Multi-output Deep-Supervised Classifier Chains for Plant Pathology

TL;DR

This paper introduces Mo-DsCC, a novel deep learning model that chains predictions of plant species and diseases, improving accuracy in plant pathology classification tasks for smart agriculture.

Contribution

The paper proposes a new multi-output deep supervised classifier chain model that explicitly models relationships between plant species and diseases, outperforming existing methods.

Findings

Mo-DsCC achieves higher accuracy and F1-score than recent approaches.

Experimental results on Plant Village and PlantDoc datasets validate the model's effectiveness.

Chaining outputs improves prediction performance in plant disease classification.

Abstract

Plant leaf disease classification is an important task in smart agriculture which plays a critical role in sustainable production. Modern machine learning approaches have shown unprecedented potential in this classification task which offers an array of benefits including time saving and cost reduction. However, most recent approaches directly employ convolutional neural networks where the effect of the relationship between plant species and disease types on prediction performance is not properly studied. In this study, we proposed a new model named Multi-output Deep Supervised Classifier Chains (Mo-DsCC) which weaves the prediction of plant species and disease by chaining the output layers for the two labels. Mo-DsCC consists of three components: A modified VGG-16 network as the backbone, deep supervision training, and a stack of classification chains. To evaluate the advantages of our model, we perform intensive experiments on two benchmark datasets Plant Village and PlantDoc. Comparison to recent approaches, including multi-model, multi-label (Power-set), multi-output and multi-task, demonstrates that Mo-DsCC achieves better accuracy and F1-score. The empirical study in this paper shows that the application of Mo-DsCC could be a useful puzzle for smart agriculture to benefit farms and bring new ideas to industry and academia.