Pest Identification in Crop Fields Using Convolutional Neural Networks (CNNs): A Deep Learning Approach to Precision Agriculture

Dr. Navneet Kaur

Abstract: The increasing threat of pest infestations poses severe challenges to global food production and agricultural sustainability. Manual pest identification remains time-consuming, subjective, and inefficient for large-scale monitoring. This paper proposes an automated framework for pest identification using Convolutional Neural Networks (CNNs), trained and validated on the benchmark IP102 dataset. The proposed method leverages transfer learning from ResNet-50 to extract robust visual features from field pest images, achieving an overall accuracy of 82.1%. The framework demonstrates significant potential to enhance precision agriculture by enabling scalable, real-time pest detection and classification, supporting timely intervention and reduced pesticide misuse. Results show that the model outperforms traditional image-processing techniques and provides a foundation for integrating deep learning with IoT-based smart farming systems.

Keywords: Precision Agriculture, Pest Identification, Convolutional Neural Networks, IP102 Dataset, Deep Learning, Image Classification, Sustainable Farming, Transfer Learning, ResNet-50, IP102 Benchmark, Fine-Grained Classification, Agricultural Computer Vision