Agriculture spraying drone typically highlights the technology's ability to enhance farming
efficiency, precision, and sustainability. Here's a general overview of what such an abstract
might contain, drawing from common themes in research:
The population is increasing tremendously and with this increase the demand of food. The
traditional
methods which were used by the farmers were not sufficient enough to fulfil these requirements.
Thus,
new automated methods (Drone technology) were introduced.
Key elements often found in abstracts:
● Problem Statement:
○ Traditional agricultural spraying methods often result in excessive chemical usage,
uneven application, and potential harm to human health and the environment.
○ Labor-intensive manual spraying is inefficient and costly.
● Solution:
○ Agricultural spraying drones offer a precise and efficient alternative, enabling
targeted application of pesticides, herbicides, and fertilizers.
○ These drones utilize GPS, sensors, and sometimes AI to optimize spraying patterns
and minimize waste.
● Benefits:
○ Increased efficiency and reduced labor costs.
○ Precise application, minimizing chemical usage and environmental impact.
○ Improved crop health monitoring and yield.
○ Enhanced safety for agricultural workers.
● Technological Aspects:
○ Mention of key technologies like GPS navigation, variable rate spraying, and
potentially image processing for targeted application.
○ Also mentioning the types of payloads and flight times.
● Potential Impact:
○ The technology's potential to contribute to sustainable agriculture and food security.
In essence, an abstract for an agricultural spraying drone will emphasize:
● The shift from traditional, less precise methods to a technology-driven, efficient approach.
● The positive impacts on resource conservation, environmental protection, and agricultural
productivity.
I hope this helps.
Introduction
As much as India depends upon the agriculture, still it is far short from adapting latest
technologies in it to get good farm. Developed countries have already started use of UAV’s in
their precision agriculture, photogrammetry and remote sensing. It is very fast and it
could reduce the work load of a farmer. In general, UAVs are equipped with the cameras and
sensors for crop monitoring and sprayers for pesticide spraying. In the past, variety of UAV
models running on military and civilian applications. A technical analysis of UAVs in precision
agriculture is to analyze their applicability in agriculture operations like crop monitoring,crop
height Estimations , pesticide Spraying ,soil and field analysis.However, their hardware
implementations are purely depended on critical aspects like weight, range of flight, payload,
configuration and their costs.Drones have long been thought of as expensive toys. One area
that has seen little attention from drones, perhaps to its detriment, is the agricultural sector.
Drones can fly autonomously with dedicated software which allows making a flight plan and
deploying the system with GPS and feed in various parameters such as speed, altitude, ROI
(Region of Interest), geo-fence and fail-safe modes. Drones are preferred over full size
aircrafts due to major factors like combination of high spatial resolution and fast turnaround
capabilities together with low operation cost and easy to trigger. These features are required
in precision agriculture where large areas are monitored and analyses are carried out in
minimum time. Using of aerial vehicle is possible due to miniaturization of compact cameras
and other sensors like infrared and sonar.The Japanese were the first to successfully apply
UAS technology to agricultural chemical spraying applications in 1980’s ], and crop dusting
in the 1990’s. As of 2001 1,220 units of Yamaha unmanned helicopters had been sold and
were in use in Japan . Over 2,000 Yamaha RMAX unmanned hellos spray about 2.5 million
acres a year, covering about 40% of the country’s rice paddies in Japan . U.S. is behind
Japan in UAV agricultural applications,and advocates have to navigate through a minefield
of privacy and legal issues in order to legally implement them into society. Although the use
of UAVs in agriculture has been steadily increasing, such growth is hindered by many
technical challenges that still need to be overcome. Among those applications, stress
detections and quantification is arguably the one that has received the greatest amount of
attention, most likely due to the potential positive impact that early stress detection can have
on the agricultural activity. As a consequence, a large amount of data has been generated
and a wide variety of strategies have been proposed, making it difficult to keep track of the
current state of the art on the subject and the main challenges yet to be overcome. In this
context, the objective of this article is to provide a comprehensive overview of the application
of Drone (UAVs) in agriculture to monitor and assess plant stresses such as drought,
diseases, nutrition deficiencies, pests, and weeds etc.Crop monitoring for insects, nutrients,
disease, water-stress, and overall plant health is an important aspect of precision agriculture
operations. This has been carried out by examination from the air or on the ground
traditionally, but these methods are limited by cost of operation and availability. Imagery
created using light aircraft usually has higher resolution, is cheaper and more up to date, but
it is still relatively expensive per acre. Small UAV or UAS can be used to acquire
temporal/spatial data with a resolution of centimetres, and can fly consistently with
repeatability of route and altitude to continuously cover the crop’s fields. The acquisition of
the images by UAVs is manageable and not as influenced by cloud cover. As indicated, UAS
has been used in many areas in agriculture, although they still have many limitations and
challenges to overcome. This paper summarizes major UAS applications and technologies
for agriculture, and discusses the challenges of using UAS in an agricultural context.
