Deep Learning based Automated Disease Detection and Classification Model for Precision Agriculture Article Swipe
A. Pavithra
,
G. Kalpana
,
T. Vigneswaran
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2263078/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2263078/v1
Plant phenotyping and Precision agriculture are information-and technology-oriented fields with specific challenges and demands for the detection and diagnosis of plant disease. Precision agriculture can be referred as a crop management method related to the spatial and temporal variability in soil and crop factors within a field. Accurate and early diagnosis and detection of plant diseases were major factors in plant production and the reduction of quantitative and qualitative losses in crop yield. Advancement of automatic disease detection and classification system is significantly explored in precision agriculture. In recent times, research workers have investigated numerous cultures leveraging dissimilar parts of a plant. This article develops a new Deep Learning based Automated Plant Disease Detection and Classification (DL-APDDC) Model for Precision Agriculture. The presented DL-APDDC algorithm concentrates on the recognition and classification of plant diseases in leaf and fruit regions. In the initial stage, the leaf and fruit regions are extracted by the use of U2Net based background removal. Next, the Adam optimizer with SqueezeNet model is exploited as feature extractor and the hyperparameters are tuned by Adam optimizer. Finally, the extreme gradient boosting (XGBoost) classifier performs classification of plant diseases. The experimental validation of the DL-APDDC technique is tested on benchmark plant disease dataset. The simulation values indicated the enhanced outcomes of the DL-APDDC approach over other models.
Related Topics
Concepts
Precision agriculture
Extractor
Artificial intelligence
Classifier (UML)
Plant disease
Machine learning
Computer science
Agriculture
Deep learning
Boosting (machine learning)
Gradient boosting
Benchmark (surveying)
Random forest
Agricultural engineering
Biotechnology
Engineering
Cartography
Biology
Geography
Process engineering
Ecology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-2263078/v1
- https://www.researchsquare.com/article/rs-2263078/latest.pdf
- OA Status
- green
- Cited By
- 13
- References
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313589487
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4313589487Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-2263078/v1Digital Object Identifier
- Title
-
Deep Learning based Automated Disease Detection and Classification Model for Precision AgricultureWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-04Full publication date if available
- Authors
-
A. Pavithra, G. Kalpana, T. VigneswaranList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-2263078/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-2263078/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-2263078/latest.pdfDirect OA link when available
- Concepts
-
Precision agriculture, Extractor, Artificial intelligence, Classifier (UML), Plant disease, Machine learning, Computer science, Agriculture, Deep learning, Boosting (machine learning), Gradient boosting, Benchmark (surveying), Random forest, Agricultural engineering, Biotechnology, Engineering, Cartography, Biology, Geography, Process engineering, EcologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 5, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
14Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.a | 29, 46, 101, 106 |
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| abstract_inverted_index.as | 28, 168 |
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| abstract_inverted_index.in | 40, 60, 71, 85, 135 |
| abstract_inverted_index.is | 82, 166, 198 |
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| abstract_inverted_index.on | 127, 200 |
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| abstract_inverted_index.The | 122, 191, 205 |
| abstract_inverted_index.and | 3, 13, 18, 37, 42, 49, 52, 63, 68, 79, 115, 130, 137, 146, 171 |
| abstract_inverted_index.are | 6, 149, 174 |
| abstract_inverted_index.can | 25 |
| abstract_inverted_index.for | 15, 119 |
| abstract_inverted_index.new | 107 |
| abstract_inverted_index.the | 16, 35, 64, 128, 141, 144, 152, 160, 172, 180, 195, 209, 213 |
| abstract_inverted_index.use | 153 |
| abstract_inverted_index.Adam | 161, 177 |
| abstract_inverted_index.Deep | 108 |
| abstract_inverted_index.This | 103 |
| abstract_inverted_index.crop | 30, 43, 72 |
| abstract_inverted_index.have | 93 |
| abstract_inverted_index.leaf | 136, 145 |
| abstract_inverted_index.over | 216 |
| abstract_inverted_index.soil | 41 |
| abstract_inverted_index.were | 57 |
| abstract_inverted_index.with | 10, 163 |
| abstract_inverted_index.Model | 118 |
