Deep learning based agricultural pest monitoring and classification

Precise pest classification plays an essential role in smart agriculture. Crop yields are severely impacted by pest damage, which poses a critical challenge for agricultural production and the economy. Identifying pests is of utmost importance, but manual identification is both labor-intensive and time-consuming. Therefore, the realm of pest identification and classification requires more advanced and effective techniques. The proposed work presents an innovative automatic approach based on the incorporation of deep learning in smart farming for pest monitoring and classification to tackle this challenge. In this work, the IP102 dataset is used to identify and classify 82 classes of pests. Autoencoder is…