PLDC-Net: A Domain-Specific Base Model for Plant Leaf Disease Classification Domain Adaptation Tasks

Plant diseases are the cause of heavy losses of crop production and, therefore, a big contributor to food shortages. Identifying these diseases as early as possible is important to limit the negative effects that these diseases have on the yields, as slow response time will lead to the spread of diseases and further loss. Traditionally, trained staff will go into the fields, multiple times during the growth period, and inspect the plants in samples through field disease monitoring. These traditional processes are time-consuming and costly, and can be error-prone, if the staff is not properly educated or if the staff simply makes mistakes due to oversight, for example. To aid farmers with the process of correctly identifying diseases, artificial intelligence deep learning methods have been employed in recent years. However, to train such deep learning models, one needs to obtain sufficiently large and high-quality datasets and a model architecture that is capable of extracting relevant features to accurately classify the plant leaves. Datasets are still a limitation in the field of plant leaf disease classification. As such, domain adaptation methods such as transfer learning are often employed to overcome this data shortage. However, in current research, these domain adaptation methods almost exclusively rely on ImageNet as the pretraining dataset, a dataset that is domain unrelated to plant leaf disease detection, and models are often left unmodified and un-optimized as a result. In this work, we propose the pretraining of an improved attention-based and SiLU-activated DenseNet201 architecture called PLDC-Net that is pretrained on a large-scale plant leaf disease dataset constructed by the authors to create a domain-specific base model for better domain adaptation to new plants and diseases, validating the improved results through transfer learning, fine-tuning, one-shot learning, and few-shot learning. PLDC-Net has managed up to just over 24% improvements in F1-Score over the baseline in domain adaptation results.