AI BASED PEST DETECTION AND ALERT SYSTEM FOR FARMERS USING IOT
Objective
Develop an AI-based system to detect and classify agricultural pests using acoustic and infrared sensors, utilizing neural networks for real-time monitoring, and sending alerts to farmers via IoT and Wi-Fi.
Abstract
Agriculture plays a major role in the economy and is the cornerstone of the economic system of emerging nations. India is a prominent participant in the global agricultural arena. Even while agriculture makes extensive use of cutting-edge technologies, there is now no adequate solution to handle pest-related problems. People's resistance to using pesticides to control agricultural pests is a global problem. To address this particular problem, an AI-based pest detection model is developed. This model uses an artificial neural network for categorization in an effort to better illustrate the effectiveness of acoustic approaches in insect identification. Numerous research have demonstrated the viability of using acoustic technologies for insect monitoring and detection, using a variety of sound parameterization and classification methods. Both acoustic and infrared sensors are used to detect the presence of insects. An AI model is used to evaluate and categorise the aural input using deep learning techniques in order to determine the type of pest. This device uses an IoT and Wi-Fi module to identify the pest and sends a mobile phone notification to the farmers.
Keywords–Pest Detection, AI, Artificial Neural Networks, Wi-Fi module, IoT.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software: Matlab 2020a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is Signal Processing?
· About Signal Processing
· Introduction to Signal Processing
· How analog and digital signal is formed
· Importing the signal via signal acquisition tools
· Analyzing and manipulation of signals.
· Phases of signal processing:
· Acquisition
· Signal enhancement
· Signal restoration
· Medical Signal Processing
· Medical Signal Analysis
· Medical Signal Diagnosis
· Filtering techniques
· Machine Learning Algorithms
· Deep Learning Algorithms etc.
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
