  A disease that limits a plant from its maximal capacity is defined as plant
disease. From the perspective of agriculture, diagnosing plant disease is
crucial, as diseases often limit plants' production capacity. However, manual
approaches to recognize plant diseases are often temporal, challenging, and
time-consuming. Therefore, computerized recognition of plant diseases is highly
desired in the field of agricultural automation. Due to the recent improvement
of computer vision, identifying diseases using leaf images of a particular
plant has already been introduced. Nevertheless, the most introduced model can
only diagnose diseases of a specific plant. Hence, in this chapter, we
investigate an optimal plant disease identification model combining the
diagnosis of multiple plants. Despite relying on multi-class classification,
the model inherits a multilabel classification method to identify the plant and
the type of disease in parallel. For the experiment and evaluation, we
collected data from various online sources that included leaf images of six
plants, including tomato, potato, rice, corn, grape, and apple. In our
investigation, we implement numerous popular convolutional neural network (CNN)
architectures. The experimental results validate that the Xception and DenseNet
architectures perform better in multi-label plant disease classification tasks.
Through architectural investigation, we imply that skip connections, spatial
convolutions, and shorter hidden layer connectivity cause better results in
plant disease classification.
