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 ...
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: 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 ...
1 Ambam Computer Science and Application Laboratory & Department of Computer Engineering, Higher Institute of Transport, Logistics and Commerce, University of Ebolowa, Ebolowa, Cameroon. 2 Institut ...
Abstract: Plant diseases remain a significant threat to global agriculture, necessitating rapid and accurate detection to minimize crop loss. This paper presents a lightweight, end-to-end system for ...
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