Abstract:
The accurate and efficient segmentation of plant diseases is the key to sustaining plant growth
quality and identifying disease severity. Unfortunately, many current plant disease
segmentation techniques frequently fall short of precisely and quickly identifying diseased
areas on plant leaves, more specifically when it comes to lightweight segmentation models
with the goal of achieving high-level accuracy. The proposed model within this study will be
using an improved version of DeepLabV3+ as the foundation for a deep-learning strategy that
is intended to quickly and precisely segment common leaf diseases in six different species of
plants. In order to modify and better the segmentation performance, the approach for this model
combines the CBAM-FF (Convolutional Block Attention Module Feature Fusion) module
which uses two analytical dimensions known as spatial attention and channel attention and
they are needed to create a sequential attention structure that moves from channel to space.
Moreover, the Lite R-ASPP (Lite Reduced Atrous Spatial Pyramid Pooling) attention module
has been utilized to enhance the MobileNetV3_large backbone network's feature extraction
performance for disease features. Furthermore, the impact of the optimizer, backbone network,
and learning rate on the DeepLabV3+ network model's performance will be examined. The
proposed model depicted an outstanding accuracy of 97.34% alongside an MIoU of 93.47%.
