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 ...

There are thousands of varieties of hosta — a leafy decorative plant that you’ve probably seen adorning outdoor spaces — being cultivated around the world, in a luxurious spread of colors, sizes, 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 ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Plant diseases threaten global food security, necessitating efficient, ...

In the garden, fall is almost as busy as spring. Winding your garden down and finishing last-minute chores before the weather turns for good can occupy much of your free time. Before you schedule your ...

This study proposes EDGE-MSE-YOLOv11, a novel lightweight rice disease detection model based on a unified Tri-Module Lightweight Perception Mechanism (TMLPM). This mechanism integrates three core ...

They look, move and even smell like the kind of furry Everglades marsh rabbit a Burmese python would love to eat. But these bunnies are robots meant to lure the giant invasive snakes out of their ...

Abstract: This study focuses on the early and accurate detection of tomato plant diseases using the lightweight and efficient deep learning model YOLOv11n. Early identification of plant diseases is ...

ABSTRACT: Timely and accurate detection of plant diseases is essential for improving crop yields and ensuring food security, particularly in regions like Cameroon, where farmers often rely on visual ...