Plant Disease Detection Using Yolo Machine Learning Approach Article Swipe
Rosemary Ngozi Ariwa
,
C. Markus
,
Nora Godwin Teneke
,
Gideon Adamu Shallangwa
,
K. G. Fumlack
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.52589/bjcnit-ejwgfw6d
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.52589/bjcnit-ejwgfw6d
Artificial intelligence and deep learning models are utilised in health, IT, animal and plant research, and more. Maize, one of the most widely eaten crops globally, is susceptible to a wide variety of disease that impede its development and reduce its output. The objective of this research work is to develop a deep learning-based model for detection of illnesses affecting maize leaves. Furthermore, the model that has been constructed not only forecasts illness but also furnishes illustrative visuals of leaf diseases, so facilitating the identification of disease types. To do this, a dataset including specified illnesses, including blight, common rust, gray leaf spot, and a healthy leaf, was obtained from Kaggle, a secondary source (Pant village). For data analysis, the cross-platform Anaconda Navigator was used, while the programming languages Python and Jupiter Notebook were implemented. The acquired data was used for both training and evaluating the models. The study presents a novel approach to plant disease detection using the YOLO deep learning model, implemented in Python and associated libraries. The Yolov8 algorithm was employed to develop a maize leaf detection system, which outperformed algorithms such as CNN (84%), KNN (81%), Random Forest (85%), and SVM (82%), achieving an impressive accuracy of 99.8%. Limitations of the study include the focus on only three maize leaf diseases and the reliance on single-leaf images for detection. Future research should address environmental elements like temperature and humidity, include numerous leaves in a frame for disease identification, and create disease stage detection methods.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.52589/bjcnit-ejwgfw6d
- https://abjournals.org/bjcnit/wp-content/uploads/sites/11/journal/published_paper/volume-7/issue-2/BJCNIT_EJWGFW6D.pdf
- OA Status
- hybrid
- Cited By
- 7
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400801762
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400801762Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.52589/bjcnit-ejwgfw6dDigital Object Identifier
- Title
-
Plant Disease Detection Using Yolo Machine Learning ApproachWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-19Full publication date if available
- Authors
-
Rosemary Ngozi Ariwa, C. Markus, Nora Godwin Teneke, Gideon Adamu Shallangwa, K. G. FumlackList of authors in order
- Landing page
-
https://doi.org/10.52589/bjcnit-ejwgfw6dPublisher landing page
- PDF URL
-
https://abjournals.org/bjcnit/wp-content/uploads/sites/11/journal/published_paper/volume-7/issue-2/BJCNIT_EJWGFW6D.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://abjournals.org/bjcnit/wp-content/uploads/sites/11/journal/published_paper/volume-7/issue-2/BJCNIT_EJWGFW6D.pdfDirect OA link when available
- Concepts
-
Python (programming language), Deep learning, Computer science, Artificial intelligence, Machine learning, Blight, Random forest, Plant disease, Support vector machine, Agronomy, Biotechnology, Biology, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7Per-year citation counts (last 5 years)
- References (count)
-
25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.from | 109 |
| abstract_inverted_index.gray | 100 |
| abstract_inverted_index.leaf | 79, 101, 178, 213 |
| abstract_inverted_index.like | 229 |
| abstract_inverted_index.most | 21 |
| abstract_inverted_index.only | 70, 210 |
| abstract_inverted_index.such | 184 |
| abstract_inverted_index.that | 34, 65 |
| abstract_inverted_index.this | 45 |
| abstract_inverted_index.used | 139 |
| abstract_inverted_index.were | 133 |
| abstract_inverted_index.wide | 30 |
| abstract_inverted_index.work | 47 |
| abstract_inverted_index.(Pant | 114 |
| abstract_inverted_index.crops | 24 |
| abstract_inverted_index.eaten | 23 |
| abstract_inverted_index.focus | 208 |
| abstract_inverted_index.frame | 238 |
| abstract_inverted_index.leaf, | 106 |
| abstract_inverted_index.maize | 60, 177, 212 |
| abstract_inverted_index.model | 54, 64 |
| abstract_inverted_index.more. | 16 |
| abstract_inverted_index.novel | 151 |
| abstract_inverted_index.plant | 13, 154 |
| abstract_inverted_index.rust, | 99 |
| abstract_inverted_index.spot, | 102 |
| abstract_inverted_index.stage | 245 |
| abstract_inverted_index.study | 148, 205 |
| abstract_inverted_index.this, | 90 |
| abstract_inverted_index.three | 211 |
| abstract_inverted_index.used, | 124 |
