Fig 1.
Detecting cucumber diseases using machine learning.
Table 1.
Concept matrix of related work.
Fig 2.
Our model architecture.
Fig 3.
Methodical procedure.
Fig 4.
Data preprocessing.
Fig 5.
Exemplary images from the classes of the dataset.
Fig 6.
Confusion matrix of the novel approach.
Fig 7.
Training and validation loss of the novel approach during the training period.
Fig 8.
Training and validation accuracy of the novel approach during the training period.
Table 2.
Performance metrics for each class: novel approach / traditional approach.
Fig 9.
Exemplary LIME results.
Table 3.
Comparison of Most Relevant Existing Work and this Study.