Embedded-based Pest Detection And Prevention System For Smart Agriculture

Abstract

Agricultural Productivity Is Significantly Affected By Pest Infestations, Leading To Substantial Crop Losses And Increased Dependence On Chemical Pesticides. Early Detection And Timely Prevention Of Pests Are Essential For Improving Crop Yield And Ensuring Sustainable Farming Practices. This Paper Presents An Embedded-based Pest Detection And Prevention System That Integrates Image Sensing, Environmental Monitoring, And Automated Control Mechanisms For Real-time Crop Protection. The Proposed System Employs A Camera Module And Embedded Processor To Detect Pest Presence Using Image Processing And Machine Learning Techniques, While Sensors Continuously Monitor Environmental Parameters Such As Temperature And Humidity. Upon Detecting Pests, The System Automatically Activates Appropriate Preventive Measures, Including Ultrasonic Repellents Or Pesticide Spraying, And Simultaneously Notifies Farmers Through A Wireless Communication Module. Experimental Evaluation Demonstrates That The Proposed System Provides Accurate Pest Detection, Minimizes Pesticide Usage, Reduces Manual Intervention, And Enhances Crop Productivity. The Developed Framework Offers A Cost-effective, Intelligent, And Scalable Solution For Precision Agriculture And Smart Farming Applications.

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