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

Abstract

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 probability density attention mechanism, designed to more effectively handle feature extraction in complex backgrounds and dense areas. Through comparative experiments with various advanced models, we comprehensively evaluate the performance of our model. In the disease detection task, our model performs excellently, achieving a precision of 0.93, a recall of 0.89, an accuracy of 0.91, and an mAP of 0.90. By introducing the density loss function, we are able to effectively improve the detection accuracy when dealing with high-density regions. In the wheat spike counting task, the model similarly demonstrates a strong performance, with a precision of 0.91, a recall of 0.88, an accuracy of 0.90, and an mAP of 0.90, further validating its effectiveness. Furthermore, this paper also conducts ablation experiments on different loss functions. The results of this research provide a new method for wheat spike counting and disease detection, fully reflecting the application value of deep learning in precision agriculture. By combining the probability density attention mechanism and the density loss function, the proposed model significantly improves the detection accuracy and efficiency, offering important references for future related research.

Plant phenotyping relevance

コムギ穂数の計数と植物病害の検出を対象に、深層学習モデルと特徴抽出・損失関数を開発し、比較およびアブレーション実験で評価しており、表現型取得手法が中心である。

Code and data availability

The supplied blocks describe a self-collected wheat disease/spike-counting image dataset and a custom model, but contain no public deposit of the authors' data, images, annotations, code, or trained models. The Kaggle wheat spike dataset is cited prior work (ref 47), not a paper-specific asset, and no code availability

No evidence-backed public reproduction asset is currently recorded.