Siamese Network-based Lightweight Framework for Tomato Leaf Disease Recognition
This addresses crop loss prevention for farmers by enabling timely disease recognition with a lightweight model, though it appears incremental as it builds on existing lightweight and Siamese network approaches.
The paper tackled tomato leaf disease recognition by proposing a Siamese network-based lightweight framework, achieving 96.97% accuracy on a PlantVillage subset and 95.48% on a Taiwan dataset, with only about 2.96 million parameters.
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.