Detection and Classification of Agriculture Pests based on Leaf Images Using Efficient Net V2-L Convolution Neural network with transfer learning

we provide a new method for classifying pest images using the Efficient NetV2L convolution neural network architecture. Our model has a high degree of accuracy in identifying agricultural pests, such as ants, bees, beetles, and others, by utilizing transfer learning on a carefully selected dataset. We accomplish efficient detection and timely interventions to reduce crop losses by fine-tuning pre-trained weights. The dataset, gathered from Flickr, guarantees diversity and realism in Pictures of pests. Our finding highlights how machine learning may help advance sustainable agricultural methods by lowering the need for chemical pesticides. We talk about potential and problems, providing guidance for further study and advancement in the field of pest image categorization.