An intelligent identification for pest and disease detection in wheat leaf based on environmental data using multimodal data fusion.

Abstract

The rapid development of intelligent technologies has transformed various industries, and agriculture benefits greatly from precision farming innovations. One of the remarkable achievements in agriculture is enhancing pest and disease identification for better crop health control and higher yields. This paper presents novel models of a multimodal data fusion technique to meet the growing need for accurate and timely wheat pest and disease identification. It combines image processing, sensor - derived environmental data, and machine learning for reliable wheat pest and disease diagnosis. First, deep - learning algorithms in image analysis detect early - stage pests and diseases on wheat leaves. Second, environmental data such as temperature and humidity improve diagnosis. Third, the data fusion process integrates image data for further analysis. Finally, several criteria compare the proposed model with previous methods. Experimental results show the proposed techniques achieve a detection accuracy of 96.5%, precision of 94.8%, recall of 97.2%, F1 score of 95.9%, MCC of 0.91, and AUC - ROC of 98.4%. The training time is 15.3 hours, and the inference time is 180 ms. Compared with CNN - based and SVM - based techniques, the proposed model's improvement is analyzed. It can be adapted for real - time use and applied to more crops and diseases.

Code and data availability

The paper uses a public Kaggle Wheat Leaf Dataset (407 images) and sensor data, but no authors' public code, model checkpoints, or dataset URL is provided in the supplied blocks, and no allowed_urls exist to cite. The Kaggle dataset is a third-party public asset, not an authors' paper-specific deposit with an available

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