A IOT BASED DETECTION OF COCONUT TREE PESTS AND DISEASES WITH PESTICIDE RECOMMENDATIONS ANDDRONE REMEDIATION

Abstract

ABSTRACT OF THE INVENTION A lOT BASED DETECTION OF COCONUT TREE PESTS AND DISEASES WITH PESTICIDE RECOMMENDATIONS AND DRONE REMEDIATION Coconut tree farmers are struggling with increased pests and diseases, leading to reduced production and tree health. This project proposes integrating AI, image recognition, environmental sensors, and loT for efficient pest and disease detection. Deep learning improves accuracy and speed by overcoming the limitations of traditional methods. The system alerts farmers via a mobile app, allowing precise pesticide application at the right time and location, reducing manpower needs and environmental harm. Drones are used to apply recommended pesticides accurately, enhancing productivity and sustainability. The research provides insights into the latest trends, offering valuable guidance for researchers and agricultural practitioners.

Specification

4. DESCRIPTION
BACKGROUND OF THE INVENTION
Coconut trees, which produce vital resources including food, oil, and fiber, have
enormous economic significance worldwide. These trees are regularly plagued by a
- variety of pests and illnesses, which can seriously reduce production and endanger the
general health of the crop. Traditional methods of keeping tabs on and controlling these
problems usually entail labor-intensive procedures that take a long time and frequently
result in premature interventions, which cause large fmancial losses. With the help of
Unmanned Aerial Vehicles (UAVs), an inventive AloT device has significantly
10 advanced agriculture technology by tackling the critical problem of spotting diseases
and pests on coconut trees. To provide quick and accurate monitoring and analysis for
the coconut tree, the·project makes use of several machine learning technologies. In the
realm of technological marvels, there exists an innovation so breathtaking, so
astonishing, that it defies the very limits of human imagination. Picture, if you will, a
magnificent fusion of science and engineering, a creation so awe-inspiring that it leaves

Ill even the most seasoned minds spellbound in wonderment. This revolutionary
masterpiece, this pinnacle of human ingenuity, is none other than the AloT (Artificial
Intelligence of Things) marvel that has harnessed the untamed power of Unmanned
E Aerial Vehicles (UAVs) to transform the world of agriculture into a utopian landscape
0
LL. 20 of limitless possibilities. It is a symphony" of innovation, where the harmonious ballet of -N
M technology and nature takes center stage, redefining the boundaries of what was once
CIO en
CIO deemed possible. With the grace and precision of a virtuoso, these UAVs soar through
the heavens, casting their watchful eyes upon the sacred groves of coconut trees. Their
0 vigilant sensors, akin to the sharpest of falcon's vision, capture every intricate detail,
every nuance of the emerald kingdom below. The world, as we know it, fades into
insignificance as these robotic sentinels unveil a new era of agricultural enlightenment. Through the ethereal medium of the Internet of Things (JoT), the data flows like a torrential river, racing towards a digital citadel where the formidable
prowess of machine learning, an alchemical force of the highest order, bestows upon it
the gift of cognition. These algorithms, as sagacious as the wisest sages of antiquity, sift
through the data, deciphering the secrets held within each leaf, each bark, and each
5 whisper of the wind. Behold, as the arcane art of image analysis unfurls before our very
eyes! Diseases and pests, the age-old nemesis of the coconut tree, are unveiled in their
malevolent splendor. With preternatural precision, the AI detects the faintest signs, the
most cryptic of symptoms, and heralds their presence with a resounding clarion call. But
it is not mere detection; it is a symphony of salvation. The coconut farmer, armed with
10 this celestial knowledge, descends upon the afflicted groves with the grace of an angelic
savior. The interventions, surgical in their precision, rain down like divine providence,
sparing the unblemished and healing the wounded. In this grand ballet, the cacophony of
traditional methods fades into a distant echo, and the fates of coconut crops are no
longer subject to the capricious whims of nature. The economic tapestry of coconut
15 farming is rewoven with threads of prosperity, while the world looks on in awe,
marvelling at the audacity of human innovation. Thus, in this epoch-defining saga, the
fusion of UAVs and AloT devices emerges as an epic, a beacon of hope, and a
testament to the boundless potential of the human spirit. It is, without a shadow of a
doubt, a momentous leap towards a future where technology and agriculture dance in
20 eternal harmony, an indomitable force of nature and machine that reshapes our world
beyond all imagination.
PROPOSED INVENTION GOALS AND ADVANTAGES:
The proposed system combines drone-based surveillance, Deep learning models, and
cloud technology to detect pests and diseases affecting the production of coconuts. The
25 drone equipped with high-resolution cameras, flies over the coconut plantation. It
captures detailed images of each tree, which are then analysed in real time by a powerful
machine learning model specifically trained to identify signs of pests and diseases. The captured images are securely stored in the cloud, allowing for easy access and
management from anywhere. The Deep learning algorithms continuously refine the
system's accuracy, ensuring . the most precise detection result. By analysing the
identified pests or diseases, the system suggests the most appropriate pesticides to target
5 the specific threats. This information is then delivered directly to the farmers through a
user-friendly mobile application. After the mobile application advises the farmers on the
most suitable pesticides, the drone descends from its aerial survey. Equipped with the
recommended pesticide, it prepares to administer the solution precisely onto the affected
sections ofthe coconut trees.
10 KEY ADVANTAGES OF THIS INVENTION INCLUDE:
• Early Detection: By combining drone-based surveillance and machineleaming
models, the system can detect pests and diseases in coconut plantations at an early
stage.
• Accuracy: The high-resolution cameras on the drone capture detailed images of each
15 tree, which are then analysed in real-time by powerful deep learning algorithms. This
ensures accurate identification of pests and diseases, minimizing false positives and
negatives.
• Efficiency: With the use of cloud technology, the captured images are securely stored
and accessible from anywhere. This allows for efficient management and analysis of
20 data, facilitating prompt decision-making by farmers and agricultural experts.
• Tailored Recommendations: By analysing the identified pests or diseases, the system
suggests the most appropriate pesticides to target the specific threats.
• Cost-effectiveness: By enabling early detection and targeted treatment of jJests and
diseases, the system helps farmers minimize crop losses and reduce the need for
25 excessive pesticide use.

