Deep Learning-Based Method for Irrigation Status Detection in Tomato Using Plant Leaves Article Swipe
Tej Bahadur Shahi
,
Chiranjibi Sitaula
,
Krishna Prasad Bhandari
,
Shobha Poudel
,
Rupesh Bhandari
,
Ravindra Mishra
,
Bharat Kumar Sharma
,
Bhogendra Mishra
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.22541/au.172322859.96503836/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.22541/au.172322859.96503836/v1
he impact of climate change, arguably the global warming and resulting drought, is one of the most escalating agricultural challenges affecting crop productivity. Therefore, effective water management is critical in agricultural practices.he impact of climate change, arguably the global warming and resulting drought, is one of the most escalating agricultural challenges affecting crop productivity. Therefore, effective water management is critical in agricultural practices.T The analysis of plant leaves presents an opportunity to gauge irrigation status through automated solutions to encourage broader adoption among farmers. Currently, there is a notable absence of AI methods in the literature for detecting tomato plant irrigation status through leaf analysis. Addressing this gap, we propose a novel end-to-end deep learning (DL)-based method, inspired by the ResNet-50 model. Our model trims unnecessary blocks and reduces larger kernels, significantly streamlining the model to better fit with the leaf image dataset related to the tomato irrigation status. We evaluate our method using a newly developed dataset and find outstanding performance (Precision: 99.05%, Recall: 99.01%, F1-score: 99.01%, mean-average F1: 98.98%, weighted-average F1: 98.95%, Kappa: 98.61%, accuracy: 98.90%) while comparing with the pre-trained DL models. Additionally, our model has fewer parameters and lower floatingpoint operations (FLOPs), enhancing its efficiency and suggesting its potential for more cost-effective and productive irrigation management practices. Impact Statement-The proposed photograph-based leaf water stress detection technology provides the water quantity required in tomato plants. This establishes a foundation for the next step: developing the model as an application (either in mobile device or in server) for effective management of limited water resources. This application will enable users to check water stress and apply precise amount of water either manually or through the automation of existing irrigation systems such as drip, sprinkler, subsurface, or other systems. This approach saves water while optimizing the productivity. Additionally, the technology is affordable and convenient to use for small scale farmers. The proposed technology has the potential to be applied to a range of crops and geographies, provided the model is adapted to fit the specific crops and topographies.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.22541/au.172322859.96503836/v1
- OA Status
- gold
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401451938
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401451938Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.22541/au.172322859.96503836/v1Digital Object Identifier
- Title
-
Deep Learning-Based Method for Irrigation Status Detection in Tomato Using Plant LeavesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-09Full publication date if available
- Authors
-
Tej Bahadur Shahi, Chiranjibi Sitaula, Krishna Prasad Bhandari, Shobha Poudel, Rupesh Bhandari, Ravindra Mishra, Bharat Kumar Sharma, Bhogendra MishraList of authors in order
- Landing page
-
https://doi.org/10.22541/au.172322859.96503836/v1Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.22541/au.172322859.96503836/v1Direct OA link when available
- Concepts
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Irrigation, Agricultural engineering, Agriculture, Productivity, Deficit irrigation, Environmental science, Irrigation management, Computer science, Mathematics, Agronomy, Engineering, Geography, Biology, Economics, Archaeology, MacroeconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.climate | 3, 34 |
| abstract_inverted_index.dataset | 142, 157 |
| abstract_inverted_index.limited | 253 |
| abstract_inverted_index.method, | 116 |
| abstract_inverted_index.methods | 92 |
| abstract_inverted_index.models. | 184 |
| abstract_inverted_index.notable | 88 |
| abstract_inverted_index.plants. | 227 |
| abstract_inverted_index.precise | 267 |
| abstract_inverted_index.propose | 109 |
