Abstract:
Bean plant is one of the most valuable cash crops worldwide. However, it is susceptible to serious diseases hindering its production and therefore, early detection and diagnosis is crucial. This study used a dataset contain- ing healthy and infected bean leaf images. It had 1035, 133 and 128 images on train, validation and test sets respectively. Image data augmentation was performed on the dataset to artificially increase the training set, by creatingdifferent versions of leaves. This was done to expose the classier to a massive
amount of data during training and ensure model robustness even in case of
leaf angle/ light variations. The study adopted transfer learning approach using
pre-trained MobileNet model and obtained 4 models from its architecture;
MobileNet model without/ with data augmentation and fine-tuned MobileNet
model without/ with data augmentation. The models were com- pared and
fine-tuned MobileNet model with data augmentation produced the best
generalization accuracy of 95% on validation and 94% on test sets. To validate
the results obtained from the best classifier, sensitivity analysis was performed
by i. training our best model on 10 random samples from the data. Onesample Student T - Test was used to examine the statistical difference between
sample mean accuracy and our known best ac- curacy. At 5% level of
significance, this difference was found to be statistically insignificant. ii.
Training our best classier using different random seeds and hyper-parameters
(learning rates, mini-batch sizes & epochs). Similarly, there was a small
difference in accuracies obtained, proving model robust- ness. A 95%
confidence interval was constructed to provide a range of values within which
our overall mean could lie. The interval was [94.94, 96.19], im- plying that
there was a 95% chance that the confidence interval contained the true overall
mean accuracy. The robust network recorded a very low misclassification
count of 7 on validation set and 8 on test set. Model interpretability results
revealed that, there was a misclassification only when the model was not paying
attention to the leaf or due to odd cases like presence of a hole on the leaf
otherwise, there was a correct prediction. Therefore, towards precision
agriculture, the robust system developed can be successfully employed in
bean disease diagnosis even in case of leaf angle/ light variations.
