{
  "abstract": "IntroductionPrecise mapping of sugarcane varieties is crucial for precision agriculture, yield evaluation, protection of breeders’ rights, efficient supply chain management and policy formulation. Previous studies highlighted that remote sensing techniques suffered issues such as low spatial resolution and inadequate field-validated ground truth data. Literature review reported usage of statistical analysis methods and machine learning approaches in classification of sugarcane varieties, while constraining the use of deep learning techniques, primarily due to low quality and insufficient dataset size. Furthermore, the literature highlights the scarcity of high spatial resolution datasets that could be attributed to employment of open-source hyper-spectral and low spatial resolution remote sensing data. To the best of our knowledge, neither any industrial solution exists nor any research study is conducted on sugarcane varietal classification in Pakistan.MethodsIn this study, we made an endeavor to prepare a high-resolution dataset of Pakistani sugarcane varieties (CPF-253 & SL-284) based on field-validated ground truth dataset using Pakistan Remote Sensing Satellite-1 (PRSS-1) single date imagery. We also included non crop classes like urban, water and road as well. Pan-sharpening is employed to enhance the spatial resolution of multispectral bands and retaining the original spectral characteristics, by aligning the 3 m bands to the 1 m panchromatic grid and then injecting the panchromatic residual. The dataset was prepared based on field-validated ground truth collection duly verified by sugarcane scientist, a farmer and research team to make the approach efficient and reliable. The study performed a random pixel split strategy as well as polygon-based experiments; the polygon split is the primary protocol so that a field is not shared across train and test. We proposed a Spatial Context Aware Transformer that includes the spatial context from neighboring pixels, resulting in an enhanced feature space. Specifically, our proposed model is characterized by a unique pixel feature augmentation approach, thus significantly increasing the size of the spectral feature space and making it possible to account for local neighborhood spectral context. We experimented with benchmark machine learning approaches such as support vector machine and random forest on our dataset to evaluate their potential as well. Moreover, we also evaluated deep learning methods suggested by previous studies like CNN and transformer architectures.ResultsThe experimental results demonstrate that our proposed spatial context-aware transformer performed better than other architectures on the polygon-based test set (accuracy 95.2%, macro F1 0.92), compared with SVM 90.5%, Random Forest 92.5%, 1-D CNN 93.0% and the Tabular Transformer 93.4% (gains of 4.7% and 1.8% in accuracy versus SVM and the Tabular-Transformer). On the same 3x3 pixels, a center-only Transformer scored 94.2%; the 3x3 Transformer 95.2%; and 3x3 MLP, CNN and RF scored 93.48%, 93.75% and 92.96%, showing that the neighborhood pixels spectral context along with attention mechanism account for the classification results. We also conducted a binary classification experiment employing CNN, Tabular Transformer, Centre-pixel only Transformer (1x1) and proposed Transformer (3x3). The results demonstrated that Center-pixel only Transformer achieved highest overall accuracy of 88.59%, followed by Tabular Transformer (87.63%), 1D-CNN (87.10%) and proposed Transformer (3x3) (86.99%).DiscussionThe feature significance analysis identified the Green Normalized Difference Vegetation Index and the red spectral band as the most discriminative features for sugarcane varieties classification. The study recommends that future research should incorporate time-series, multi-seasonal and multi-region imagery analysis including non-image information such as climate and production environment for highly precise sugarcane varieties classification in real-world conditions. precise sugarcane varieties classification in real-world conditions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Original Research",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "Shehryar Attaullah Khan"
    },
    {
      "name": "Musharif Ahmed"
    },
    {
      "name": "Naeem Fiaz"
    },
    {
      "name": "Maaz Alam"
    },
    {
      "name": "Atif Naseer"
    },
    {
      "name": "Sultan Daud Khan"
    },
    {
      "name": "Hina Afridi"
    },
    {
      "name": "Mohib Ullah"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/arr778xbsp5p5.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1981795",
  "fulltext_url": null,
  "id": "arr778xbsp5p5",
  "issns": [
    "1664-462X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1981795",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "feed-journal",
      "biology/plant-biology"
    ]
  ],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-09",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/arr778xbsp5p5.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:20:32Z",
      "native_id": "10.3389/fpls.2026.1981795",
      "source": "feed-frontiers",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1981795"
    },
    {
      "first_seen": "2026-11-09T12:23:05Z",
      "native_id": "10.3389/fpls.2026.1981795",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1981795/full"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "feed-journal:biology/plant-biology",
      "subfield": "plant-biology"
    }
  ],
  "title": "Fine-grained sugarcane varietal classification using high-resolution PRSS-1 satellite imagery: a field-validated dataset and a spatial context-aware deep learning framework",
  "title_text": "Fine-grained sugarcane varietal classification using high-resolution PRSS-1 satellite imagery: a field-validated dataset and a spatial context-aware deep learning framework",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:23:05Z",
  "url": "https://research.draconitic.com/paper/arr778xbsp5p5",
  "venue": "Frontiers in Plant Science",
  "versions": []
}