Enhancing sugarcane leaf disease classification using vision transformers over CNNs Article Swipe
Sandeep Miryala
,
Krupa Rasane
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s44163-025-00340-7
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s44163-025-00340-7
Sugarcane is a globally significant crop facing threats from leaf diseases that impact its productivity. Traditional detection methods are often inefficient and time-consuming. This study explores the use of Vision Transformers (ViT) for classifying sugarcane leaf diseases and compares their performance with traditional CNNs. A dataset of 19,926 images across six classes was used to fine-tune both ViT and CNN models. The optimized ViT model achieved a test accuracy of 96.53%, outperforming the CNN models (ResNet50 and VGG16) with accuracies of 91.92% and 92.30%, respectively. These findings demonstrate the superior performance of ViTs over CNNs in early disease detection for sustainable crop management. Future work will focus on expanding the dataset and optimizing model parameters for further improvements in disease classification accuracy.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s44163-025-00340-7
- https://link.springer.com/content/pdf/10.1007/s44163-025-00340-7.pdf
- OA Status
- gold
- References
- 13
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410990199
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4410990199Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s44163-025-00340-7Digital Object Identifier
- Title
-
Enhancing sugarcane leaf disease classification using vision transformers over CNNsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-03Full publication date if available
- Authors
-
Sandeep Miryala, Krupa RasaneList of authors in order
- Landing page
-
https://doi.org/10.1007/s44163-025-00340-7Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s44163-025-00340-7.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://link.springer.com/content/pdf/10.1007/s44163-025-00340-7.pdfDirect OA link when available
- Concepts
-
Artificial intelligence, Transformer, Pattern recognition (psychology), Computer science, Computer vision, Engineering, Electrical engineering, VoltageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
13Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4410990199 |
|---|---|
| doi | https://doi.org/10.1007/s44163-025-00340-7 |
| ids.doi | https://doi.org/10.1007/s44163-025-00340-7 |
| ids.openalex | https://openalex.org/W4410990199 |
| fwci | 0.0 |
| type | article |
| title | Enhancing sugarcane leaf disease classification using vision transformers over CNNs |
| biblio.issue | 1 |
| biblio.volume | 5 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10616 |
| topics[0].field.id | https://openalex.org/fields/11 |
| topics[0].field.display_name | Agricultural and Biological Sciences |
| topics[0].score | 0.9998000264167786 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1110 |
| topics[0].subfield.display_name | Plant Science |
| topics[0].display_name | Smart Agriculture and AI |
| topics[1].id | https://openalex.org/T10640 |
| topics[1].field.id | https://openalex.org/fields/16 |
| topics[1].field.display_name | Chemistry |
| topics[1].score | 0.9889000058174133 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1602 |
| topics[1].subfield.display_name | Analytical Chemistry |
| topics[1].display_name | Spectroscopy and Chemometric Analyses |
| topics[2].id | https://openalex.org/T14365 |
| topics[2].field.id | https://openalex.org/fields/11 |
| topics[2].field.display_name | Agricultural and Biological Sciences |
| topics[2].score | 0.9690999984741211 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1110 |
| topics[2].subfield.display_name | Plant Science |
| topics[2].display_name | Leaf Properties and Growth Measurement |
| is_xpac | False |
| apc_list.value | 990 |
| apc_list.currency | EUR |
| apc_list.value_usd | 1067 |
| apc_paid.value | 990 |
| apc_paid.currency | EUR |
| apc_paid.value_usd | 1067 |
| concepts[0].id | https://openalex.org/C154945302 |
| concepts[0].level | 1 |
| concepts[0].score | 0.47717562317848206 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[0].display_name | Artificial intelligence |
| concepts[1].id | https://openalex.org/C66322947 |
| concepts[1].level | 3 |
| concepts[1].score | 0.4133955240249634 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11658 |
| concepts[1].display_name | Transformer |
| concepts[2].id | https://openalex.org/C153180895 |
| concepts[2].level | 2 |
| concepts[2].score | 0.40461084246635437 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[2].display_name | Pattern recognition (psychology) |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.379733681678772 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C31972630 |
| concepts[4].level | 1 |
| concepts[4].score | 0.3212786614894867 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[4].display_name | Computer vision |
| concepts[5].id | https://openalex.org/C127413603 |
| concepts[5].level | 0 |
| concepts[5].score | 0.22723346948623657 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[5].display_name | Engineering |
| concepts[6].id | https://openalex.org/C119599485 |
| concepts[6].level | 1 |