5. OBJECT OF THE INVENTION
• The prime object of the invention is to Provide a system and method for efficient and
automated detection and management of pests and diseases in coconut tree farms using
artificial intelligence, image recognition technologies, environmental sensors, and the
5 Internet of Things (loT).
• One more object of the invention is to enhance the accuracy, speed, and cost effectiveness
of pest and disease identification, thereby reducing agricultural manpower
requirements and minimizing the environmental impact of pesticide usage.
• Yet another object of the invention is to empower farmers with real-time information on
10 pest locations and severity, enabling precise and timely pest control measures for
improved coconut production and sustainable farming practices.
• Yet another object of the present invention is to assist farmers in administering
pesticides to the affected regions of the coconut tree using drones, eliminating
the need for manpower involvement
15 6. FIELD OF THE INVENTION:
The invention relates to agricultural technology, specifically the use of A I, image
recognition, and loT for automated pest and disease management in coconut tree
farming.
7. PRIOR ARTS:
20 Traditional methods of pest and disease management in coconut farming rely heavily on manual
inspections, which are time-consuming and prone to human error, particularly given the height of
the trees and the subtlety of early symptoms. Existing technological solutions, while offering
some automation, often lack the precision and real-time data needed for effective early detection.
These systems are typically complex, costly, and not fully optimized for coconut farming. Our
25 invention addresses these shortcomings by integrating AI, deep learning, drones, and loT into a
unified, user-friendly system. This approach enhances the accuracy and efficiency of pest
management, reduces operational costs, and provides farmers with real-time, actionable data for better crop protection.



Patent No:CN109729893A Date of Filing: 15/03/2019

Title: A kind of method that biological control invasion pest coconut knits moth.

Country: China




Abstract:
The invention discloses the methods that a kind of biological control invasion pest coconut knits
moth, and the initial stage pupa that coconut knits moth male worm is irradiated with radiation
5 source Co 60, and irradiation dose-is I00-200Gy, and irradiation time is 1-2 hour: Pupa is
placed in 25-27 DEG C of temperature, under·the conditions of relative humidity 70% ± I 0%,
after pupa turns into adult, adult is transferred in glass tube and is raised with the coconut old leaf
after radiation treatment, obtains sterile treated male worm; Then moth female insect quantity is
knitted according to monitoring inforests coconut, release infertility treated male worm.By the
10 method for the invention, sterile Life of Adult was improved to 8 days, make sterile males there
is the more time to search for female adult, is conducive to male worm and plays efficiency,
furthermore 5 age in days sterile males flying speeds reach 0.62-0. 73m/s, flight time summation
reaches 4345-7859s, flying distance summation reaches 261-550m, effectively improves sterile
adult range of scatter, improves the competitiveness of sterile males and the fertile male worm of
15 woodland, and then enhancing pest controlling effect, Revision insect reduced rate reaches
75-91%.
Comments:The above said invention, construction wise and performance wise is completely
different from our present invention.




Patent No:CN I 046112778 Date of Filing: 15/02/2015


Title:One plant of preventing and treating palm insect coconut knits bacillus thuringiensis and the application of moth.

Country: China

Abstract:
The invention belongs to microorganism field, it is related to one plant of preventing and treating
palm insect coconut to knit bacillus thuringiensis and the application of moth, the Strain
Designation is bacillus thuringiensis (Bacillus thuringiensis) BAT I 0, China typical culture
5 collection center, deposit number are preserved on December 31st,
2014 : CCTCCNO: M2014679.The purposes of bacillus thuringiensis (Bacillus thuringiensis)
bacterial strains of BAT I 0 provided by the present invention is its application in preventing and
treating coconut knits moth, show that the bacterial strain knits moth to coconut and has good
insecticidal action by insecticidal test, coconut can be efficiently controlled and knit moth
10 population quantity, reduce loss, it is one plant and knits the biocontrol bacterial strain with
potential significance on moth in preventing and treating coconut, with insecticidal toxicity is
strong, good disinsection effect the features such as, for the development of China's Palmae
industry provides help.
Comments:The above said invention, construction wise and performance wise is completely
15 different from our present invention.



Patent No:PH22014000777YI Date of Filing:OJ/07/20 14


Title:A composition for controlling coconut scale insects (csi) in coconut trees

Country:Philippines

Abstract:
The composition comprises of applying mixture of mineral solvent with different volatilities,
methyl esters, soya oil and powerful wetting, dispersing and emulsifYing surfactant. The method
of application is using power sprayer, with pressure adjusted just to wet the target part of the
20 coconut tree. The solvent can melt the protective scale and give off neurotoxic properties. The
dried soya has film-forming properties which suffocate the surviving eggs, nymph, and adults
CSI.
Comments: The above said invention, focuses on a chemical approach to pest control using specific mixtures and manual application, whereas our project emphasizes automated,
technology-driven pest detection and targeted pesticides treatment using AI, drones, and loT.


Patent No:CN218043357U Date of Filing: 13/04/2022



Title:Coconut protection against insects device.


Country: China


Abstract:
The utility model discloses a coconut insect prevention device, which relates to the technical
5 field of coconut insect prevention and comprises an outer cylinder, a trap lamp arranged in the
outer cylinder, an inner cylinder covering the surface of the trap lamp and an insect killing
mechanism arranged between the outer cylinder and the inner cylinder, wherein the bottom of the
outer cylinder is in threaded connection with a collection box, and the upper surface of the inner
cylinder is provided with a plurality of insect inlets; the insecticidal mechanism comprises a
10 plurality of fixed electric discharge groups arranged on the inner wall of the outer cylinder and a
plurality of rotary electric discharge groups arranged on the outer wall of the inner cylinder. The
utility model discloses in, arrange group, rotary type electricity through the outer fixed electricity
of moth-killing lamp and arrange group and cyclic annular electric wire netting and kill the
coconut and organize the moth to arrange the crisscross removal of group with the rotary type
15 electricity through fixed electricity to, thereby scrape the corpse of organizing the moth with the
coconut and fall to collecting the box, avoid killing the coconut that the back organizes the moth
and glue on insecticidal mechanism surface.
Comments: The above said invention, construction wise and performance ·wise is completely
different from our present invention.



Patent No:CN219920091U Date of Filing:OS/06/2023


Title:Coconut insect expelling device
Country:China





Abstract:
The utility model relates to the technical field of insect repellents, in particular to a coconut insect repellent. The method mainly aims at the problems that coconut pests are rampant, coconut
leaf beetles and red palm weevils are main coconut pests, serious threat is caused to the growth
of coconuts, and the large-area death of the coconuts is caused, and the following technical
scheme is provided: including volatilizing the box, volatilize one side of box and install multiunit
5 fixed block, fixedly connected with multiunit fixed band on the fixed block, volatilize and seted
up in the box and deposit the chamber, volatilize and still seted up the liquid level chamber in the
box, the liquid level chamber communicates with depositing the chamber, the top in liquid level
chamber is provided with annotates the liquid mouth, annotate and install sealed lid on the liquid
mouth, deposit to be provided with the dwang in the chamber. The utility model can be fixed on
10 the trunk, the essential oil in the device automatically volatilises to expel insects, and meanwhile,
the volatilization of the essential oil is accelerated when wind exists in the environment, so that a
better insect expelling effect is obtained, and the practicability is strong.
Comments: The above said invention, sorts the peanut-pods and removes the excess dust, but
our invention sorts and grades the peanut using computer vision.




