Abstract
Plant diseases are a major problem for farmers around the world, reducing crop yields. The absence of expertise makes plant disease detection difficult and complicated. Plant disease detection is made easier by deep learning algorithms; however, they are computationally demanding and need huge training datasets. This research work proposes a novel Conv-7 DCNN model with modified ParNet attention layer to classify plant leaves into distinct categories with improved accuracy. Because of its architecture, the proposed network can identify leaf diseases with more accuracy by extracting the wider range of features from the images. The proposed Conv-7 DCNN model classifies the leaf diseases of three plants such as tomato, potato, and pepper-bell into fifteen categories. The CNN model is trained using publicly accessible Kaggle dataset, utilising image augmentation techniques. It is evident from the simulation results that the proposed model outperforms several pre-trained, and other trending deep learning models. Proposed model achieves 99.18% classification accuracy with an average precision of 99.17% and area under the curve (AUC) of 1, making this model highly effective in leaf diseases detection. Additionally, Conv-7 DCNN achieved high FPS of 112.49, low inference time of 18.34 s, and low GFLOPS of 13.98, making it suitable for real-time applications in smart agriculture systems.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by-nc-nd. Read at the source ↗
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 11
Claim · model-written summary
Conv-7 DCNN classifies tomato leaf diseases within a fifteen-category system.
- Taxon
- tomato
Solanum lycopersicume0 - Outcome
- classifies the leaf diseasese0
- Value
- fifteen categoriese0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The proposed Conv-7 DCNN model classifies the leaf diseases of three plants such as tomato, potato, and pepper-bell into fifteen categories.”
Claim · model-written summary
Conv-7 DCNN classifies potato leaf diseases within a fifteen-category system.
- Taxon
- potato
Solanum tuberosume0 - Outcome
- classifies the leaf diseasese0
- Value
- fifteen categoriese0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The proposed Conv-7 DCNN model classifies the leaf diseases of three plants such as tomato, potato, and pepper-bell into fifteen categories.”
Claim · model-written summary
Conv-7 DCNN classifies pepper-bell leaf diseases within a fifteen-category system.
Verbatim
e0“The proposed Conv-7 DCNN model classifies the leaf diseases of three plants such as tomato, potato, and pepper-bell into fifteen categories.”
Claim · model-written summary
The model outperformed several pretrained and trending deep learning models.
Not reportedTaxon
Verbatim
e0“the proposed model outperforms several pre-trained, and other trending deep learning models.”
Claim · model-written summary
Classification accuracy was 99.18%.
Not reportedTaxon
Verbatim
e0“Proposed model achieves 99.18% classification accuracy with an average precision of 99.17% and area under the curve (AUC) of 1”
Claim · model-written summary
Average precision was 99.17%.
Not reportedTaxon
Verbatim
e0“an average precision of 99.17% and area under the curve (AUC) of 1”
Claim · model-written summary
Area under the curve was 1.
Not reportedTaxon
Verbatim
e0“area under the curve (AUC) of 1”
Claim · model-written summary
Conv-7 DCNN achieved FPS of 112.49.
Not reportedTaxon
Verbatim
e0“Conv-7 DCNN achieved high FPS of 112.49, low inference time of 18.34 s, and low GFLOPS of 13.98”
Claim · model-written summary
Inference time was 18.34 s.
Not reportedTaxon
Verbatim
e0“low inference time of 18.34 s”
Claim · model-written summary
GFLOPS was 13.98.
Not reportedTaxon
Verbatim
e0“low GFLOPS of 13.98”
Claim · model-written summary
The model is suitable for real-time smart agriculture applications.
- Outcome
- real-time applications in smart agriculture systemse0
Not reportedTaxon
Verbatim
e0“making it suitable for real-time applications in smart agriculture systems.”