Abstract
Dragon fruit cultivation in Bangladesh is steadily growing. Farmers are increasingly interested in planting dragon fruit in cropland at the marginal level. The cost is little and the yield is simply achieved with great efficiency. With the rising output of the fruit, its cultivation is likewise steadily increasing. Consequently, a multitude of ailments affecting the dragon tree are emerging. A significant number of trees are perishing prior to the growth of dragon fruit. Hence, we offer a sophisticated deep-learning model capable of accurately detecting and classifying different diseases affecting dragon fruit. For this particular instance, we utilized 6000 photographs that were captured manually using our mobile device. We have identified three prevalent diseases that affect dragon fruits: Anthracnose, Root rot, and Stem canker. I have also acquired a recently harvested dragon fruit. Four distinct models have been selected in this instance. These options are Restnet50, VGG16, CNN, and VGG19. All the models yielded satisfactory results, however, VGG19 exhibited exceptional performance. The VGG19 model achieved an accuracy of 92%. This is an excellent outcome. The device successfully identified three diseases that impact dragon fruit trees and was also capable of detecting freshly harvested dragon fruit. The practical application of this technology will result in a transformation within the community of dragon farmers. Implementing this measure will enhance farmers’ crop production while reducing mortality rates among dragon trees.
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Claims 6
Claim · model-written summary
The photographed dragon fruit plants showed three prevalent diseases: anthracnose, root rot and stem canker.
- Taxon
- dragon fruits
Selenicereus undatuse0 - Outcome
- three prevalent diseasese0
- Value
- threee0
- Qualifier
- Anthracnose, Root rot, and Stem cankere0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“We have identified three prevalent diseases that affect dragon fruits: Anthracnose, Root rot, and Stem canker.”
Claim · model-written summary
All four image classification models yielded satisfactory disease detection results.
Verbatim
e0“All the models yielded satisfactory results, however, VGG19 exhibited exceptional performance.”
t0“Dragon Tree”
Claim · model-written summary
VGG19 performed exceptionally among the disease detection models.
Verbatim
e0“All the models yielded satisfactory results, however, VGG19 exhibited exceptional performance.”
t0“Dragon Tree”
Claim · model-written summary
The VGG19 model reached 92% disease classification accuracy.
Verbatim
e0“The VGG19 model achieved an accuracy of 92%.”
t0“Dragon Tree”
Claim · model-written summary
The device detected three dragon fruit diseases.
- Taxon
- dragon fruit treesSelenicereus undatuse0
- Outcome
- identified three diseasese0
- Value
- threee0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The device successfully identified three diseases that impact dragon fruit trees and was also capable of detecting freshly harvested dragon fruit.”
Claim · model-written summary
The device detected freshly harvested dragon fruit.
- Taxon
- dragon fruit treesSelenicereus undatuse0
- Outcome
- detecting freshly harvested dragon fruite0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The device successfully identified three diseases that impact dragon fruit trees and was also capable of detecting freshly harvested dragon fruit.”