| abstract_inverted_index.using | 157 |
| abstract_inverted_index.which | 181 |
| abstract_inverted_index.while | 125 |
| abstract_inverted_index.(81%), | 189 |
| abstract_inverted_index.(82%), | 195 |
| abstract_inverted_index.(84%), | 187 |
| abstract_inverted_index.(85%), | 192 |
| abstract_inverted_index.99.8%. | 201 |
| abstract_inverted_index.Forest | 191 |
| abstract_inverted_index.Future | 223 |
| abstract_inverted_index.Maize, | 17 |
| abstract_inverted_index.Python | 129, 165 |
| abstract_inverted_index.Random | 190 |
| abstract_inverted_index.Yolov8 | 170 |
| abstract_inverted_index.animal | 11 |
| abstract_inverted_index.common | 98 |
| abstract_inverted_index.create | 243 |
| abstract_inverted_index.images | 220 |
| abstract_inverted_index.impede | 35 |
| abstract_inverted_index.leaves | 235 |
| abstract_inverted_index.model, | 162 |
| abstract_inverted_index.models | 5 |
| abstract_inverted_index.reduce | 39 |
| abstract_inverted_index.should | 225 |
| abstract_inverted_index.source | 113 |
| abstract_inverted_index.types. | 87 |
| abstract_inverted_index.widely | 22 |
| abstract_inverted_index.Jupiter | 131 |
| abstract_inverted_index.Kaggle, | 110 |
| abstract_inverted_index.address | 226 |
| abstract_inverted_index.blight, | 97 |
| abstract_inverted_index.dataset | 92 |
| abstract_inverted_index.develop | 50, 175 |
| abstract_inverted_index.disease | 33, 86, 155, 240, 244 |
| abstract_inverted_index.health, | 9 |
| abstract_inverted_index.healthy | 105 |
| abstract_inverted_index.illness | 72 |
| abstract_inverted_index.include | 206, 233 |
| abstract_inverted_index.leaves. | 61 |
| abstract_inverted_index.models. | 146 |
| abstract_inverted_index.output. | 41 |
| abstract_inverted_index.system, | 180 |
| abstract_inverted_index.variety | 31 |
| abstract_inverted_index.visuals | 77 |
| abstract_inverted_index.Anaconda | 121 |
| abstract_inverted_index.Notebook | 132 |
| abstract_inverted_index.accuracy | 199 |
| abstract_inverted_index.acquired | 136 |
| abstract_inverted_index.approach | 152 |
| abstract_inverted_index.diseases | 214 |
| abstract_inverted_index.elements | 228 |
| abstract_inverted_index.employed | 173 |
| abstract_inverted_index.learning | 4, 161 |
| abstract_inverted_index.methods. | 247 |
| abstract_inverted_index.numerous | 234 |
| abstract_inverted_index.obtained | 108 |
| abstract_inverted_index.presents | 149 |
| abstract_inverted_index.reliance | 217 |
| abstract_inverted_index.research | 46, 224 |
| abstract_inverted_index.training | 142 |
| abstract_inverted_index.utilised | 7 |
| abstract_inverted_index.Navigator | 122 |
| abstract_inverted_index.achieving | 196 |
| abstract_inverted_index.affecting | 59 |
| abstract_inverted_index.algorithm | 171 |
| abstract_inverted_index.analysis, | 118 |
| abstract_inverted_index.detection | 56, 156, 179, 246 |
| abstract_inverted_index.diseases, | 80 |
| abstract_inverted_index.forecasts | 71 |
| abstract_inverted_index.furnishes | 75 |
| abstract_inverted_index.globally, | 25 |
| abstract_inverted_index.humidity, | 232 |
| abstract_inverted_index.illnesses | 58 |
| abstract_inverted_index.including | 93, 96 |
| abstract_inverted_index.languages | 128 |
| abstract_inverted_index.objective | 43 |
| abstract_inverted_index.research, | 14 |
| abstract_inverted_index.secondary | 112 |
| abstract_inverted_index.specified | 94 |
| abstract_inverted_index.village). | 115 |
| abstract_inverted_index.Artificial | 0 |
| abstract_inverted_index.algorithms | 183 |
| abstract_inverted_index.associated | 167 |
| abstract_inverted_index.detection. | 222 |
| abstract_inverted_index.evaluating | 144 |
| abstract_inverted_index.illnesses, | 95 |
| abstract_inverted_index.impressive | 198 |
| abstract_inverted_index.libraries. | 168 |
| abstract_inverted_index.Limitations | 202 |
| abstract_inverted_index.constructed | 68 |
| abstract_inverted_index.development | 37 |
| abstract_inverted_index.implemented | 163 |
| abstract_inverted_index.programming | 127 |
| abstract_inverted_index.single-leaf | 219 |
| abstract_inverted_index.susceptible | 27 |
| abstract_inverted_index.temperature | 230 |
| abstract_inverted_index.Furthermore, | 62 |
| abstract_inverted_index.facilitating | 82 |
| abstract_inverted_index.illustrative | 76 |
| abstract_inverted_index.implemented. | 134 |
| abstract_inverted_index.intelligence | 1 |
| abstract_inverted_index.outperformed | 182 |
| abstract_inverted_index.environmental | 227 |
| abstract_inverted_index.cross-platform | 120 |
| abstract_inverted_index.identification | 84 |
| abstract_inverted_index.learning-based | 53 |
| abstract_inverted_index.identification, | 241 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.95340418 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |