Classification of Corn Diseases using Random Forest, Neural Network, and Naive Bayes Methods Article Swipe
Ahmad Ubaidillah
,
Eka Mala Sari Rochman
,
Doni Abdul Fatah
,
Aeri Rachmad
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2406/1/012023
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2406/1/012023
Corn is one of the staple foods consumed by many people after rice plants, especially in Indonesia. High consumer demand requires corn production in large quantities to meet these needs. However, corn production is not always in large quantities due to several factors, namely diseases in corn plants. Unhealthy corn plants can reduce the amount of production. Healthy and unhealthy corn plants can be identified manually, but this method is not efficient, so in this study, it is proposed to classify corn diseases using the Random Forest, Neural Network, and Nave Bayes methods. The dataset used is a collection of corn leaf images taken from farmers’ fields in the Madura Region with four target classes, namely healthy, gray leaf spot, blight, and common rust. Based on the test results, the classification using the Neural Network method provides a better accuracy value than the other two methods in classifying corn leaf datasets, namely the AUC value reaches 90.09%, classification accuracy is 74.44%, f1 score is 72.01%, precision is 74.14% and recall by 74.43%.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/2406/1/012023
- https://iopscience.iop.org/article/10.1088/1742-6596/2406/1/012023/pdf
- OA Status
- diamond
- Cited By
- 19
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4312192482
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4312192482Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1742-6596/2406/1/012023Digital Object Identifier
- Title
-
Classification of Corn Diseases using Random Forest, Neural Network, and Naive Bayes MethodsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-01Full publication date if available
- Authors
-
Ahmad Ubaidillah, Eka Mala Sari Rochman, Doni Abdul Fatah, Aeri RachmadList of authors in order
- Landing page
-
https://doi.org/10.1088/1742-6596/2406/1/012023Publisher landing page
- PDF URL
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https://iopscience.iop.org/article/10.1088/1742-6596/2406/1/012023/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://iopscience.iop.org/article/10.1088/1742-6596/2406/1/012023/pdfDirect OA link when available
- Concepts
-
Naive Bayes classifier, Random forest, Artificial neural network, Crop, Blight, Mathematics, Agronomy, Artificial intelligence, Computer science, Biology, Support vector machineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
19Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 10, 2023: 6Per-year citation counts (last 5 years)
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27Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.with | 112 |
| abstract_inverted_index.Based | 125 |
| abstract_inverted_index.Bayes | 92 |
| abstract_inverted_index.after | 12 |
| abstract_inverted_index.foods | 7 |
| abstract_inverted_index.large | 25, 38 |
| abstract_inverted_index.other | 144 |
| abstract_inverted_index.rust. | 124 |
| abstract_inverted_index.score | 163 |
| abstract_inverted_index.spot, | 120 |
| abstract_inverted_index.taken | 104 |
| abstract_inverted_index.these | 29 |
| abstract_inverted_index.using | 84, 132 |
| abstract_inverted_index.value | 141, 155 |
| abstract_inverted_index.74.14% | 168 |
| abstract_inverted_index.Madura | 110 |
| abstract_inverted_index.Neural | 88, 134 |
| abstract_inverted_index.Random | 86 |
| abstract_inverted_index.Region | 111 |
| abstract_inverted_index.always | 36 |
| abstract_inverted_index.amount | 55 |
| abstract_inverted_index.better | 139 |
| abstract_inverted_index.common | 123 |
| abstract_inverted_index.demand | 20 |
| abstract_inverted_index.fields | 107 |
| abstract_inverted_index.images | 103 |
| abstract_inverted_index.method | 69, 136 |
| abstract_inverted_index.namely | 44, 116, 152 |
| abstract_inverted_index.needs. | 30 |
| abstract_inverted_index.people | 11 |
| abstract_inverted_index.plants | 51, 62 |
| abstract_inverted_index.recall | 170 |
| abstract_inverted_index.reduce | 53 |
| abstract_inverted_index.staple | 6 |
| abstract_inverted_index.study, | 76 |
| abstract_inverted_index.target | 114 |
| abstract_inverted_index.72.01%, | 165 |
| abstract_inverted_index.74.43%. | 172 |
| abstract_inverted_index.74.44%, | 161 |
| abstract_inverted_index.90.09%, | 157 |
| abstract_inverted_index.Forest, | 87 |
| abstract_inverted_index.Healthy | 58 |
| abstract_inverted_index.Network | 135 |
| abstract_inverted_index.blight, | 121 |
| abstract_inverted_index.dataset | 95 |
| abstract_inverted_index.methods | 146 |
| abstract_inverted_index.plants, | 14 |
| abstract_inverted_index.plants. | 48 |
| abstract_inverted_index.reaches | 156 |
| abstract_inverted_index.several | 42 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 31 |
| abstract_inverted_index.Network, | 89 |
| abstract_inverted_index.accuracy | 140, 159 |
| abstract_inverted_index.classes, | 115 |
| abstract_inverted_index.classify | 81 |
| abstract_inverted_index.consumed | 8 |
| abstract_inverted_index.consumer | 19 |
| abstract_inverted_index.diseases | 45, 83 |
| abstract_inverted_index.factors, | 43 |
| abstract_inverted_index.healthy, | 117 |
| abstract_inverted_index.methods. | 93 |
| abstract_inverted_index.proposed | 79 |
| abstract_inverted_index.provides | 137 |
| abstract_inverted_index.requires | 21 |
| abstract_inverted_index.results, | 129 |
| abstract_inverted_index.Unhealthy | 49 |
| abstract_inverted_index.datasets, | 151 |
| abstract_inverted_index.manually, | 66 |
| abstract_inverted_index.precision | 166 |
| abstract_inverted_index.unhealthy | 60 |
| abstract_inverted_index.Indonesia. | 17 |
| abstract_inverted_index.collection | 99 |
| abstract_inverted_index.efficient, | 72 |
| abstract_inverted_index.especially | 15 |
| abstract_inverted_index.farmers’ | 106 |
| abstract_inverted_index.identified | 65 |
| abstract_inverted_index.production | 23, 33 |
| abstract_inverted_index.quantities | 26, 39 |
| abstract_inverted_index.classifying | 148 |
| abstract_inverted_index.production. | 57 |
| abstract_inverted_index.classification | 131, 158 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.91712601 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |