Pest Detection

Pest detection in agriculture is rapidly advancing through the application of computer vision and machine learning, aiming to improve crop yields and reduce reliance on harmful pesticides. Current research heavily utilizes deep learning models, including convolutional neural networks (CNNs) like ResNet and MobileNet, often enhanced with attention mechanisms and integrated with other techniques such as state space models or natural language processing for multimodal analysis. These advancements enable more accurate and efficient pest identification, counting, and location prediction, leading to improved decision-making in integrated pest management strategies and more sustainable agricultural practices.