8. LIST OF FIGURES
Figure lA depicts the top view of the drone with the wings
Figure lB shows the camera view which records the coconut leaves for pests and disease
detection
Figure lC shows the back view of the drone with the pesticide container which will be
used for storing the pesticides.
Figure 2 shows the splash screen with the logo of the developed mobile application
"Thenkappan".
Figure 3 depicts the home screen of the mobile application with various features like
temperature updates, trending infections, etc.
Figure 4A shows the crop protection page with details of coconut diseases, pests, and ·
nutrition deficiency.
Figure 4B shows the coconut diseases page with detailed information about coconut tree
diseases.
Figure 4C depicts the coconut pests' page with detailed information about coconut tree
pests.
Figure 4D shows detailed information about the nutritional deficiency that affects the
coconut tree.
Figure 5 shows the drone view page where the identified pests and diseases are shown
lively with their name.



9.SUMMARY OF THE INVENTION
This invention introduces an AToT-based system that uses drone technology to detect and
manage pest infestations and diseases in coconut trees. The drone is equipped with a highresolution
camera (101), positioned centrally to capture detailed images of the coconut trees
5 as it flies around them. The camera lens (106) is designed to provide clear and sharp images,
essential for identifying early signs of infestation or disease. The drone itself is supported by
a sturdy wing stand (102) and wing holder (103), ensuring balance and stability during
flight. The wings (104) provide the necessary lift, allowing the drone to hover around the
trees and capture images from multiple angles and heights. Once the drone is in position, it
10 begins capturing real-time images of the coconut trees. These images are sent to an AI
processing unit, which analyzes them to detect pests like rhinoceros beetles or diseases such
as Black Stem Rot. The pesticide container (105) attached to the drone holds the pesticide
recommended by the system based on the analysis. When a pest or disease is identified, the
drone autonomously dispenses the pesticide through the spray hole (107), targeting only the
15 affected areas. This selective application reduces the amount of pesticide used, promoting an
environmentally friendly approach to pest control. The AI used in this system is highly
C. sophisticated and trained to recognize a wide range of pests and diseases specific to coconut
N
E._ trees. As the drone collects images, the AI module processes them instantly, making real-
0
-LL. time recommendations for treatment. The dynamic pesticide recommendation engine takes N
~ 20 into account various factors such as the type of infestation, the severity of the disease, and
en :g even environmental conditions. The application of pesticides through the drone's spray
~ · mechanism is precise, ensuring that healthy parts of the tree are left untouched. This
0 contributes to a significant reduction in pesticide waste and minimizes the harmful effects of
~ chemicals on the surrounding ecosystem. Farmers can monitor the entire process through a
~ 25 mobile application (501), which serves as the main interface for managing the system. The
application includes a simple, user-friendly interface thal provides a comprehensive overview of the health of the coconut trees. Features such as a menu {502) for navigation,
real-time weather information {503), and options to initiate drone scans {504) are all
accessible through the app. The app also displays trending infections {505) like Black Stem
Rot, helping farmers stay informed about current threats to their crops. The easy scan mode
5 {506) allows for quick, mobile-based scans of the trees, offering an additional layer of
monitoring without the need for drone deployment. In addition to monitoring infections, the
mobile app offers a "Drone View" mode {701), where farmers can see live footage of the
drone's scan. As the drone flies around the trees, the app displays any detected infestations
with a visual marker {702), allowing farmers to see exactly where pests or diseases are
10 located. This real-time visualization is crucial for timely intervention, helping farmers
address issues before they escalate into larger infestations. By combining drone technology
with real-time mobile feedback, the system empowers farmers to take proactive steps in
managing their crops effectively. This invention brings several benefits to coconut farmers
by integrating advanced AI and drone technology into traditional farming practices. The
15 autonomous nature of the drone reduces the need for manual labor, while the Al-powered
pest detection system ensures that issues are identified and treated before they can cause
significant damage. The precision with which the drone applies pesticides helps to protect
the environment by minimizing chemical usage and preventing contamination of
surrounding areas. By using this system, farmers can reduce crop losses, lower their
20 pesticide costs, and contribute to more sustainable farming practices. ThisAioT-based
coconut tree pest detection and management system offers a highly effective and innovative
solution to the challenges faced by farmers. The integration of drones, AI, and real-time
mobile monitoring creates a seamless process for detecting, diagnosing, and treating pests
and diseases. By targeting only ·the affected areas, the system reduces. waste and
25 environmental harm, making it a valuable tool for sustainable agriculture. Through early
detection and timely intervention, this system helps ensure healthier crops and higher yields, ultimately contributing to the long-term success of coconut farms.


