Index -1714550800 Index

Index
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Index …………………………………………………………………………………………………………………………IList of Figures …………………………………………………………………………………………………………..IIIList of Tables ……………………………………………………………………………………………………………IVAcknowledgements …………………………………………………………………………………………………….VAbstract ……………………………………………………………………………………………………………………VI1 Introduction …………………………………………………………………………………………………………11.1Problem Summary ………………………………………………………………………………………….11.2Aim and objective ………………………………………………………………………………………….11.3Problem Specifications ……………………………………………………………………………………21.4Prior Art Search ……………………………………………………………………………………………..21.5Plan of work ………………………………………………………………………………………………….61.6Materials / Tools required ……………………………………………………………………………….72 Design Analysis, Methodology and Implementation. ………………………………………………..82.1Observation Matrix (A E I O U summary sheet) ………………………………………………..82.1.1Activities: …………………………………………………………………………………………………………92.1.2Environment: …………………………………………………………………………………………………….92.1.3Interaction: ……………………………………………………………………………………………………….92.1.4Objects: ……………………………………………………………………………………………………………92.1.5Users: ………………………………………………………………………………………………………………92.2Empathy Mapping Canvas …………………………………………………………………………….102.2.1Users: …………………………………………………………………………………………………………….112.2.2Stakeholders: …………………………………………………………………………………………………..112.2.3Activities ………………………………………………………………………………………………………..112.2.4Sad and happy stories ……………………………………………………………………………………….112.3Ideation Canvas ……………………………………………………………………………………………122.3.1People …………………………………………………………………………………………………………….13I
2.3.2Activities ………………………………………………………………………………………………………..132.3.3Situation/ Context/ Location ……………………………………………………………………………..132.3.4Props/ Tools/ Equipment …………………………………………………………………………………..132.4Product Development Canvas ………………………………………………………………………..142.4.1Purpose …………………………………………………………………………………………………………..142.4.2People …………………………………………………………………………………………………………….142.4.3Experience ………………………………………………………………………………………………………152.4.4Functions ………………………………………………………………………………………………………..152.4.5Features ………………………………………………………………………………………………………….152.4.6Components ……………………………………………………………………………………………………152.4.7Revalidation ……………………………………………………………………………………………………152.4.8Redesign …………………………………………………………………………………………………………153Implementation …………………………………………………………………………………………………..164Summary ……………………………………………………………………………………………………………204.1Advantages of the solution …………………………………………………………………………….204.2Future Scope ………………………………………………………………………………………………..204.3Unique features of innovation/ project …………………………………………………………….21References ………………………………………………………………………………………………………………..22Appendix ………………………………………………………………………………………………………………….23II
List of Figures
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Figure 1. Plan Of Work6Figure 2. AEIOU Summary Sheet8Figure 3. Empathy Mapping Canvas10Figure 4. Ideation Canvas12Figure 5. Product Development Canvas14Figure 6. Sem 7 Implementation16Figure 7. Circuit Diagram for Sound Capturing17Figure 8. Simplified Circuit Structure18Figure 9. Implemented Circuit18Figure 10. Implemented Circuit 219III
List of Tables
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Table 1. Prior art search result in patent databases2Table 2. Important prior art details3Table 3. Closest Prior art5Table 4. Comparative Novelty Analysis21IV
Acknowledgements
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The success and final outcome of this project required a lot of guidance and assistance from many people and We are extremely privileged to have got this all along the completion of our project.

Firstly we’d like to thank god and our parents who always motivate us to achieve our desired goal in life.

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We owe our deep gratitude to our project guide Dr. Vivaksha Jariwala and our external guide Dr. Devesh Jinwala who took keen interest on our project work and guided us all along, till the completion of our project work by providing all the necessary information for developing a good system.

We are thankful to and fortunate enough to get constant encouragement, support and guidance from all Teaching staffs of the IT department, SCET which helped us in successfully completing our project work. Also, We would like to extend our sincere esteems to all staff in laboratory for their timely support.

Last but not the least, Thanks to Sarvajanik College of Engineering and Technology for giving us the platform for representing this project
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Abstract
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We propose the design of an integrated pest management system using a bio-acoustic sensor that can be installed in crops under study and is able to analyze the captured audio signal during large periods of time. An acoustic sensor is an insect pest detection sensor which works by monitoring the noise level of the insect pests. Wireless sensor nodes connected to a base station are placed in the field. When the noise level of the pest crosses the threshold, a sensor transmits that information to an application, which then accurately indicates the infestation area. It is programmed to send warning messages.These sensors help detect an infestation at a very early stage, thus greatly reducing crop damage.

VI
1Introduction
1.1Problem Summary
Starting with the early crop stages, a farmer must closely monitor crops because of various crop insect pests and diseases.

Excessive use of pesticides on crops increases risk not only to human health but also to environment. Farmers do not have information about precise application of pesticides and it causes harmful effect on other organisms including human.

For this reason, improved sensors for precision farming are constantly being improved. Such modern technology includes pest detection sensors which detect disease and insect pest occurrence on crops providing real-time data from the field 1.

1.2Aim and objective
In this work we propose the design of an integrated pest management tool using a bio-acoustic sensor that can be installed in crops under study and is able to analyse the captured audio signal during large periods of time. An acoustic sensor is an insect pest detection sensor which works by monitoring the noise level of the insect pests. Wireless sensor nodes connected to a base station are placed in the field. When the noise level of the pest crosses the threshold, a sensor transmits that information to the control room computer, which then accurately indicates the infestation area. It is programmed to send warning messages, thereby allowing supervisors to check online the status and evolution of crops. These sensors help detect an infestation at a very early stage, thus greatly reducing crop damage. It helps in the monitoring of large field areas with very low energy consumption.

1
1.3Problem Specifications
We design and develop a system prototype which efficiently detects the sounds produced by pests after first infestation stage.

The proposed bioacoustic sensor is able to:
Effectively detect the pests presence with high detection rates (over 90%)
Performs monitoring tests at user programmable frequencies in order to achieve a fast detection response.

Form a wireless sensor network to cover from little orchards to large plantation extensions.

Trigger an alarm system defined at the control station to warn supervisors about the desired events by means of an application.

Work without maintenance requirements after installation.

1.4Prior Art Search
Table 1. Prior art search result in patent databases
Sr no Query Google patent EspaceNet PatentScope
1. pest detection and 240 204 3160
control 2. pest detection using 6090 71 4540
acoustic sensor 3. pest control systems 150 247 3160
4. Integrated pest 75,172 10 2,750
Management in crops 2
Table 2. Important prior art details
Sr no. Title Application No. Priority date Pest control and detection system with conductive bait matrix 1. 8 US201615523312 2015-07-13 Pest intellectual detection system 2. device and granary CN201720941188U 2017-07-31 Systems and methods for dispensing an insecticide via unmanned vehicles to defend a crop- containing area against 3. pests 2 WO2017US49531 2016-09-08 Methods for positioning a residential pest detector and a system for detecting 4. residential pests US201514835872 2015-06-15 Insect Image Recognition and Instant Active 5. Response 6 US201213542416 2011-07-05 Pest control system, pest control method and pest control 6. program 5 WO2013069059A1 2011-11-09 Method, apparatus for 7. laser pest control 9 US08300089 1992-06-12 3
Method for pest management using pest identification sensors and network accessible 8. database 12 US20030069697A1 2001-02-05 Pest trap plants and 9. crop protection 4 US5640804A 1994-09-14 Method and system for detecting residential 10. pests 10 US14739041 2015-06-15 Acoustic chamber for 11. detection of insects 7 11/822384 2007-07-05 Termite acoustic 12. detection 11 10/680377 2002-10-09 Grain insect detecting 13. device CN 03245960 2003-06-12 Remote detection, monitoring and information management system 14. 13 US20020062205A1 2000-08-22 Control of pests and animal parasites through direct neuronal 15. uptake 1 US20030181376A1 2000-08-30 4
Table 3. Closest Prior art
Sr. Application No. Summary of Invention Similarity Novelty point The present invention has been made to solve such problems, a pest control program which can destroy A GSM module pests from agriculture with can be used to no harmful effects to other send signals useful organisms, crops and along with humans as no pesticides are description of 1. WO2013069059A1 used. ;70% infested area A automatic pest detection system can be configured with The systems and methods the aerial described herein provide for vehicle so that automated monitoring of vehicle can WO2017US4953120 crop-containing areas via trigger itself 2. 170831 unmanned vehicles. ;70% immediately A detection system is for detecting a residential pest. An acoustic which includes an alert sensor can be generator that may be used to detect configured to generate a noise so that US20151483587220 pest alert and to transmit large area can 3. 150826 over a wireless network. ;70% be covered 5
The method includes gathering pest data in connection with a crop of the grower. It includes pest identification information gathered using a Sensor. It further includes locational information. It further includes transmitting the data to a pest sampling Wifi module
4. 10/294,418 database. ;70% can be added
1.5Plan of work
723265255270
Figure 1. Plan Of Work
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Design and simulate circuit: In this stage we design and simulate a sound detection circuit that capture the noise generated by pest and convert it into digital form and given as a input to microcontroller using Proteus 8 Professional software.

Program the microcontroller: In this stage we insert various noises of pests in the microcontroller. We program it in such a way that the captured noise by noise detection circuit matches any noise that is stored in it. The pest name is displayed as a output.

