Abstract
People frequently find it straightforward to identify insects that feed on cabbage plants based on the insects’ characteristics. However, challenges arise when employing a computer for precise bug categorization and identification. The implementation of Deep Learning (DL) utilizes the Convolutional Neural Network (CNN) technique, renowned for its effectiveness in categorizing and recognizing objects in digital photo data. The CNN algorithm delivers notable results in photo identification, simulating the human visual brain’s image recognition through artificial neural networks in the Python programming language. To enrich the user experience, frameworks like Keras and TensorFlow are employed in neural network development. Our research will use the CNN algorithm to determine insect species in cabbage images. Achieving high accuracy involves coupling the CNN algorithm with the VGGNet architectural model. Notably, utilizing an epoch value of 150 resulted in a 97.75% accuracy rate during the training model technique; however, the validation accuracy was slightly lower at 94.25%. The corresponding loss and validation rates were 0.0613% and 0.2945%, respectively. Moreover, based on the model (150 epochs), the testing outcomes in the study, involving 48 images beyond the dataset and incorporating additional information, resulted in an accuracy rate of 91.66%.
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Claims 5
Claim · model-written summary
The CNN with VGGNet reached 97.75% training accuracy at 150 epochs.
- Taxon
- Cabbage
Brassica oleracea var. capitatat0 - Treatment
- epoch value of 150e0
- Outcome
- accuracy ratee0
- Value
- 97.75%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“utilizing an epoch value of 150 resulted in a 97.75% accuracy rate during the training model technique”
t0“Cabbage Plant”
Claim · model-written summary
Validation accuracy was 94.25%, below training accuracy.
- Taxon
- CabbageBrassica oleracea var. capitatat0
- Outcome
- validation accuracye0
- Direction
- lowere0
- Value
- 94.25%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the validation accuracy was slightly lower at 94.25%”
t0“Cabbage Plant”
Claim · model-written summary
Training loss was 0.0613%.
- Taxon
- CabbageBrassica oleracea var. capitatat0
- Outcome
- losse0
- Value
- 0.0613%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The corresponding loss and validation rates were 0.0613% and 0.2945%, respectively”
t0“Cabbage Plant”
Claim · model-written summary
Validation loss was 0.2945%.
- Taxon
- CabbageBrassica oleracea var. capitatat0
- Outcome
- validation ratese0
- Value
- 0.2945%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The corresponding loss and validation rates were 0.0613% and 0.2945%, respectively”
t0“Cabbage Plant”
Claim · model-written summary
Testing on 48 additional images yielded 91.66% accuracy.
- Taxon
- CabbageBrassica oleracea var. capitatat0
- Outcome
- accuracy ratee0
- Value
- 91.66%e0
- Qualifier
- 48 images beyond the datasete0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“testing outcomes in the study, involving 48 images beyond the dataset and incorporating additional information, resulted in an accuracy rate of 91.66%”
t0“Cabbage Plant”