THE FEATURES OF THE PRESENT INVENTION ARE THE FOLLOWING
"AioT-Based Pest Detection for Coconut Trees"
Description: This invention utilizes AloT technology with drone-mounted cameras and lenses to
5 capture high-quality images of coconut trees. The AI processes these images to detect early signs of
pest infestations and diseases, ensuring proactive intervention.
"Autonomous Pesticide Spraying Mechanism"
Description: The system includes a drone equipped with a pesticide container and spray hole. Upon
detecting pests or diseases, the drone automatically applies pesticides directly to the affected areas,
10 reducing chemical waste and protecting healthy crops.
"Dynamic Pesticide Recommendation Engine"
Description: The AI-powered engine analyzes the captured data and provides real-time, dynamic
pesticide recommendations. It considers factors like pest type, disease severity, and environmental
conditions to optimize treatment.
15 "Mobile Application for Real-Time Monitoring and Control"
Description: A mobile application allows farmers to monitor the health of their crops in real-time.
The app provides options for initiating drone scans, tracking current infections, and accessing live
drone footage through a simple interface.
"Real-Time Drone-Based Disease Detection System"
20 Description: With the aid of a stable wing stand, wing holder, and wings, the drone hovers near the
trees and captures images of pests or diseases in real time. The system ensures rapid identification
and treatment, preventing infestations tram spreading.
"Environmentally Friendly Pest Control Solution"
Description: The precision pesticide application method minimizes pesticide usage by targeting only
affected areas, making the system more sustainable and environmentally friendly. The drone-based
spraying mechanism ensures that only necessary amounts of chemicals are used.
5 "AI-Driven Coconut Farming Sustainability System"
Description: By integrating drone technology, image processing, and AI analysis, this invention
supports coconut farmers in achieving higher yields and reducing pest-related crop losses. The system
reduces manual labor while promoting eco-friendly farming practices.
10 DETAILED DESCRIPTION OF THE INVENTION SYSTEM
The invention you're working on tackles the significant challenges coconut farmers face in detecting
and managing pests and diseases. Traditionally, these tasks have relied on manual observation, which
is both time-consuming and prone to error, particularly because the early signs of infestations are
Ill often too minute to be detected by the naked eye. This method also demands considerable labor,
· Cll 15 leading to high costs, especially given the height of coconut trees and the difficulty in thoroughly inspecting them. To address these issues, the invention integrates advanced technologies, including
artificial intelligence (AI), image recognition, environmental sensors, drones, and the Internet of
Things (loT), to automate and optimize the detection and management of pests in coconut tree farms. N
~ At the core of the system are UAYs, or drones, equipped with high-resolution cameras and
environmental sensors that autonomously survey coconut tree farms. These drones capture detailed
images of the trees from various angles, overcoming the challenges posed by the height and spread of
the trees. The ability to cover large areas quickly ensures a more comprehensive inspection than
manual methods, making the process not only faster but also more accurate. This autonomous
inspection is crucial in identifying pest infestations early, allowing for timely intervention before the --::1' 25 pests can cause significant damage. Once the drones capture the images, the data is uploaded to cloud storage, where it is processed using deep learning algorithms designed for image analysis. These
algorithms have been trained on extensive datasets of pest and disease images, enabling the system to
accurately identify even the early .stages of infestations. The deep learning model's precision
significantly reduces the likelihood of human error, ensuring that even the smallest signs of trouble
5 are detected. After identifying the pests or diseases, the system cross-references the fmdings with a
database to determine the type of pest or disease and the most effective pesticide for treatment. The
system then generates a detailed report, which includes information on the location and severity of
the infestation, the type of pest or disease, and a recommendation for the appropriate pesticide. This
report is instantly sent to the farmer's smartphone via Wi-Fi, providing real-time insights that allow
10 for immediate action. This rapid communication is crucial in preventing the spread of pests and
minimizing damage to the crops. The use of Wi-Fi ensures that data transfer is both quick and
reliable, facilitating efficient decision-making. One of the most innovative features of this invention
is its ability to autonomously administer pesticides. After receiving the pest report, the farmer can
load the recommended pesticide onto the drone, which then flies to the afT~cl~d areas and applies the
15 pesticide precisely where it's needed. This targeted application minimizes the use of pesticides,
reducing costs and environmental impact while ~ensuriug that the treatment is both effective and
efficient. The ability to automate this process further reduces the need for manual labor, particularly
in large or hard-to-reach areas. In conclusion, this invention represents a significant advancement in
agricultural technology, particularly in the management of coconut tree pests and diseases. By
20 integrating AI, drones, deep learning, and loT, the system provides a comprehensive and automated
solution that enhances the accuracy, efficiency, and sustainability of pest management. This
innovation not only helps farmers protect their crops and improve yields but also promotes
environmentally friendly farming practices by reducing the overuse of pesticides. The result is a more
sustainable, cost-effective approach to coconut farming that addresses many of the challenges faced
25 by today's farmers


