Deep Learning for Plant Identification and Disease Classification from
  Leaf Images: Multi-prediction Approaches
Jianping Yao Son N. Tran Saurabh Garg Samantha Sawyer
Deep learning plays an important role in modern agriculture, especially in
plant pathology using leaf images where convolutional neural networks (CNN) are
attracting a lot of attention. While numerous reviews have explored the
applications of deep learning within this research domain, there remains a
notable absence of an empirical study to offer insightful comparisons due to
the employment of varied datasets in the evaluation. Furthermore, a majority of
these approaches tend to address the problem as a singular prediction task,
overlooking the multifaceted nature of predicting various aspects of plant
species and disease types. Lastly, there is an evident need for a more profound
consideration of the semantic relationships that underlie plant species and
disease types. In this paper, we start our study by surveying current deep
learning approaches for plant identification and disease classification. We
categorise the approaches into multi-model, multi-label, multi-output, and
multi-task, in which different backbone CNNs can be employed. Furthermore,
based on the survey of existing approaches in plant pathology and the study of
available approaches in machine learning, we propose a new model named
Generalised Stacking Multi-output CNN (GSMo-CNN). To investigate the
effectiveness of different backbone CNNs and learning approaches, we conduct an
intensive experiment on three benchmark datasets Plant Village, Plant Leaves,
and PlantDoc. The experimental results demonstrate that InceptionV3 can be a
good choice for a backbone CNN as its performance is better than AlexNet,
VGG16, ResNet101, EfficientNet, MobileNet, and a custom CNN developed by us.
Interestingly, empirical results support the hypothesis that using a single
model can be comparable or better than using two models. Finally, we show that
the proposed GSMo-CNN achieves state-of-the-art performance on three benchmark
datasets.