1.
Crop health monitoring:
Drones can be used for monitoring the conditions of crops throughout the crop
season so that the need-based and timely action can be taken. By using different
kinds of sensors pertaining to visible, NIR and thermal infrared rays, different
multispectral indices can be computed based on the reflection pattern at different
wavelengths. These indices can be used to assess the conditions of crops like water
stress, nutrient stress, insect-pest attack, diseases, etc. The sensors present over
thedrones can see the incidence of diseases or deficiency even before the
appearance of visible symptoms. Thus, they serve as a tool for early detection of the
diseases. In this way, drones can be used for early warning system so that timely
action can be taken by applying the remedial measures based on the degree of the
stress. UAVs (Drone) are capable of observing the crop with different indices. The
UAVs are able to cover up hectares of fields in single flight. For this observation
thermal and multi spectral Cameras to record reflectance of vegetation canopy, which
is mounted to downside of the quad copter. The camera takes one capture per
second and stores it into memory and sends to the ground station through
telemetry.The data coming from the multispectral camera through telemetry was
analysed by the Geographic indicator Normalized Difference Vegetation Index (NDVI)
represented in equation.
NDVI = (RNIR – RRED)/ (RNIR + RRED)
RNIR = Reflectance of the near infrared band.
RRED = Reflectance of the red band.
Normalization difference vegetation index is a simple metric which indicates the
health of green vegetation. The basic theory is chlorophyll strongly reflects near
infrared light (NIR, around 750nm) while red and blue are absorbed. Chlorophyll
reflects strongly which is why plants appear green to us but reflection in NIR in even
greater, this plays a very important role and helps in rendering precise data for
analysis. The calculations gives the values -1 to +1; near to 0 (ZERO) indicates no
vegetation on the crop and near to +1 (0.8 to 0.9) means highest density of green
leaves on the crop . Based upon these result farmers easily identify crop health
condition also monitoring crops. Based upon these results, farmers easily identify the
field where can spray the pesticides. Drones can be used for monitoring the
conditionsof crops throughout the crop season so that the need-basedand timely
action can be taken. The quick and appropriate action can prevent yield loss. This
technology will eliminate the need to visually inspecting the crops by the farmers.
Theycan monitor the horticultural crops or other crops present inremote areas like
mountainous regions. They can also monitorthe tall crops and trees efficiently, which
are otherwise challenging to scout physically by farmers.
Water stress monitoring:
The characterization of water stress on crops is a complex task because the effects
of drought affect (and can be affected by) several factors . Variables derived from
thermal images often rely on very slight temperature variations to detect stresses and
other phenomena. As a result, thresholds and regression equations derived under
certain conditions usually do not hold under even slightly different circumstances. For
example, different genotypes of a given crop may present significantly different
canopy temperatures under the same conditions due to inherent differences in
stomatal conductance and transpiration rates.Researchers used various types of
sensors and model to identify water stresses: Using multispectral or hyper spectral
images and the vegetation indices (NDVI, GNDVI, etc.)spectral transformations
aiming at highlighting certain vegetation properties. Using multispectral or hyper
spectral images and the photochemical reflectance index (PRI)reflectance
measurement sensitive to changes in carotenoid pigments present in leaves. Using
thermal infrared imagery and the difference between the canopy and air
temperatures (Tc - Ta)some studies use the canopy temperature directly. Using
thermal infrared imagery and the crop water stress index (CWSI), used in References
is based on the difference between canopy temperature and air temperature (Tc -
Ta), normalized by the vapour pressure deficit (VPD) [21]. A related variable, called
Non Water Stress Baseline (NWSB), was also used in some investigations.The
rationale behind this is that water stress induces a decrease in stomatal conductance
and less heat dissipation in plants, causing a detectable increase in the canopy
temperature . Red-Green-Blue (RGB) images have been employed sparingly, usually
associated with multispectral or thermal images for the calculation of hybrid variables
such as the Water Deficit Index (WDI) . Chlorophyll fluorescence, calculated using
narrow-band multispectral images, has also been sporadically applied to the problem
of water stress detection and monitoring.