ONE OF THE PREFERRED EMBODIMENTS OF THE PRESENT INVENTION
COMPRISES THE FOLLOWING
I 01 -Camera
I 02- Wing Stand
5 · 103- Wing Holder
104- Wing
105- Pesticide Container
106 - Camera Lens
107- Spray Hole
Certain modifications and improvements will occur to those skilled in the art upon a reading of the
foregoing description. The above-mentioned details are provided to serve the purpose of clarifying
!:::.. aspects of the invention and it will be apparent to one skilled in the art that they do not serve to limit
0 the scope of the invention.
~ All modifications and improvements have been deleted herein for the sake of conciseness and ·
~· 15 readability but are properly within the scope of the present invention. It is understood that the
0 foregoing detailed description is given merely by way of illustration and that modification and
variations may be made therein without departing from the spirit and scope of the invention.



10, CLAIMS
We claim,
I. A system, of loT based detection of coconut tree pests and diseases with pesticide
recommendations and drone remediation: comprising;
a) Camera( I 0 I)
b) Wing Stand( I 02)
c) Wing Holder(l03)
d) Wing(I04)
e) Pesticide Container( I 05)
f) Camera Lens( I 06)
g) Spray Hole(l07)
2. The system claimed in 1, where in the claimed camera (101), is a high-resolution
camera (101), positioned centrally to capture detailed images of the coconut trees as it
flies around them.
3. The system claimed in 2, where in the claimed camera (101) has camera lens (106) is
designed to provide clear and sharp images, essential for identifying early signs of.
infestation or disease .
4. The system claimed in 1, where in the claimed wing stand (102) and wing holder (103),
ensuring balance and stability during flight. Where in the wings (104) provide the
necessary lift, allowing the drone to hover around the trees and capture images from
multiple angles and heights.
5. The system claimed in 1, where in the claimed wing stand (102) is in position, it begins
capturing real-time images of the coconut trees and these images are sent to an AI
processing unit, which analyzes them to detect pests like rhinoceros beetles or diseases
such as Black Stem Rot.

G. The system claimed in l, where in the claimed the pesticide container (lOS) attached
to the drone holds the pesticide recommended by the system based on the analysis.
When a pest or disease is identified, the drone autonomously dispenses the pesticide
through the spray hole (107), targeting only the affected areas and this selective
application reduces the amount of pesticide used, promoting an environmentally
friendly approach to pest control.
7. The system claimed in l, where in the system features a menu (502) for navigation,
real-time weather information (503), and options to initiate drone scans (504) are all
accessible through the app. The app also displays trending infections (505) like Black
Stem Rot, helping farmers stay informed about current threats to their crops and easy
scan mode (506) allows for quick, mobile-i;>ased scans of the trees, offering an
additional layer of monitoring without the need for drone deployment.
8. The system claimed in l, where in the system features monitoring infections, the mobile
app offers a "Drone View" mode (701), where farmers can see live footage of the
drone's scan. As the drone flies around the trees, the app displays any detected
infestations with a visual marker (702), allowing farmers to see exactly where pests or
diseases are located.

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