One-Shot Learning Methodology For Plant Leaf Disease Identification

Plant diseases are a significant challenge in the agriculture and farming sector, leading to reduced crop yields and lower-quality produce. This issue can exacerbate poverty and food scarcity. Deep Learning has shown promise in computer vision tasks, including disease identification and detection in plants. However, the limited variety of datasets available for different plants and diseases is a major obstacle. One-Shot Learning (OSL), a subfield of meta-learning, can be used to address this challenge when few or limited datasets for specific plants are available. OSL has been employed in other domains such as face and symbol recognition, but its application in plant and disease detection remains relatively underexplored. In this study, we presented several methodologies that can produce an accurate OSL model for plants with limited datasets. OSL employs Siamese Networks and triplet loss functions to create a model that can identify an unknown object or category with just one sample. This approach has the potential to reduce processing time and energy consumption, lower costs associated with massive data storage, and provide farmers with a valuable tool to quickly identify and treat plant diseases. The proposed methodologies aim to significantly improve the accuracy of OSL models for plants with fewer datasets. Furthermore, the use of functions such as the triplet loss function and metric learning is critical for the OSL to perform effectively. Overall, the study demonstrates the potential of OSL as a powerful tool for plant disease detection and has important implications for improving crop yields and addressing food scarcity.