Classification of cotton leaf disease using YOLOv8 based k-fold cross validation deep learning method for precision agriculture

Nature·Oct 13

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Researchers are exploring the use of deep learning methods for the classification and detection of diseases in crops, including cotton. Various techniques such as YOLOv8, ConvNeXt, and feature pyramid networks are being applied to image-based diagnosis and pest detection. The development of these methods aims to improve precision agriculture, but it is unclear which specific approach is being emphasized or has shown the most promise.

Full coverage

Image-based cotton leaf disease diagnosis using YOLO and faster R-CNN techniques

Nature·Dec 2

PalmNeXt: a ConvNeXt-based deep learning model for pest detection in date palm leaves

Frontiers·Jan 21

A Leaf-Level Dataset for Soybean-Cotton Detection and Segmentation

Nature·Apr 15

Monitoring and predicting cotton leaf diseases using deep learning approaches and mathematical models

Nature·Jul 2

Cotton leaf disease detection model focusing on small targets and comprehensive feature extraction

Nature·Nov 20

An efficient feature pyramid network with adaptive LSTM for pest detection and classification in IoT

Nature·Jan 6

Detection of weeds in teff crops using deep learning and UAV imagery for precision herbicide application

Nature·Aug 21

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