Connect Circuit with application using wifi module: Here we will make an android application that display the result using wifi module and can alert the farmers about the infestation.

Input Training dataset: Here we input some training datasets into microcontroller.

Test and Debug Model: By detecting various noise of pests we test our model and check for any future errors.

Validate Model: Product validation after successful testing and results
1.6Materials / Tools required
Hardware of Bioacoustic Sensor consists of following:
An audio probe
A low-power processor
A wireless communication interface
A power supply unit based on Solar cell
Software Requirements
Android Studio
Arduino Programming Software
Proteus 8 Professional
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2Design Analysis, Methodology and Implementation.

2.1Observation Matrix (A E I O U summary sheet)
132715254635
Figure 2. AEIOU Summary Sheet
The following figure shows the AEIOU summary which consists of the following components
A: Activities
E: Environment
I: Interactions
O: Objects
U: Users
This canvas is totally based on the observations of surrounding environment. After observing some places like farms, storage houses and platations we started working to make it fair.

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2.1.1Activities:
Ploughing
Planting and harvesting crops
Monitoring
Applying fertilizers
Irrigation
2.1.2Environment:
Breezy
Muddy farms
Sunny
Quiet environment
2.1.3Interaction:
Winding the drip tube manually
Interacting with instruments
Spraying fertilizers, pesticides through pumps
Interacting with government agencies for farming knowledge
2.1.4Objects:
Tractor
Fertilizers/ pesticides
Sprayers
Plows
Water pump
2.1.5Users:
Farmers
Pesticides and fertilizers salesmen
Crop consumers
Crop processing industries
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2.2Empathy Mapping Canvas
408940254635
Figure 3. Empathy Mapping Canvas
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Figure 3 shows the empathy mapping canvas. This canvas involves knowing the problems of people around us along with researching about the stakeholders and related users to the main client. Hence, this canvas helps us in empathizing with users and their work.

Components of empathy mapping canvas:
2.2.1Users:
Farmer
2.2.2Stakeholders:
Family members
Salesmen
Industries
2.2.3Activities
Ploughing
Spraying fertilizers
Planting and harvesting crops
Storing crops
2.2.4Sad and happy stories
This part showcases the different case studies of users and their reactions using the product and not using the product.

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2.3Ideation Canvas
55880254635
Figure 4. Ideation Canvas
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The above figure emphasises about the people for whom we want to solve the problem. For our product we listed the people who can generally use our product, the thought for context/location /situation and finally for possible solutions.

Components for Ideation canvas:
2.3.1People
Farmer
Retailers
Landlord
Salesmen
Consumers
2.3.2Activities
Ploughing
Spraying fertilizers
Planting and harvesting crops
Storing crops
2.3.3Situation/ Context/ Location
Sunny
Large amount of pest infestation
Crop damage
Draught
Loss of resources
Farms
2.3.4Props/ Tools/ Equipment
Sprayers
Pumps
Pesticides/ fertilizers
Automatic threshers
Containers
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2.4Product Development Canvas
6985254635
Figure 5. Product Development Canvas
Figure 5 shows the product development canvas where we mentioned about our product, its function, features, revalidation, and product experience.

Components of Product Development canvas
2.4.1Purpose
Prevent loss of resources
Help farmers monitor their farms
Reduce consumption of pesticides
2.4.2People
Farmer
Crop consumers
Retailers
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2.4.3Experience
No need of continuous monitoring
Easy to use
Reliable
2.4.4Functions
Pest detection
Alert message to farmers
Can detect all sound of pest activities
2.4.5Features
Additional preventive measures
Provides detailed information of pests
Location of infested area
2.4.6Components
Internet connectivity
Analog to digital converter
Microphone
Wi-fi module
2.4.7Revalidation
Limited range
Connot detect noise level below min threshold level
2.4.8Redesign
Range can be extended
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3Implementation
1618615864870
Figure 6. Sem 7 Implementation
After preparing all canvases and knowing the aspects of user problems and solutions, we moved on to the implementation of the project.

Initially we started with the hardware module of the project.

In this sem we started with the first 2 parts of our project i.e designing and simulating the circuit and programming the microcontroller.

The following figure illustrates the initial circuit diagram for the project.

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914400914400
Figure 7. Circuit Diagram for Sound Capturing
The following circuit consists of the following components
High Pass Filter
Amplifier
Voltage Rectifier
The microphone used to capture the noise is connected to the 4th order High Pass Filter which filters out the frequencies above the threshold frequency.

Then the output is passed to the amplifier to amplify the sound so that it can used for future computation.

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The output obtained from the amplifier is made to pass through the rectifier and finally through the ADC circuit to match it with the stored dataset.

