Enhanced Crop Protection: Mobile-Net V2 for Pest Identification

This project introduces a novel crop pest classification system that harnesses the MobileNet architecture, a lightweight convolutional neural network meticulously designed for efficient inference on mobile and embedded devices. Through the utilization of deep learning techniques, our meticulously crafted model achieves an outstanding accuracy rate of 96.89% in accurately identifying and categorizing various types of crop pests, showcasing its formidable potential to revolutionize pest detection within agricultural contexts. The inherent efficiency of the MobileNet architecture renders it particularly well-suited for deployment on platforms with limited computational resources, ensuring scalability and widespread accessibility for farmers operating across diverse agricultural landscapes. Our rigorous experimentation and comprehensive evaluation further validate the robustness and efficacy of our approach, underscoring its practical utility in real-world scenarios. By simplifying and expediting the process of pest identification, our proposed system not only equips farmers with invaluable insights but also empowers them to make informed decisions promptly, thereby facilitating early interventions and enhancing overall pest management practices. This transformative technology holds promise for fostering more sustainable crop production methodologies and ultimately contributing to improved yields and global food security. In conclusion, our project represents a significant leap forward in agricultural technology, offering a cost-effective and scalable solution to the perennial challenge of pest management, while simultaneously paving the way for more resilient and sustainable agricultural systems in the future.