Abstract
Iron deficiency in plants causes iron chlorosis which frequently occurs in soils that are alkaline (pH greater than 7.0) and that contain lime. This deficiency turns affected plant leaves to yellow, or with brown edges in advanced stages. The goal of this research is to use the deep learning model to identify a nutrient deficiency in plant leaves and perform soil analysis to identify the cause of the deficiency. Two pre-trained deep learning models, Single Shot Detector (SSD) MobileNet v2 and EfficientDet D0, are used to complete this task via transfer learning. This research also contrasts the architecture and performance of the models at each stage and freezes the models for future use. Classification accuracy ranged from 93% to 98% for the SSD Mobilenet v2 model. Although this model took less time to process, its accuracy level was lower. While the EfficientDet D0 model required more processing time, it provided very high classification accuracy for the photos, ranging from 87% to 98.4%. These findings lead to the conclusion that both models are useful for real-time classifications, however, the EfficientDet D0 model may perform significantly better.
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The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 5
Claim · model-written summary
SSD MobileNet v2 classification accuracy ranged from 93% to 98%.
- Taxon
- PlantsViridiplantaet0
- Outcome
- Classification accuracye0
- Value
- 93% to 98%e0
Verbatim
e0“Classification accuracy ranged from 93% to 98% for the SSD Mobilenet v2 model”
t0“Plants”
Claim · model-written summary
SSD MobileNet v2 took less processing time but had lower accuracy.
- Taxon
- PlantsViridiplantaet0
- Outcome
- time to processe0
- Direction
- lesse0
- Comparator
- SSD Mobilenet v2 modele1
Verbatim
e0“this model took less time to process, its accuracy level was lower”
e1“SSD Mobilenet v2 model”
t0“Plants”
Claim · model-written summary
EfficientDet D0 accuracy ranged from 87% to 98.4%.
- Taxon
- PlantsViridiplantaet0
- Outcome
- classification accuracye0
- Value
- 87% to 98.4%e0
Verbatim
e0“EfficientDet D0 model required more processing time, it provided very high classification accuracy for the photos, ranging from 87% to 98.4%”
t0“Plants”
Claim · model-written summary
EfficientDet D0 required more processing time.
- Taxon
- PlantsViridiplantaet0
- Outcome
- processing timee0
- Direction
- moree0
- Comparator
- EfficientDet D0 modele0
Verbatim
e0“EfficientDet D0 model required more processing time”
t0“Plants”
Claim · model-written summary
The authors conclude that both models are useful for real-time classification and EfficientDet D0 may perform better.
- Taxon
- PlantsViridiplantaet0
- Outcome
- both models are useful for real-time classificationse0
Verbatim
e0“both models are useful for real-time classifications, however, the EfficientDet D0 model may perform significantly better”
t0“Plants”