Drones, DNA, and weather: A phase-oriented hybrid engine predicts sugar beet disease

A fungus that can wipe out up to 50% of a sugar beet crop may soon meet its match in a new generation of smart disease ...
IEEE

Comparative Analysis of YOLOv12 and RF-DETR Models for Coffee Leaf Disease Detection Using Roboflow

Abstract: Coffee is a vital crop in the Philippines, yet its yield is increasingly threatened by foliar diseases such as coffee rust, Septoria, and Phoma leaf spot. Manual detection remains ...
IEEE

Detection of Powdery Mildew in Cherry Plant Leaves using Machine Learning and CNN

Abstract: Timed observation and accurate detection of plant diseases is vital for minimizing the damage to the crops and securing food for the long term, but particularly with cherry powdery mildew ...
IEEE

Crispvision: Apple Leaf Disease Detection Using Machine Learning and Deeper CNN

Abstract: Identifying diseases in apple leaves plays a vital role in boosting farm productivity and preventing crop losses. This research introduces a comprehensive approach for classifying images of ...
IEEE

Real-Time Fall Detection With Hybrid CNN-LSTM Using IWR6843AOP FMCW Radar

Abstract: Falls are a leading health risk for the elderly, highlighting the need for accurate and unobtrusive monitoring systems. Traditional camera-based approaches often raise privacy concerns, ...
IEEE

A Comparative Study for Yemeni Poets Detection Using TEXT-CNN and RNN-LSTM Text Classification

Abstract: The growing prevalence of internet usage has led to a substantial capacity in textual data. Text classification is an essential field in natural language processing (NLP). It differs in ...