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A Precise and Autonomous System for the Detection of Insect Emergence Patterns
Published on: January 9, 2019
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IoT based intelligent pest management system for precision agriculture
Salman Ahmed1, Safdar Nawaz Khan Marwat2,3, Ghassen Ben Brahim4
1Faculty of Computer Science and Engineering, GIK Institute, Swabi, 23640, Pakistan.
Scientific Reports
|December 31, 2024
Summary
This study introduces an IoT-based pest detection system using a smart insect trap and a convolutional neural network (CNN) to combat food insecurity. The technology accurately identifies pests, aiding farmers in precision agriculture and integrated pest management.
Area of Science:
- Agricultural Technology
- Computer Science
- Food Security
Background:
- Traditional agricultural methods face challenges like human error and labor shortages in pest detection, threatening food security, especially in developing nations.
- Precision Agriculture (PA) offers technology-driven solutions to enhance crop protection and productivity.
- Existing methods for pest identification are often inefficient and prone to inaccuracies.
Purpose of the Study:
- To propose and evaluate a smart Internet of Things (IoT)-based pest detection platform for integrated pest management.
- To develop a system that assists farmers by monitoring crop field conditions and detecting pests accurately.
- To address food insecurity risks through technological advancements in agriculture.
Main Methods:
- Development of a physical prototype: a smart insect trap with embedded computing for pest detection and classification.
- Creation of a dataset of 1000+ images of oriental fruit flies under varying illumination conditions in guava orchards.
- Training a convolutional neural network (CNN) classifier using Haralick features, Histogram of Oriented Gradients (HOG), Hu moments, and Color histogram.
Main Results:
- The proposed system achieved a recall of 86.2% and a Mean Average Precision (mAP) of 97.3% on real test images.
- Comparative analysis showed the proposed model outperformed numerous other machine learning (ML) and deep learning (DL) models.
- The model demonstrated superior performance with accuracy (97.5%), precision (92.82%), recall (98.92%), F1-score (95.00%), specificity (95.90%), and low False Negative Rate (FNR) (5.88%).
Conclusions:
- The developed IoT-based pest detection platform is effective for integrated pest management and crop monitoring.
- The smart insect trap system, powered by CNN, offers a reliable and accurate solution for pest identification, mitigating risks associated with traditional methods.
- This technology holds significant potential to enhance agricultural productivity and contribute to global food security.
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