detection, and live data capture.

  • I. INTRODUCTION: Smart agriculture applies ICT, including IoT, remote sensing, and UAVs, to support sustainable and clean food production –.
  • I. INTRODUCTION: UAVs combine embedded systems, GPS, sensors, and cameras to support remote operation and image transmission, while enabling missions too dangerous for humans.
  • I. INTRODUCTION: UAVs support agricultural monitoring through mobility in variable weather and high-resolution imaging from approximately 50 to 100 meters.Their data can help assess crop quality and monitor attacks by pests, weeds, and animals, with remote access through cloud platforms and apps.
  • I. INTRODUCTION: The paper explores Bluetooth Smart, UAV types, and agricultural sensors, including sensor applications and features for smart agriculture.
  • I. INTRODUCTION: It identifies enabling requirements, presents Bluetooth Smart-enabled sensor and UAV case studies, and outlines future challenges and research directions.
  • II. BACKGROUND: UAVs are pilotless aircraft that fly autonomously while their motion is remotely controlled, and they include sensors and cameras that record and relay images,.
  • II. BACKGROUND: In agriculture, UAVs are used for crop monitoring, spraying, weed detection, and disease detection, supported by low maintenance, fast setup, low acquisition costs, and live data capture.The background section covers UAV architecture, agricultural applications, and UAV sensors.

A. Overview of Bluetooth Smart · B. Types of Unmanned Aerial Vehicles · Multi Rotor UAVs:

The section introduces Bluetooth Smart as a lower-power, lower-cost wireless technology and outlines UAV types, emphasizing multi-rotor categories, common uses, and operational limitations. It also notes BLE’s integration into Bluetooth Core Specification 4.0 and its long battery life.

  • A. Overview of Bluetooth Smart: BLE supports technical applications in medical, environmental, safety, and energy sectors.
  • A. Overview of Bluetooth Smart: Compared with classic Bluetooth, BLE uses less power and costs less while maintaining the same communication range.
  • A. Overview of Bluetooth Smart: BLE was incorporated into Bluetooth Core Specification 4.0 in July 2010 as Bluetooth Smart, alongside classic and high-speed Bluetooth protocols.
  • A. Overview of Bluetooth Smart: BLE devices can operate from a coin-cell battery for months or even years, with different BLE versions summarized in Table I [13].
  • B. Types of Unmanned Aerial Vehicles: The paper discusses different types of UAVs.
  • Multi Rotor UAVs:: Multi-rotor UAVs are mainly used for aerial surveillance and photography, are easy to manufacture, and are the cheapest UAV type.
  • Multi Rotor UAVs:: Multi-rotor categories include tricopters with three rotors, quadcopters with four, hexacopters with six, and octocopters with eight.
  • Multi Rotor UAVs:: Multi-rotor UAV limitations include approximately 30 minutes of flight time and limited speed, making them unsuitable for long-distance surveillance.

Fixed wing UAVs: … 1) Location-based Sensors:

The section contrasts UAV categories used in smart agriculture and introduces location-based sensors for precision farming. Fixed-wing UAVs support long-distance autonomous operations, while location sensors help identify field areas and crop-life-cycle positions.

  • Fixed wing UAVs:: Fixed-wing UAVs operate autonomously without an onboard pilot and are suited to long-distance operations.Their average flight time is 2 hours, while recent models can fly up to 16 hours.
  • Fixed wing UAVs:: Fixed-wing UAVs require runways for launching and involve high costs and highly skilled operating training.
  • Single Rotor UAVs:: Single-rotor UAVs resemble helicopters, using one large rotor and a smaller tail rotor.
  • Single Rotor UAVs:: Single-rotor UAVs can fly longer than multi-rotor UAVs but are more complex, operationally riskier, and more expensive.
  • Hybrid Vertical Take-off and Landing (VTOL):: Hybrid VTOL UAVs combine fixed-wing and rotor-based designs, include sensors, and can be controlled remotely.Table II presents commonly used agricultural UAVs.
  • C. Types of Agricultural Sensors: The sensor section explores sensor types suitable for smart farming.
  • 1) Location-based Sensors:: Location-based sensors identify areas and spots in agricultural fields and support farmers across different crop-life-cycle stages,.GPS receivers normally determine a point’s longitude and latitude using a GPS satellite network, supporting precision agriculture by locating field positions.

2) Electrochemical Sensors: · 3) Temperature and Humidity Sensors: · 4) Optical Sensors:

The sections describe electrochemical, temperature and humidity, and optical sensors used to monitor soil, environmental conditions, and crop health in smart agriculture. Optical sensing ranges from affordable RGB cameras to multispectral and hyperspectral systems supporting precision analysis and early problem detection.

