Few-shot learning for biotic stress classification of coffee leaves

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In the last few years, deep neural networks have achieved promising results in several fields. However, one of the main limitations of these methods is the need for large-scale datasets to properly generalize. Few-shot learning methods emerged as an attempt to solve this shortcoming. Among the few-shot learning methods, there is a class of methods known as embedding learning or metric learning. These methods tackle the classification problem by learning to compare, needing fewer training data. One of the main problems in plant diseases and pests recognition is the lack of large public datasets available. Due to this difficulty, the field emerges as an intriguing application to evaluate the few-shot learning methods. The field is also relevant due to the social and economic importance of agriculture in several countries. In this work, datasets consisting of biotic stresses in coffee leaves are used as a case study to evaluate the performance of few-shot learning in classification and severity estimation tasks. We achieved competitive results compared with the ones reported in the literature in the classification task, with accuracy values close to 96%. Furthermore, we achieved superior results in the severity estimation task, obtaining 6.74% greater accuracy than the baseline.