# Agricultural Image Analysis

A Vision Transformer model fine-tuned on the beans dataset based on google/vit-base-patch16-224-in21k for image classification tasks

A plant leaf disease image segmentation model based on the SegFormer architecture, fine-tuned on an enhanced dataset, capable of accurately identifying multiple types of plant diseases.

A high-precision image classification model fine-tuned on the beans dataset based on Google's ViT model

A fine-tuned bean leaf disease classification model based on Google Vision Transformer (ViT) architecture

This model is a fine-tuned image classification model based on Google's ViT-base-patch16-224-in21k on the beans dataset, achieving an accuracy of 98.5%.

A corn leaf detection model based on ViT architecture, achieving 91.54% accuracy on the evaluation set

An image classification model fine-tuned on the beans dataset based on Google's ViT model, achieving an accuracy of 97.74%

This model is a fine-tuned image classification model based on Google's ViT architecture on the beans dataset, achieving an accuracy of 97.74%.

This model is an image classification model fine-tuned on the beans dataset based on Google's ViT architecture, achieving an accuracy of 97.74%.

A high-precision image classification model fine-tuned on the beans dataset based on Google's ViT model

This model is based on Google's ViT architecture, fine-tuned on the beans dataset, achieving an accuracy of 98.5%.

An image classification model fine-tuned on the beans dataset based on Google's ViT model, achieving 98.5% accuracy

A plant disease image classification model based on the Swin Transformer architecture, fine-tuned on a plant disease dataset with an accuracy of 96.9%

This is an image classification model generated by HuggingPics, specifically designed to identify different types of grains.

This model is an image classification model fine-tuned on the beans dataset based on the ConvNeXt-Tiny architecture, achieving an accuracy of 96.09%.

This is a micro image classification model based on the Swin Transformer architecture, specifically fine-tuned for plant health diagnosis tasks, achieving 99.83% accuracy on the evaluation set.