| abstract_inverted_index.Next, | 159 |
| abstract_inverted_index.Plant | 1, 112 |
| abstract_inverted_index.U2Net | 155 |
| abstract_inverted_index.based | 110, 156 |
| abstract_inverted_index.early | 50 |
| abstract_inverted_index.fruit | 138, 147 |
| abstract_inverted_index.major | 58 |
| abstract_inverted_index.model | 165 |
| abstract_inverted_index.other | 217 |
| abstract_inverted_index.parts | 99 |
| abstract_inverted_index.plant | 21, 55, 61, 133, 189, 202 |
| abstract_inverted_index.tuned | 175 |
| abstract_inverted_index.field. | 47 |
| abstract_inverted_index.fields | 9 |
| abstract_inverted_index.losses | 70 |
| abstract_inverted_index.method | 32 |
| abstract_inverted_index.plant. | 102 |
| abstract_inverted_index.recent | 89 |
| abstract_inverted_index.stage, | 143 |
| abstract_inverted_index.system | 81 |
| abstract_inverted_index.tested | 199 |
| abstract_inverted_index.times, | 90 |
| abstract_inverted_index.values | 207 |
| abstract_inverted_index.within | 45 |
| abstract_inverted_index.yield. | 73 |
| abstract_inverted_index.Disease | 113 |
| abstract_inverted_index.article | 104 |
| abstract_inverted_index.demands | 14 |
| abstract_inverted_index.disease | 77, 203 |
| abstract_inverted_index.extreme | 181 |
| abstract_inverted_index.factors | 44, 59 |
| abstract_inverted_index.feature | 169 |
| abstract_inverted_index.initial | 142 |
| abstract_inverted_index.models. | 218 |
| abstract_inverted_index.regions | 148 |
| abstract_inverted_index.related | 33 |
| abstract_inverted_index.spatial | 36 |
| abstract_inverted_index.workers | 92 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Accurate | 48 |
| abstract_inverted_index.DL-APDDC | 124, 196, 214 |
| abstract_inverted_index.Finally, | 179 |
| abstract_inverted_index.Learning | 109 |
| abstract_inverted_index.approach | 215 |
| abstract_inverted_index.boosting | 183 |
| abstract_inverted_index.cultures | 96 |
| abstract_inverted_index.dataset. | 204 |
| abstract_inverted_index.develops | 105 |
| abstract_inverted_index.disease. | 22 |
| abstract_inverted_index.diseases | 56, 134 |
| abstract_inverted_index.enhanced | 210 |
| abstract_inverted_index.explored | 84 |
| abstract_inverted_index.gradient | 182 |
| abstract_inverted_index.numerous | 95 |
| abstract_inverted_index.outcomes | 211 |
| abstract_inverted_index.performs | 186 |
| abstract_inverted_index.referred | 27 |
| abstract_inverted_index.regions. | 139 |
| abstract_inverted_index.removal. | 158 |
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| abstract_inverted_index.temporal | 38 |
| abstract_inverted_index.(XGBoost) | 184 |
| abstract_inverted_index.Automated | 111 |
| abstract_inverted_index.Detection | 114 |
| abstract_inverted_index.Precision | 4, 23, 120 |
| abstract_inverted_index.algorithm | 125 |
| abstract_inverted_index.automatic | 76 |
| abstract_inverted_index.benchmark | 201 |
| abstract_inverted_index.detection | 17, 53, 78 |
| abstract_inverted_index.diagnosis | 19, 51 |
| abstract_inverted_index.diseases. | 190 |
| abstract_inverted_index.exploited | 167 |
| abstract_inverted_index.extracted | 150 |
| abstract_inverted_index.extractor | 170 |
| abstract_inverted_index.indicated | 208 |
| abstract_inverted_index.optimizer | 162 |
| abstract_inverted_index.precision | 86 |
| abstract_inverted_index.presented | 123 |
| abstract_inverted_index.reduction | 65 |
| abstract_inverted_index.technique | 197 |
| abstract_inverted_index.(DL-APDDC) | 117 |
| abstract_inverted_index.SqueezeNet | 164 |
| abstract_inverted_index.background | 157 |
| abstract_inverted_index.challenges | 12 |
| abstract_inverted_index.classifier | 185 |
| abstract_inverted_index.dissimilar | 98 |
| abstract_inverted_index.leveraging | 97 |
| abstract_inverted_index.management | 31 |
| abstract_inverted_index.optimizer. | 178 |
| abstract_inverted_index.production | 62 |
| abstract_inverted_index.simulation | 206 |
| abstract_inverted_index.validation | 193 |
| abstract_inverted_index.Advancement | 74 |
| abstract_inverted_index.agriculture | 5, 24 |
| abstract_inverted_index.phenotyping | 2 |
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| abstract_inverted_index.variability | 39 |
| abstract_inverted_index.Agriculture. | 121 |
| abstract_inverted_index.agriculture. | 87 |
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| abstract_inverted_index.investigated | 94 |
| abstract_inverted_index.quantitative | 67 |
| abstract_inverted_index.significantly | 83 |
| abstract_inverted_index.Classification | 116 |
| abstract_inverted_index.classification | 80, 131, 187 |
| abstract_inverted_index.hyperparameters | 173 |
| abstract_inverted_index.information-and | 7 |
| abstract_inverted_index.technology-oriented | 8 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.7799999713897705 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.95293835 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |