New global modeling shows that about 9.3 percent of the world’s land area is highly vulnerable to the risk of dangerous disease outbreaks. These hotspots are concentrated in Latin America and Oceania, ...
The PlantIF framework consists of image and text feature extractors, semantic space encoders, and a multimodal feature fusion module. Image and text feature extractors are used to present visual and ...
This project uses deep learning to automatically detect plant diseases from leaf images. The model leverages Convolutional Neural Networks (CNNs) and Transfer Learning (MobileNetV2) for accurate and ...
Accurate detection of crop diseases from unmanned aerial vehicle (UAV) imagery is critical for precision agriculture. This task remains challenging due to the complex backgrounds, variable scales of ...
Climate Lab is a Seattle Times initiative that explores the effects of climate change in the Pacific Northwest and beyond. The project is funded in part by The Bullitt Foundation, CO2 Foundation, Jim ...
Abstract: In India, various plant diseases affect agricultural productivity. For this reason, crop losses occur every year. On-time, the accurate detection of all diseases is essential to ensure ...
In general, disease refers to an illness of living organisms caused by either infection or health failure rather than an accident. Specifically, Encyclopedia of Microbiology (Third Edition) 2009 has ...
The early detection of Verticillium wilt (VW) in cotton is a critical challenge in agricultural disease management. Cotton, a vital global textile resource, is severely threatened by this devastating ...
Python 3.9.23 Libraries (install via pip install -r requirements.txt): torch==2.3.0 (or latest compatible version) torchvision==0.18.0 flask==3.0.0 scikit-learn==1.5. ...
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