Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

PND-Net: Plant Nutrition Deficiency and Disease Classification using Graph Convolutional Network

Asish Bera, Debotosh Bhattacharjee, Ondrej Krejcar

TL;DR

The proposed PND-Net effectively boosts the performances of automated health analysis of various plants in real and intricate field environments, implying PND-Net’s aptness for agricultural growth as well as human cancer classification.

Abstract

Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. The deep learning methods have proven its superior performances in the automated detection of plant diseases and nutrition deficiencies from visual symptoms in leaves. This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN), added upon a base convolutional neural network (CNN). Sometimes, a global feature descriptor might fail to capture the vital region of a diseased leaf, which causes inaccurate classification of disease. To address this issue, regional feature learning is crucial for a holistic feature aggregation. In this work, region-based feature summarization at multi-scales is explored using spatial pyramidal pooling for discriminative feature representation. A GCN is developed to capacitate learning of finer details for classifying plant diseases and insufficiency of nutrients. The proposed method, called Plant Nutrition Deficiency and Disease Network (PND-Net), is evaluated on two public datasets for nutrition deficiency, and two for disease classification using four CNNs. The best classification performances are: (a) 90.00% Banana and 90.54% Coffee nutrition deficiency; and (b) 96.18% Potato diseases and 84.30% on PlantDoc datasets using Xception backbone. Furthermore, additional experiments have been carried out for generalization, and the proposed method has achieved state-of-the-art performances on two public datasets, namely the Breast Cancer Histopathology Image Classification (BreakHis 40X: 95.50%, and BreakHis 100X: 96.79% accuracy) and Single cells in Pap smear images for cervical cancer classification (SIPaKMeD: 99.18% accuracy). Also, PND-Net achieves improved performances using five-fold cross validation.

PND-Net: Plant Nutrition Deficiency and Disease Classification using Graph Convolutional Network

TL;DR

The proposed PND-Net effectively boosts the performances of automated health analysis of various plants in real and intricate field environments, implying PND-Net’s aptness for agricultural growth as well as human cancer classification.

Abstract

Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. The deep learning methods have proven its superior performances in the automated detection of plant diseases and nutrition deficiencies from visual symptoms in leaves. This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN), added upon a base convolutional neural network (CNN). Sometimes, a global feature descriptor might fail to capture the vital region of a diseased leaf, which causes inaccurate classification of disease. To address this issue, regional feature learning is crucial for a holistic feature aggregation. In this work, region-based feature summarization at multi-scales is explored using spatial pyramidal pooling for discriminative feature representation. A GCN is developed to capacitate learning of finer details for classifying plant diseases and insufficiency of nutrients. The proposed method, called Plant Nutrition Deficiency and Disease Network (PND-Net), is evaluated on two public datasets for nutrition deficiency, and two for disease classification using four CNNs. The best classification performances are: (a) 90.00% Banana and 90.54% Coffee nutrition deficiency; and (b) 96.18% Potato diseases and 84.30% on PlantDoc datasets using Xception backbone. Furthermore, additional experiments have been carried out for generalization, and the proposed method has achieved state-of-the-art performances on two public datasets, namely the Breast Cancer Histopathology Image Classification (BreakHis 40X: 95.50%, and BreakHis 100X: 96.79% accuracy) and Single cells in Pap smear images for cervical cancer classification (SIPaKMeD: 99.18% accuracy). Also, PND-Net achieves improved performances using five-fold cross validation.

Paper Structure

This paper contains 22 sections, 8 equations, 12 figures, 13 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Methods on Plant Nutrition Deficiencies
  4. Approaches on Plant Diseases
  5. Methods on Graph Convolutional Networks (GCN)
  6. Proposed Method
  7. Background of Graph Convolutional Network (GCN)
  8. Convolutional Feature Representation
  9. Spatial Pyramid Pooling (SPP)
  10. Graph Convolutional Network (GCN)
  11. Classification Module
  12. Results and Performance Analysis
  13. Implementation Details
  14. Summary of Convolutional Network Architectures
  15. Data Pre-processing and Data Splitting Techniques
  16. ...and 7 more sections

Figures (12)

  • Figure 1: Proposed GCN-based method, PND-Net for visual classification of plant disease and nutrition inadequacy.
  • Figure 2: Sample images of banana dataset showing the nutrition deficiency of iron, calcium, and magnesium.
  • Figure 3: Sample images of Coffee nutrition deficiency of boron, manganese, and nitrogen.
  • Figure 4: Sample images of potato diseases infected by bacteria, pest, and Nematodes.
  • Figure 5: Sample images of infected leaves of soybean, tomato, and bell pepper from the PlantDoc dataset.
  • ...and 7 more figures