Pest Identification

Pest identification in agriculture is crucial for effective pest management and maximizing crop yields. Current research focuses on developing robust and accurate automated identification systems using deep learning models, such as convolutional neural networks (CNNs), vision transformers (ViTs), and hybrid approaches incorporating state space models and attention mechanisms, often trained on large, high-quality datasets captured by UAVs or mobile devices. These advancements aim to improve the speed and accuracy of pest detection, enabling timely interventions and reducing reliance on manual identification methods. The resulting impact extends to improved food security and reduced economic losses for farmers.