Enhancing Wheat Spike Counting and Disease Detection Using a Probability Density Attention Mechanism in Deep Learning Models for Precision Agriculture

Enhancing Wheat Spike Counting and Disease Detection Using a Probability Density Attention Mechanism in Deep Learning Models for Precision Agriculture

This study aims to improve the precision of wheat spike counting and disease detection, exploring the application of deep learning in the agricultural sector. Addressing the shortcomings of traditional detection methods, we propose an advanced feature extraction strategy and a model based on the pro...

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Bibliographic Details
Main Authors: Ruiheng Li, Wenjie Hong, Ruiming Wu, Yan Wang, Xiaohan Wu, Zhongtian Shi, Yifei Xu, Zixu Han, Chunli Lv
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Plants
Subjects:
wheat
spike
disease detection
smart agriculture
deep learning
Online Access:https://www.mdpi.com/2223-7747/13/24/3462
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