Mulberry Leaf Disease Detection Using CNN-Based Smart Android Application
Abstract
Mulberry leaves serve as the primary food source
for Bombyx mori silkworms, crucial for silk thread
production. However, mulberry trees are highly
susceptible to diseases, spreading rapidly and
causing significant losses. Manual disease
identification across large farms is arduous and
time-consuming.
Leveraging computer vision for early disease
detection and classification can mitigate up to 90%
of production losses. This study collected leaves
from two regions of Bangladesh, categorized as
healthy, leaf rust-affected, and leaf spot-affected.
With a total of 1091 images, split into training (764),
testing (218), and validation (109) sets for 5-fold
cross-validation, preprocessing and augmentation
yielded 6,000 images, including synthetics. This
study
compares
ResNet50,
VGG19, and
MobileNetV3Small on a specific task following
architecture modifications. Four convolutional
layers with different output channels (512, 128, 64,
and 32) were added to baseline models. We assessed
how these architectural changes affected model
correctness, computing efficiency, and convergence
rates. Comparing three pretrained convolutional
neural networks (CNNs) - MobileNetV3Small,
ResNet50, and VGG19 - augmented with four
additional layers, the modified MobileNetV3Small
excelled in precision, recall, F1-score, and accuracy,
achieving notable results of 97.0%, 96.4%, 96.4%,
and 96.4%, respectively, across cross-validation
folds.
An efficient smartphone application employing the proposed model for mulberry leaf
disease recognition was developed. Overall, the
model outperformed existing State of the Art
(SOTA) approaches, showcasing its effectiveness in
disease identification. The interpretative Grad-CAM
visualization images match sericulture specialists'
assessments, validating the model's predictions.
These results imply that, this explainable AI (XAI)
approach with a modified deep learning architecture
can appropriately classify mulberry leaves