| abstract_inverted_index.reduces | 128 |
| abstract_inverted_index.related | 143 |
| abstract_inverted_index.server) | 248 |
| abstract_inverted_index.status. | 148 |
| abstract_inverted_index.systems | 280 |
| abstract_inverted_index.through | 75, 102, 274 |
| abstract_inverted_index.warming | 8, 39 |
| abstract_inverted_index.(FLOPs), | 195 |
| abstract_inverted_index.adoption | 81 |
| abstract_inverted_index.analysis | 64 |
| abstract_inverted_index.approach | 290 |
| abstract_inverted_index.arguably | 5, 36 |
| abstract_inverted_index.critical | 28, 59 |
| abstract_inverted_index.drought, | 11, 42 |
| abstract_inverted_index.evaluate | 150 |
| abstract_inverted_index.existing | 278 |
| abstract_inverted_index.farmers. | 83, 309 |
| abstract_inverted_index.inspired | 117 |
| abstract_inverted_index.kernels, | 130 |
| abstract_inverted_index.learning | 114 |
| abstract_inverted_index.manually | 272 |
| abstract_inverted_index.presents | 68 |
| abstract_inverted_index.proposed | 213, 311 |
| abstract_inverted_index.provided | 326 |
| abstract_inverted_index.provides | 220 |
| abstract_inverted_index.quantity | 223 |
| abstract_inverted_index.required | 224 |
| abstract_inverted_index.specific | 334 |
| abstract_inverted_index.systems. | 288 |
| abstract_inverted_index.F1-score: | 166 |
| abstract_inverted_index.ResNet-50 | 120 |
| abstract_inverted_index.accuracy: | 176 |
| abstract_inverted_index.affecting | 20, 51 |
| abstract_inverted_index.analysis. | 104 |
| abstract_inverted_index.automated | 76 |
| abstract_inverted_index.comparing | 179 |
| abstract_inverted_index.detecting | 97 |
| abstract_inverted_index.detection | 218 |
| abstract_inverted_index.developed | 156 |
| abstract_inverted_index.effective | 24, 55, 250 |
| abstract_inverted_index.encourage | 79 |
| abstract_inverted_index.enhancing | 196 |
| abstract_inverted_index.potential | 202, 315 |
| abstract_inverted_index.resulting | 10, 41 |
| abstract_inverted_index.solutions | 77 |
| abstract_inverted_index.(DL)-based | 115 |
| abstract_inverted_index.Addressing | 105 |
| abstract_inverted_index.Currently, | 84 |
| abstract_inverted_index.Therefore, | 23, 54 |
| abstract_inverted_index.affordable | 301 |
| abstract_inverted_index.automation | 276 |
| abstract_inverted_index.challenges | 19, 50 |
| abstract_inverted_index.convenient | 303 |
| abstract_inverted_index.developing | 236 |
| abstract_inverted_index.efficiency | 198 |
| abstract_inverted_index.end-to-end | 112 |
| abstract_inverted_index.escalating | 17, 48 |
| abstract_inverted_index.foundation | 231 |
| abstract_inverted_index.irrigation | 73, 100, 147, 208, 279 |
| abstract_inverted_index.literature | 95 |
| abstract_inverted_index.management | 26, 57, 209, 251 |
| abstract_inverted_index.operations | 194 |
| abstract_inverted_index.optimizing | 294 |
| abstract_inverted_index.parameters | 190 |
| abstract_inverted_index.practices. | 210 |
| abstract_inverted_index.productive | 207 |
| abstract_inverted_index.resources. | 255 |
| abstract_inverted_index.sprinkler, | 284 |
| abstract_inverted_index.suggesting | 200 |
| abstract_inverted_index.technology | 219, 299, 312 |
| abstract_inverted_index.(Precision: | 162 |
| abstract_inverted_index.application | 241, 257 |
| abstract_inverted_index.establishes | 229 |
| abstract_inverted_index.opportunity | 70 |
| abstract_inverted_index.outstanding | 160 |
| abstract_inverted_index.performance | 161 |
| abstract_inverted_index.practices.T | 62 |
| abstract_inverted_index.pre-trained | 182 |
| abstract_inverted_index.subsurface, | 285 |
| abstract_inverted_index.unnecessary | 125 |
| abstract_inverted_index.agricultural | 18, 30, 49, 61 |
| abstract_inverted_index.geographies, | 325 |
| abstract_inverted_index.mean-average | 168 |
| abstract_inverted_index.practices.he | 31 |
| abstract_inverted_index.streamlining | 132 |
| abstract_inverted_index.Additionally, | 185, 297 |
| abstract_inverted_index.Statement-The | 212 |
| abstract_inverted_index.floatingpoint | 193 |
| abstract_inverted_index.productivity. | 22, 53, 296 |
| abstract_inverted_index.significantly | 131 |
| abstract_inverted_index.topographies. | 337 |
| abstract_inverted_index.cost-effective | 205 |
| abstract_inverted_index.photograph-based | 214 |
| abstract_inverted_index.weighted-average | 171 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.7699999809265137 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.79777208 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |