  To ensure global food security and the overall profit of stakeholders, the
importance of correctly detecting and classifying plant diseases is paramount.
In this connection, the emergence of deep learning-based image classification
has introduced a substantial number of solutions. However, the applicability of
these solutions in low-end devices requires fast, accurate, and computationally
inexpensive systems. This work proposes a lightweight transfer learning-based
approach for detecting diseases from tomato leaves. It utilizes an effective
preprocessing method to enhance the leaf images with illumination correction
for improved classification. Our system extracts features using a combined
model consisting of a pretrained MobileNetV2 architecture and a classifier
network for effective prediction. Traditional augmentation approaches are
replaced by runtime augmentation to avoid data leakage and address the class
imbalance issue. Evaluation on tomato leaf images from the PlantVillage dataset
shows that the proposed architecture achieves 99.30% accuracy with a model size
of 9.60MB and 4.87M floating-point operations, making it a suitable choice for
real-life applications in low-end devices. Our codes and models are available
at https://github.com/redwankarimsony/project-tomato.
