  Plant disease detection is an essential factor in increasing agricultural
production. Due to the difficulty of disease detection, farmers spray various
pesticides on their crops to protect them, causing great harm to crop growth
and food standards. Deep learning can offer critical aid in detecting such
diseases. However, it is highly inconvenient to collect a large volume of data
on all forms of the diseases afflicting a specific plant species. In this
paper, we propose a new metrics-based few-shot learning SSM net architecture,
which consists of stacked siamese and matching network components to address
the problem of disease detection in low data regimes. We demonstrated our
experiments on two datasets: mini-leaves diseases and sugarcane diseases
dataset. We have showcased that the SSM-Net approach can achieve better
decision boundaries with an accuracy of 92.7% on the mini-leaves dataset and
94.3% on the sugarcane dataset. The accuracy increased by ~10% and ~5%
respectively, compared to the widely used VGG16 transfer learning approach.
Furthermore, we attained F1 score of 0.90 using SSM Net on the sugarcane
dataset and 0.91 on the mini-leaves dataset. Our code implementation is
available on Github: https://github.com/shruti-jadon/PlantsDiseaseDetection.
