Traditional methods of disease recognition, relying heavily on human expertise and visual inspection, often fall short, especially in complex and noisy field environments. PlantIF marks a significant ...

The growing global population and rising concerns about food security highlight the critical need for intelligent agriculture. Among various technologies, plant disease detection is vital but faces ...

Abstract: Global food security is seriously threatened by plant diseases, especially in areas with limited access to prompt professional diagnosis. We introduce PlantNet, a deep learning-powered ...

This project aims to develop a method for detecting plant diseases using CNNs by analyzing leaf images.The CNNs are proficient in handling large datasets and can dynamically learn new features from ...

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 ...

The Apple Watch is on the wrist of millions of users across the world. Over the years, Apple has developed algorithms that collect data from the integrated PPG sensor to sense irregular heart rhythms ...

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 ...

Ralstonia solanacearum in a potato plant. The bacterium destroys the vascular system in plants, causing them to succumb to wilt disease. (Credit: Amilcar Sanchez) Scientists at the University of ...