Plant pathology identification using local-global feature level based on transformer

Abstract

Deep learning plays a crucial role in addressing the challenge of plant disease identification in the field of agriculture. Detecting diseases in plants requires extensive effort, along with a comprehensive understanding of various plant diseases and increased processing time. Balancing both speed and accuracy in predicting leaf diseases in plants can significantly improve crop production and reduce environmental damage. In this paper, we examined deseases on popular plants in agriculture. We proposed a novel model to predict crop pathology on a feature space of global-local based on transformer aggregation. Paticular, we use refined feature of different layer to correlate semantics from high-level feature and low-level feature. Besides, to capture the extended temporal scale across the entire image, we employ a transformer to discern long-range dependencies among frames. Subsequently, the enhanced features incorporating these dependencies are inputted into a classifier for preliminary crop pathology prediction. The plant village dataset and VietNam strawberry disease (VNStr) dataset were utilized for training and disease classification in the experiments. Extensive experiments show that the proposed method outperforms by 99.18% and 94.05% accuracy in plant village and VNStr, respectivly. The model after being judged was applied on Android devices and therefore is easy to use.

Plant phenotyping relevance

植物葉の病害を画像から分類するTransformerベースの手法を提案し、複数データセットで精度検証しているため、植物フェノタイピング手法が中心である。

Code and data availability

The paper uses the public PlantVillage dataset and a self-collected VNStr dataset, but provides no public deposit, URL, or availability statement for its code, trained models, VNStr data, or any paper-specific asset. PlantVillage is a cited third-party dataset, not a paper-specific asset.

No evidence-backed public reproduction asset is currently recorded.