| concepts[6].score | 0.07301688194274902 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q43035 |
| concepts[6].display_name | Electrical engineering |
| concepts[7].id | https://openalex.org/C165801399 |
| concepts[7].level | 2 |
| concepts[7].score | 0.0 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q25428 |
| concepts[7].display_name | Voltage |
| keywords[0].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[0].score | 0.47717562317848206 |
| keywords[0].display_name | Artificial intelligence |
| keywords[1].id | https://openalex.org/keywords/transformer |
| keywords[1].score | 0.4133955240249634 |
| keywords[1].display_name | Transformer |
| keywords[2].id | https://openalex.org/keywords/pattern-recognition |
| keywords[2].score | 0.40461084246635437 |
| keywords[2].display_name | Pattern recognition (psychology) |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.379733681678772 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/computer-vision |
| keywords[4].score | 0.3212786614894867 |
| keywords[4].display_name | Computer vision |
| keywords[5].id | https://openalex.org/keywords/engineering |
| keywords[5].score | 0.22723346948623657 |
| keywords[5].display_name | Engineering |
| keywords[6].id | https://openalex.org/keywords/electrical-engineering |
| keywords[6].score | 0.07301688194274902 |
| keywords[6].display_name | Electrical engineering |
| language | en |
| locations[0].id | doi:10.1007/s44163-025-00340-7 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210220416 |
| locations[0].source.issn | 2731-0809 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2731-0809 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Discover Artificial Intelligence |
| locations[0].source.host_organization | https://openalex.org/P4310319965 |
| locations[0].source.host_organization_name | Springer Nature |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319965 |
| locations[0].source.host_organization_lineage_names | Springer Nature |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | https://link.springer.com/content/pdf/10.1007/s44163-025-00340-7.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Discover Artificial Intelligence |
| locations[0].landing_page_url | https://doi.org/10.1007/s44163-025-00340-7 |
| locations[1].id | pmh:oai:doaj.org/article:09d986effd9743269b24998c492fc566 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Discover Artificial Intelligence, Vol 5, Iss 1, Pp 1-18 (2025) |
| locations[1].landing_page_url | https://doaj.org/article/09d986effd9743269b24998c492fc566 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5026474122 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-1277-1745 |
| authorships[0].author.display_name | Sandeep Miryala |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Saritha Miryala |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5017008597 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Krupa Rasane |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Krupa Rasane |
| authorships[1].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://link.springer.com/content/pdf/10.1007/s44163-025-00340-7.pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Enhancing sugarcane leaf disease classification using vision transformers over CNNs |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10616 |
| primary_topic.field.id | https://openalex.org/fields/11 |
| primary_topic.field.display_name | Agricultural and Biological Sciences |
| primary_topic.score | 0.9998000264167786 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1110 |
| primary_topic.subfield.display_name | Plant Science |
| primary_topic.display_name | Smart Agriculture and AI |
| related_works | https://openalex.org/W2772917594, https://openalex.org/W2036807459, https://openalex.org/W2058170566, https://openalex.org/W2755342338, https://openalex.org/W2166024367, https://openalex.org/W3116076068, https://openalex.org/W2229312674, https://openalex.org/W2951359407, https://openalex.org/W2079911747, https://openalex.org/W1969923398 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1007/s44163-025-00340-7 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210220416 |
| best_oa_location.source.issn | 2731-0809 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2731-0809 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Discover Artificial Intelligence |
| best_oa_location.source.host_organization | https://openalex.org/P4310319965 |
| best_oa_location.source.host_organization_name | Springer Nature |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319965 |
| best_oa_location.source.host_organization_lineage_names | Springer Nature |
| best_oa_location.license | cc-by-nc-nd |
| best_oa_location.pdf_url | https://link.springer.com/content/pdf/10.1007/s44163-025-00340-7.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Discover Artificial Intelligence |
| best_oa_location.landing_page_url | https://doi.org/10.1007/s44163-025-00340-7 |
| primary_location.id | doi:10.1007/s44163-025-00340-7 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210220416 |
| primary_location.source.issn | 2731-0809 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2731-0809 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Discover Artificial Intelligence |