Nutrient status and deficiency monitoring:
Plants need the appropriate levels of nutrients in order to thrive and produce a strong
yield. The appropriate levels of nitrogen will ensure strong growth of vegetation and
foliage, appropriate levels of phosphorous are required for strong root and stem
growth and appropriate levels of potassium are necessary for improving of the
resistance to disease and also to ensure a better quality of crop. If soil lacks any of
these nutrients, the plant will become stressed and will struggle to thrive. NDVI Index
mosaics offer the possibility to identify exactly which areas of the crops are stressed
or struggling and to target directly these areas. The NIR/multispectral imagery
provided by the UAVs can identify these management zones long before the problem
become visible to the naked eye. This means that these management zones can be
targeted before crop development and yield is affected.Currently, the most common
way to determine the nutritional status is visually, by means of plant colour guides
that do not allow quantitatively rigorous assessments. More accurate evaluations
require laboratorial leaf analyses, which are time consuming and require the
application of specific methods for a correct interpretation of the data. There are
some indirect alternatives available for some nutrients, such as the chlorophyll meter
(Soil-plant analyses development (SPAD) for nitrogen predictions, but this is a time
consuming process and the estimates are not always accurate . Thus, considerable
effort has been dedicated to the development of new methods for the detection and
estimation of nutritional problems in plants Nitrogen is, by far, the most studied
nutrient due to its connection to biomass and yield. Potassium and sodium have also
received some attention. Multispectral images have been the predominant choice for
the extraction of meaningful features and indices, but RGB and hyper spectral
images are also frequently adopted. Data fusion combining two or even three types
of sensors (multispectral, RGB, and thermal) has also been investigated .The vast
majority of the studies found in the literature extracts vegetation indices (VI) from the
images and relates them with nutrient content using a regression model (usually
linear). Although less common, other types of variables have also been used to feed
the regression models, such as the average reflectance spectra , selected spectral
bands , colour features , and principal components . All of these are calculated from
hyper spectral images, except the colour features, which are calculated from RGB
images.
Diseases monitoring
Crop diseases can be devastating and classified as fungal, bacterial or viral. Drones
equipped with Infrared cameras can see inside plants, giving a clear image of the
condition thereof. If a farmer can detect an infection before it spreads, preventative
measures can be taken - like removing the plant -before the infection spreads to
neighbour plants. Image-based tools can, thus, play an important role in detecting
and recognizing plant diseases when human assessment is unsuitable, unreliable, or
unavailable, especially with the extended coverage provided by UAVs. RGB [39, 40]
and multispectral images have been preferred methods for acquiring information
about the studied areas, but hyper spectral and thermal images have also been
tested. The latter is employed mostly to detect water stress signs potentially caused
by the targeted disease.
.
Objectives of Agricultural Drones
1. Increase Crop Monitoring Efficiency
Enable real-time, large-scale monitoring of crop health, growth, and anomalies
using aerial imagery and sensors.
2. Enhance Precision Agriculture
Deliver precise data for targeted irrigation, fertilization, and pesticide application to
minimize waste and maximize yield.
3. Reduce Labor and Operational Costs
Automate labor-intensive tasks like surveying, spraying, and crop counting to
reduce manual workload and expenses.
4. Improve Decision-Making
Provide actionable insights through data analytics to support smarter farming
strategies and timely interventions.
5. Boost Yield and Productivity
Optimize resource usage and identify potential threats early to improve overall farm
productivity and crop output.
6. Support Sustainable Farming
Promote eco-friendly practices by reducing chemical overuse and conserving water
and soil through targeted application.
7. Enable Early Detection of Pests and Diseases
Use multispectral and thermal imaging to identify early signs of infestation or
disease for prompt treatment.
8. Facilitate Disaster and Damage Assessment
Quickly assess crop damage after natural disasters (e.g., floods, droughts) to inform
recovery efforts and insurance claims.
Plan of Work: Agricultural Spraying Drone
1. Introduction & Objective Setting
○ Define project goals (e.g., improve spraying efficiency, reduce chemical usage).
○ Identify target crops and field conditions.
○ Establish success metrics (e.g., coverage accuracy, cost savings).
2. Research & Feasibility Study
○ Study different types of spraying drones and spraying mechanisms (rotary
atomizers, pressure nozzles).
○ Analyze terrain, weather patterns, and crop types.
○ Assess regulatory requirements for drone use in agriculture.
3. Design & Development
○ Select or design drone hardware (frame, motors, GPS, tanks, sprayers).
○ Integrate payload system (chemical tank, spraying system).
○ Develop or adapt control software (route planning, automation, obstacle
avoidance).
○ Implement safety protocols (emergency landing, no-fly zones).
4. Field Testing & Calibration
○ Conduct initial trial flights on test fields.
○ Calibrate spraying systems for optimal flow rate and coverage.
○ Adjust flight path algorithms for terrain and crop layout.
5. Data Collection & Analysis
○ Monitor spray distribution accuracy, chemical usage, and flight stability.
○ Record weather and environmental variables.
○ Gather farmer/user feedback for improvements.
6. Optimization & Troubleshooting
○ Fine-tune software and hardware based on test results.
○ Improve flight duration, spray efficiency, and maintenance access.
○ Address issues like drift control and battery performance.
7. Training & Deployment
○ Train operators/farmers on usage, safety, and maintenance.
○ Provide documentation, manuals, and troubleshooting guides.
○ Deploy drones to real-world farms for operational use.
8. Evaluation & Reporting
○ Measure results against initial objectives.
○ Compare cost-effectiveness with traditional methods.
○ Create a final report with findings, improvements, and scalability plans.
