Self-supervised learning (SSL) is a potential deep learning (DL) technique that uses
massive volumes of unlabeled data to train neural networks. SSL techniques have evolved …

Artificial intelligence (AI) is revolutionizing plant sciences by enabling precise plant species
identification, early disease diagnosis, crop yield prediction, and precision agriculture …

Forest fires, influenced by climatic and ecological changes, pose significant risks to global
ecosystems and human communities. To address this challenge, our research introduces a …

In computer vision, recognizing plant pictures has emerged as a multidisciplinary area of
interest. In the last several years, much research has been conducted to determine the type …

In response to the current lack of annotations for flower images and insufficient focus on key
image features in traditional fine-grained flower image classification based on deep …

Introduction Grapes are prone to various diseases throughout their growth cycle, and the
failure to promptly control these diseases can result in reduced production and even …

Lithological identification and map** using remote sensing (RS) imagery are challenging.
Traditional lithological map** relies mainly on multispectral data and machine learning …

Medical images play a vital role in medical analysis by providing crucial information about
patients' pathological conditions. However, the quality of these images can be compromised …

This study examines the effectiveness of five deep learning models—ViTb-19, SwinV2-t,
VGG-16, ResNet-50, and DenseNet-121—in distinguishing different vegetation types in the …

Natural reserves, such as the Santurbán Moor in Colombia, are ecologically important but
face significant threats from activities like mining and agriculture. Preserving biodiversity in …