A Cross-Crop and Cross-Regional Generalized Deep Learning Framework for Intelligent Disease Detection and Economic Decision Support in Horticulture

Abstract

In facility horticultural production, intelligent disease recognition and precise intervention are vital for crop health and economic efficiency. We construct a multi-source dataset from Bayan Nur, Weifang, and Honghe that integrates handheld camera photos, drone field images, and laboratory-controlled samples. Handheld images capture fine lesion texture for close-up diagnosis common in greenhouses; drone images provide canopy-scale patterns and spatial context suited to open-field management; laboratory images offer controlled illumination and background for stable supervision and cross-crop feature learning. Our objective is robust cross-crop, cross-regional diagnosis and economically rational control. To this end, a model named CCGD-Net is proposed. It is designed as a multi-task framework. The framework incorporates a multi-scale perception module (MSFE) to produce hierarchical representations. It includes a cross-domain alignment module (CDAM) that reduces distribution shifts between greenhouse and open-field environments. The training follows an unsupervised domain adaptation setting that uses unlabeled target-region images. When such images are not available, the model functions in a pure generalization mode. The framework also integrates a regional economic strategy module (RESM) that transforms recognition outputs and local cost information into optimized intervention intensity. Experiments show an accuracy of 91.6%, an F1-score of 89.8%, and an mAP of 88.9%, outperforming Swin Transformer and ConvNeXt; removing RESM reduces F1 to 87.2%. In cross-regional testing (Weifang training → Honghe testing), the model attains an F1 of 88.0% and mAP of 86.5%. These results indicate that integrating complementary imaging modalities with domain alignment and economic optimization provides an effective solution for disease diagnosis across greenhouse and field systems.

Plant phenotyping relevance

植物病斑・冠層画像から病害状態を推定するマルチモーダル深層学習法を開発し、データセット、ドメイン適応、交差地域検証を含むため、植物フェノタイピング手法が中心である。

Code and data availability

The paper's local disease image datasets, environmental/economic parameters, CCGD-Net code, and trained models are not publicly deposited. The Data Availability Statement only offers contact with the corresponding author, and no authors' public URL for data or code is provided in the supplied blocks. Publicly cited第三方s

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