  • 2) Electrochemical Sensors:: Electrochemical sensors detect pH and soil nutrient levels by using electrodes to identify specific ions in biological samples such as plants and soil.
  • 3) Temperature and Humidity Sensors:: Temperature and humidity sensors monitor environmental factors that affect crop health and growth, helping farmers adjust fertilizer and water quantities.Available devices include wireless-enabled, battery-operated sensors for fields and greenhouses.
  • 4) Optical Sensors:: Optical sensors convert light rays into electrical signals and include RGB, converted near-infrared, multispectral, and high-resolution spectrometer systems used in UAV precision agriculture.
  • 4) Optical Sensors:: RGB sensors are widely used in UAV smart agriculture because their cameras reproduce human-visible red, green, and blue bands while remaining affordable and lightweight.They are also effective for creating orthomosaic maps.
  • 4) Optical Sensors:: Multispectral sensors capture high-spatial-resolution imagery and near-infrared reflectance, making them suitable for UAV-based agricultural analytics.
  • 4) Optical Sensors:: Multispectral data support crop-health analysis, precision insights into plant vigor and canopy cover, and early detection of diseases, weeds, pests, and vegetative biomass.Without such data, early detection and biomass calculation become almost impossible.
  • 4) Optical Sensors:: Hyperspectral sensors capture detailed spectral and spatial imagery using area detectors that convert incident photons into electrons through CCD or CMOS sensors.

5) Thermal infrared sensors: · III. APPLICATIONS AND CASE STUDIES OF UAV IN AGRICULTURE · A. Potential applications of UAV in Smart agriculture

Thermal infrared sensors capture temperature-based imagery for monitoring crop and soil conditions, while UAVs can reduce manual labor and support rapid, low-cost, high-resolution agricultural observation. The section introduces sensor types and major UAV-enabled agricultural applications.

  • 5) Thermal infrared sensors:: Thermal cameras use infrared sensors and optical lenses to capture emitted thermal energy and represent object temperatures as images.Objects above absolute zero emit infrared radiation at wavelengths proportional to their temperatures.
  • 5) Thermal infrared sensors:: Colored thermal imagery can distinguish warmer regions in yellow from cooler regions in blue.
  • 5) Thermal infrared sensors:: UAV-mounted thermal sensors support irrigation management by calculating soil and crop water stress.
  • 5) Thermal infrared sensors:: Thermal sensors mounted on UAVs can detect or predict crop diseases, including pathogens, and support soil-texture and crop-maturity mapping,.
  • A. Potential applications of UAV in Smart agriculture: UAV adoption in agriculture reduces manual farming labor and enables detailed observation of cultivation fields, including areas obscured below cloud coverage.
  • A. Potential applications of UAV in Smart agriculture: UAVs offer accelerated deployment and high-resolution image capture at minimal cost, supporting major agricultural implementation areas discussed in the section.
  • 5) Thermal infrared sensors:: The section presents different types of agricultural sensors in Table III.

1) UAV as Sky-farmers: … 5) UAV is Artificial Pollination:

UAVs extend smart agriculture from aerial field and livestock monitoring to precision crop assessment, irrigation data collection, aerial mustering, and artificial pollination. Their sensors and imaging support detection of crop, soil, irrigation, pest, livestock, and pollination-related conditions.

  • 1) UAV as Sky-farmers:: UAVs provide a bird’s-eye view of cultivation fields, revealing irrigation issues, soil variability, and pest infestations while supporting livestock counting and food-pattern studies.Sky-level observations help farmers prioritize detected problems.
  • 2) UAVs in Precision Agriculture:: Small UAVs offer a feasible, lower-cost alternative to satellite and manned-aircraft remote sensing by producing high-quality hyperspectral and multispectral imagery.The imagery supports crop-health monitoring through remote sensing and image analytics.
  • 2) UAVs in Precision Agriculture:: Vegetation indices derived from UAV imagery, including NDVI, help assess biomass and identify crop variability, diseases, pest infestations, and nutrient deficiencies.Both fixed-wing and rotary-wing UAVs are used in precision agriculture.
  • 3) UAV in Irrigation Monitoring:: UAVs acquire irrigation data at any time and at minimum cost, addressing the lack of adequate and accurate information needed for effective irrigation management.Micro UAVs are presented as more appropriate than conventional UAVs for irrigation-data collection.
  • 4) UAV in Aerial Mustering:: UAVs automate aerial mustering by locating and gathering animals across large areas, avoiding the training, certification, fuel costs, and risks associated with helicopters.The task traditionally performed by sheep dogs or cowboys can therefore be supported by aerial vehicles.
  • 5) UAV is Artificial Pollination:: Robotic pollinators are gaining momentum in response to worldwide concern over declining honeybee populations.The paper presents artificial pollination as an emerging UAV-related agricultural application.
  • 5) UAV is Artificial Pollination:: AIST developed a micro UAV for artificial pollination using gel-coated animal hair to carry pollen, with cameras, GPS, and AI technologies.The UAV’s wind power is also used in the artificial-pollination process.

B. Case studies · 1) Use Case 1: Renewable Energy based UAV for Agriculture: · 2) Use Case 2: Bluetooth Embedded UAV for Air traffic Control:

The case studies present Bluetooth-enabled UAVs for agricultural operations and aerial-traffic control. They emphasize reduced labor and cost, broad agricultural applications, aerial-device detection, and signal-obstruction challenges.

  • 1) Use Case 1: Renewable Energy based UAV for Agriculture:: The READ pesticide-spraying hexacopter uniformly distributes pesticides and fertilizers while reducing manual labor and farmer workload.The case study presents the UAV as improving farmer security during cultivation-field spraying.
  • 1) Use Case 1: Renewable Energy based UAV for Agriculture:: The renewable-energy UAV is presented as a potential smart-farming solution for field operations.Figure 2 illustrates the renewable-energy UAV used in smart farming.
  • 1) Use Case 1: Renewable Energy based UAV for Agriculture:: Bluetooth-embedded UAVs can support crop monitoring, precision agriculture, spraying, irrigation management, and crop planting.The framework is described as a potential UAV solution offering higher efficiency, reliability, and reduced cost.
  • 2) Use Case 2: Bluetooth Embedded UAV for Air traffic Control:: Growing UAV adoption could create aerial congestion, causing collisions, quadcopter damage, and substantial infrastructure losses.The case study identifies competition for shared airspace as a threat to safe UAV operation.
  • 2) Use Case 2: Bluetooth Embedded UAV for Air traffic Control:: Intel’s Bluetooth-enabled UAV technology is designed to broadcast aerial-device information for air-traffic control.The described system connects the UAV with an application that receives location data.
  • 2) Use Case 2: Bluetooth Embedded UAV for Air traffic Control:: Detection reaches almost 2,625 feet at low cost, but obstructions can weaken Bluetooth signals and hinder implementation in crowded locations.Intel’s falcon-like UAV structure embeds Bluetooth for location-data transmission to a connected application.

3) Use Case 3: Bluetooth Embedded UAV for Self Sustained Ecosystem: … B. Network Availability

The paper envisions integrating UAV agricultural applications into a self-sustained ecosystem, while identifying regulation, hardware, and network connectivity as requirements for effective operation. Bluetooth Smart supports local UAV-to-smartphone data transfer, but cloud or storage use requires strong internet connectivity and bandwidth.

  • 3) Use Case 3: Bluetooth Embedded UAV for Self Sustained Ecosystem:: Integrating irrigation, spraying, mapping, livestock management, and pest control could create a self-sustained agricultural ecosystem with reduced human intervention.
  • 3) Use Case 3: Bluetooth Embedded UAV for Self Sustained Ecosystem:: The proposed UAV platform combines sensors, auto drivers, simulators, and autonomous flight controls that communicate wirelessly to perform individual tasks.
  • 3) Use Case 3: Bluetooth Embedded UAV for Self Sustained Ecosystem:: Effective ecosystem operation depends on hardware for the UAV and associated devices, with wireless links connecting controllers and base stations for monitoring and control.
  • IV. REQUIREMENTS: Successful smart-agriculture UAV deployment requires addressing key operational requirements, including regulation, network availability, and supporting infrastructure.
  • A. Regulation of UAVs: Worldwide UAV regulation is a fundamental requirement because operating UAVs is prohibited in some countries, while agricultural-use laws have been published by organisations such as CTA.
  • B. Network Availability: Bluetooth Smart data transfer from UAVs to smartphones does not require internet access, but forwarding data to cloud or storage platforms requires strong internet connectivity and bandwidth.
  • B. Network Availability: Network glitches or weak connectivity can have serious consequences for real-time smart-agriculture applications, while stronger routing and internet connections are presented as improvements,.

C. Data Storage · D. Security and Privacy · E. Efficient and Low Energy Consumption

UAVs for smart agriculture require substantial onboard data storage, robust cybersecurity and privacy protections, and efficient low-energy operation. These requirements arise from data-intensive applications, cyberattack and surveillance risks, and limited flight endurance during complex sensing and processing tasks.

  • C. Data Storage: Data-intensive smart-agriculture applications require immense storage when UAVs operate without internet connectivity and must retain data onboard.Operations include capturing high-definition images, extracting log-files, and analysing data.
  • C. Data Storage: Onboard storage is especially important for static UAV deployments lacking internet connectivity, where collected data cannot be transferred immediately.
  • D. Security and Privacy: UAV operations must secure data against cyberattacks while preserving privacy during agricultural monitoring.
  • D. Security and Privacy: WiFi-based eavesdropping, denial of service, and information-injection attacks can severely disrupt UAV operations –.Privacy concerns include secretly photographing surrounding properties and spy-related activities.
  • E. Efficient and Low Energy Consumption: A typical UAV flies for only about 15-25 minutes on a single battery, constraining energy-intensive smart-agriculture operations.Examples include livestock monitoring, real-time data transmission, soil-moisture monitoring, weed detection, and humidity monitoring.
  • E. Efficient and Low Energy Consumption: Complex tasks such as long-duration flights and high-resolution infrared, multispectral, or hyperspectral imaging require greater processing power.

F. User Acceptance of UAV technology … 1) Short remote-range of BLE:

UAV adoption in agriculture is constrained by limited flight coverage, ethical and accuracy concerns, and the 100-meter range of BLE-enabled systems. Proposed future directions include distributed UAV networks, but synchronization and operational conditions remain important challenges.

  • F. User Acceptance of UAV technology: UAV adoption is hindered by the absence of standardized agricultural workflows, short flight times, and limited flight radius, which may not meet farmers’ acreage-coverage needs.Reported UAV flight times range from minutes to an hour, with each flight limited to a certain radius.
  • G. Operational Ethics: UAV use raises ethical concerns because both the drone’s activities and the user’s consequential actions determine how its operation is evaluated.Evaluation remains necessary unless the UAV is completely automated without human intervention.
  • H. Accuracy of Results: Although farmers are keen to adopt UAVs, the accuracy of agricultural UAV data can be questionable, affecting crop monitoring and connected irrigation or fertilization decisions.These applications use multispectral imaging sensors to measure reflected energy from crops.
  • H. Accuracy of Results: Variations in UAV altitude and sun angle can produce erroneous comparative analyses, vegetation assessments, and predictions with costly consequences for farmers.Controlling these factors requires skilled professionals.
  • V. CHALLENGES AND FUTURE RESEARCH DIRECTIONS: The paper frames these limitations as challenges for future research on agricultural UAV usage, particularly for proposed BLE-enabled systems.The identified challenges are intended to guide subsequent research directions.
  • 1) Short remote-range of BLE:: BLE’s maximum range is 100 meters, which is insufficient for large farmlands; a distributed network could use a farmer-controlled master UAV and appropriately spaced slave UAVs.The proposed architecture introduces synchronization between master and slave UAVs as an additional challenge.

2) Achieving Higher-Data rates for Dynamic Storage of Data: … VI. CONCLUSION

The paper identifies data-rate, interference, user acceptance, skill, and privacy challenges for BLE-controlled UAVs in smart agriculture, while concluding that low-cost, smartphone-accessible Bluetooth Smart supports sensor-enabled case studies.

  • 2) Achieving Higher-Data rates for Dynamic Storage of Data:: BLE-controlled smart-agriculture applications need higher data rates, particularly livestock monitoring, before deployment; BLE Version 5 theoretically reaches 24 Mbps.Future BLE versions may provide still higher data rates.
  • 3) Interference:: BLE’s 2.4 GHz frequency overlaps with WiFi, Zigbee, and regular Bluetooth, creating interference risks that can cause disconnections or poor performance as device density rises.Although rural farms may have limited interference, the challenge remains relevant for future deployments.
  • 4) UAV Technology Acceptance:: Farmers with limited flying skills or technical knowledge may struggle to operate UAVs accurately, reducing willingness to adopt the technology.User acceptance depends on both capability and willingness to use sophisticated UAV systems.
  • 4) UAV Technology Acceptance:: Privacy concerns and potential legal consequences of violating others’ privacy may further hinder agricultural UAV acceptance.Ensuring privacy during UAV use is therefore a material adoption requirement.
  • VI. CONCLUSION: The article explores UAV architecture, adaptation, usage, Bluetooth Smart-enabled agricultural sensors, and potential smart-agriculture case studies.These case studies connect UAVs with sensor-based agricultural applications.
  • VI. CONCLUSION: Bluetooth Smart was selected for its low cost and smartphone accessibility, but other technologies can replace it in implementation.The article presents Bluetooth Smart as a motivated implementation choice rather than an exclusive requirement.
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