0138430
Figure 8. Simplified Circuit Structure
System Specifications
LM741 Operational Amplifier
ATMEGA328 28 pin microprocessor
Capacitors, resistors
589915478155
Figure 9. Implemented Circuit
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1374775918210
Figure 10. Implemented Circuit 2
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4Summary
4.1Advantages of the solution
High quality standards have largely been imposed by government agencies on producers, processors, packers and retailers in response to consumer concerns. It will be the need to maintain consumer confidence in the food industry that will continue to drive other stakeholders to invest in ‘safe’ technologies.

Adoption of Integrated Pest Management strategies will benefit economically due to sustained development, increased productivity and reduced pest damage.

Some of the benefits of an integrated approach are as follows:
Protects the non-target species through reduced impact of pest management activities.

Reduces the need for pesticides by using several pest management methods
Reduces or eliminates issues related to pesticide residue.

Decreases worker, tenant and public exposure to pesticides.

Maintains or increases the cost-effectiveness of pest management programs
4.2Future Scope
We need to create awareness about integrated pest management among farmers so that, they can take more advantage out of our work.

If government finds our project a good approach then they can provide farmers to try the product for free for specific time and if farmers feel that it is fruitful then they can apply for paid version though it is not that much costly.

Integrated Pest Management is a holistic approach to sustainable crop protection that focuses on managing pest insects through use of acoustical methods that are cost effective, environmentally sound and socially acceptable. Integrated Pest Management is a system of pest monitoring, and intervention when necessary; utilising a range of pest management tools
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to provide farmers a means to minimise crop losses to pests and disease, and sustainably maximise production.

4.3Unique features of innovation/ project
Table 4. Comparative Novelty Analysis
S.N. Prior art Features Novelty features
1. Easy to use but needs continuous Reliable and cost effective in long term use
maintenance 2. Automatic signal trigger using gsm Automatic signal trigger using wifi module
module 3. Pest detection using sensor Pest detection using acoustic sensor
4. Agricultural applicability medium Agricultural applicability high
5. Efficient use of hardware Efficient use of software with hardware
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References
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http://blog.agrivi.com/post/farm-revolution-sensors-for-crop-pest-detection
Cantrell, R.L., Thompson, J.P., Winkle, D.C., Atchley, M.D., High, D.R., Mattingly, T.D., Mchale, B.G., O’brien, J.J. And Simon, J.F., Wal-Mart Stores, Inc., 2018. Systems And Methods For Dispensing An Insecticide Via Unmanned Vehicles To Defend A Crop-Containing Area Against Pests. U.S. Patent Application 15/697,860.

Cink, J.H., Storey, G.K., Jordan, K.K., Austin, J.W., Evanhoe, C.E., Ivy, B., Engels, D.W., Freeman, D., Lauffenburger, K., Dills, T. and Brown, K.S., 2018. Pest control and detection system with conductive bait matrix. U.S. Patent Application 15/523,312.

Driver, J. and Dandekar, A.M., Treetech Management Inc, 1997. Pest trap plants and crop protection. U.S. Patent 5,640,804.

Feugier, F.G., 2014. Pest control system, pest control method and pest control program. U.S. Patent Application 14/357,449.

Fryshman, B., 2014. Insect image recognition and instant active response. U.S. Patent 8,855,374.

Hawwa, M.A. and Al-Sulaiman, F.A., King Fahd University of Petroleum, 2009. Acoustic chamber for detection of insects. U.S. Patent 7,597,003.

Atkinson, H.J., McPherson, M.J. and Winter, M., 2002. Control of crop pests and animal parasites through direct neuronal uptake patent. Application publication number WO2001EP10004 20010828. International publication number WO, 2(17948), p.A2.

Johnson, W.D., Johnson W Dudley, 1994. Method and apparatus for laser pest control. U.S. Patent 5,343,652.

Korakin, Y., Geva, N. and Shriki, O., WALL SENSOR Ltd., 2017. Method and system for detecting residential pests. U.S. Patent 9,606,226.

Lee, P., University of Mississippi, 2007. Termite acoustic detection. U.S. Patent 7,271,706.

Mafra-Neto, A. and Coler, R.R., ISCA Tech Inc, 2004. Method for pest management using pest identification sensors and network accessible database. U.S. Patent 6,766,251
Roberts, J.R., Eye on Solutions LLC, 2004. Remote detection, monitoring and information management system. U.S. Patent 6,792,395.

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Appendix
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Design Engineering canvases
Periodic Progress Report (PPR)
Patent Search and Analysis Report (PSAR)
Novelty Search Report
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1047115914400
AEIOU Summary Sheet
242570156845
Product Development Canvas
24
970915914400
Empathy Mapping Canvas
25
914400914400
Ideation Canvas
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