  Automatic tomato disease recognition from leaf images is vital to avoid crop
losses by applying control measures on time. Even though recent deep
learning-based tomato disease recognition methods with classical training
procedures showed promising recognition results, they demand large labelled
data and involve expensive training. The traditional deep learning models
proposed for tomato disease recognition also consume high memory and storage
because of a high number of parameters. While lightweight networks overcome
some of these issues to a certain extent, they continue to show low performance
and struggle to handle imbalanced data. In this paper, a novel Siamese
network-based lightweight framework is proposed for automatic tomato leaf
disease recognition. This framework achieves the highest accuracy of 96.97% on
the tomato subset obtained from the PlantVillage dataset and 95.48% on the
Taiwan tomato leaf disease dataset. Experimental results further confirm that
the proposed framework is effective with imbalanced and small data. The
backbone deep network integrated with this framework is lightweight with
approximately 2.9629 million trainable parameters, which is way lower than
existing lightweight deep networks.