| primary_location.source.host_organization | https://openalex.org/P4310319965 |
| primary_location.source.host_organization_name | Springer Nature |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319965 |
| primary_location.source.host_organization_lineage_names | Springer Nature |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | https://link.springer.com/content/pdf/10.1007/s44163-025-00340-7.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Discover Artificial Intelligence |
| primary_location.landing_page_url | https://doi.org/10.1007/s44163-025-00340-7 |
| publication_date | 2025-06-03 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W4403723052, https://openalex.org/W2949060638, https://openalex.org/W2982381523, https://openalex.org/W4377981429, https://openalex.org/W4313396197, https://openalex.org/W4391531549, https://openalex.org/W4399927709, https://openalex.org/W4406026285, https://openalex.org/W4393187924, https://openalex.org/W4402994936, https://openalex.org/W2194775991, https://openalex.org/W3188465708, https://openalex.org/W4283830389 |
| referenced_works_count | 13 |
| abstract_inverted_index.A | 45 |
| abstract_inverted_index.a | 3, 67 |
| abstract_inverted_index.in | 96, 119 |
| abstract_inverted_index.is | 2 |
| abstract_inverted_index.of | 29, 47, 70, 81, 92 |
| abstract_inverted_index.on | 108 |
| abstract_inverted_index.to | 55 |
| abstract_inverted_index.CNN | 60, 74 |
| abstract_inverted_index.The | 62 |
| abstract_inverted_index.ViT | 58, 64 |
| abstract_inverted_index.and | 22, 38, 59, 77, 83, 112 |
| abstract_inverted_index.are | 19 |
| abstract_inverted_index.for | 33, 100, 116 |
| abstract_inverted_index.its | 14 |
| abstract_inverted_index.six | 51 |
| abstract_inverted_index.the | 27, 73, 89, 110 |
| abstract_inverted_index.use | 28 |
| abstract_inverted_index.was | 53 |
| abstract_inverted_index.CNNs | 95 |
| abstract_inverted_index.This | 24 |
| abstract_inverted_index.ViTs | 93 |
| abstract_inverted_index.both | 57 |
| abstract_inverted_index.crop | 6, 102 |
| abstract_inverted_index.from | 9 |
| abstract_inverted_index.leaf | 10, 36 |
| abstract_inverted_index.over | 94 |
| abstract_inverted_index.test | 68 |
| abstract_inverted_index.that | 12 |
| abstract_inverted_index.used | 54 |
| abstract_inverted_index.will | 106 |
| abstract_inverted_index.with | 42, 79 |
| abstract_inverted_index.work | 105 |
| abstract_inverted_index.(ViT) | 32 |
| abstract_inverted_index.CNNs. | 44 |
| abstract_inverted_index.These | 86 |
| abstract_inverted_index.early | 97 |
| abstract_inverted_index.focus | 107 |
| abstract_inverted_index.model | 65, 114 |
| abstract_inverted_index.often | 20 |
| abstract_inverted_index.study | 25 |
| abstract_inverted_index.their | 40 |
| abstract_inverted_index.19,926 | 48 |
| abstract_inverted_index.91.92% | 82 |
| abstract_inverted_index.Future | 104 |
| abstract_inverted_index.VGG16) | 78 |
| abstract_inverted_index.Vision | 30 |
| abstract_inverted_index.across | 50 |
| abstract_inverted_index.facing | 7 |
| abstract_inverted_index.images | 49 |
| abstract_inverted_index.impact | 13 |
| abstract_inverted_index.models | 75 |
| abstract_inverted_index.92.30%, | 84 |
| abstract_inverted_index.96.53%, | 71 |
| abstract_inverted_index.classes | 52 |
| abstract_inverted_index.dataset | 46, 111 |
| abstract_inverted_index.disease | 98, 120 |
| abstract_inverted_index.further | 117 |
| abstract_inverted_index.methods | 18 |
| abstract_inverted_index.models. | 61 |
| abstract_inverted_index.threats | 8 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.accuracy | 69 |
| abstract_inverted_index.achieved | 66 |
| abstract_inverted_index.compares | 39 |
| abstract_inverted_index.diseases | 11, 37 |
| abstract_inverted_index.explores | 26 |
| abstract_inverted_index.findings | 87 |
| abstract_inverted_index.globally | 4 |
| abstract_inverted_index.superior | 90 |
| abstract_inverted_index.(ResNet50 | 76 |
| abstract_inverted_index.Sugarcane | 1 |
| abstract_inverted_index.accuracy. | 122 |
| abstract_inverted_index.detection | 17, 99 |
| abstract_inverted_index.expanding | 109 |
| abstract_inverted_index.fine-tune | 56 |
| abstract_inverted_index.optimized | 63 |
| abstract_inverted_index.sugarcane | 35 |
| abstract_inverted_index.accuracies | 80 |
| abstract_inverted_index.optimizing | 113 |
| abstract_inverted_index.parameters | 115 |
| abstract_inverted_index.Traditional | 16 |
| abstract_inverted_index.classifying | 34 |
| abstract_inverted_index.demonstrate | 88 |
| abstract_inverted_index.inefficient | 21 |
| abstract_inverted_index.management. | 103 |
| abstract_inverted_index.performance | 41, 91 |
| abstract_inverted_index.significant | 5 |
| abstract_inverted_index.sustainable | 101 |
| abstract_inverted_index.traditional | 43 |
| abstract_inverted_index.Transformers | 31 |
| abstract_inverted_index.improvements | 118 |
| abstract_inverted_index.outperforming | 72 |
| abstract_inverted_index.productivity. | 15 |
| abstract_inverted_index.respectively. | 85 |
| abstract_inverted_index.classification | 121 |
| abstract_inverted_index.time-consuming. | 23 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.8399999737739563 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.14122048 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |