1. Introduction
Chilli crops are highly vulnerable to pest infestations that severely impact yield and quality. Traditional detection methods rely on manual observation, which is often labor-intensive, time-consuming, and prone to errors. To overcome these limitations, deep learning technology has emerged as a powerful solution for real-time and automated pest detection. This research investigates various AI-based models to determine the most accurate and efficient approach for identifying chilli pests under different environmental conditions.
2. Dataset Preparation and Image Annotation Techniques
High-quality datasets are essential for accurate deep learning model training. This section explores the process of collecting chilli leaf and fruit images under natural farm conditions, followed by manual and semi-automated annotation of pest-affected regions. Techniques such as data augmentation, noise reduction, and class balancing are applied to ensure robustness against variations in lighting, angles, and pest density.
3. Performance Comparison of Deep Learning Models
Multiple deep learning architectures such as Convolutional Neural Networks (CNN), YOLO (You Only Look Once), and ResNet are evaluated based on accuracy, precision, recall, F1-score, and inference time. YOLO demonstrates superior speed for real-time applications, while ResNet provides higher precision in complex backgrounds. The analysis highlights trade-offs between lightweight and high-performance architectures depending on deployment needs.
4. Real-Time Detection and Deployment Considerations
To transition from laboratory experiments to practical applications, the study examines deployment on edge devices like Raspberry Pi and mobile-based detection systems. Challenges such as computational limitations, model compression, and offline usability are addressed. The findings indicate that optimized YOLO variants are highly suitable for real-time field monitoring.
5. Impact on Sustainable Farming Practices
The adoption of AI-driven detection systems reduces pesticide misuse by enabling early-stage and targeted intervention. This not only improves crop health but also minimizes environmental impact and economic loss. The integration of deep learning models with IoT-based alert systems further enhances precision agriculture strategies.
6. Future Research Directions
Future work may include multi-pest classification, integration with drone surveillance, and hybrid models combining thermal and spectral imaging. Additionally, developing open-source datasets and collaborative platforms can accelerate innovation in pest management using artificial intelligence.
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