Intelligent Data Mining and Fusion Systems in Agriculture 1st Edition by Xanthoula Eirini Pantazi, Dimitrios Moshou, Dionysis Bochtis ISBN 9780128143926 0128143924

Chapter 1: Sensors in agriculture

Abstract

1.1 Milestones in agricultural automation and monitoring in agriculture

1.2 Sensors for soil analysis and characteristics

1.3 Yield sensing

1.4 Sensors for weed management

1.5 Sensors for disease detection and classification

Chapter 2: Artificial intelligence in agriculture

Abstract

2.1 Artificial intelligence and data mining basics

2.2 Artificial neural networks (ANNs)

2.3 Artificial neural networks applications in Biosystems engineering

2.4 Contribution of artificial intelligence in precision agriculture (economical, practical etc.)

2.5 One class classifiers

2.6 Support vector machines (SVMs)

2.7 One class-support vector machines (SVMs)

2.8 Active learning

2.9 Hierarchical self-organizing artificial neural networks

Chapter 3: Utilization of multisensors and data fusion in precision agriculture

Abstract

3.1 The necessity of multisensors network utilization for agriculture monitoring and control

3.2 Data fusion background

3.3 Data fusion applications in precision agriculture

Chapter 4: Tutorial I: Weed detection

Abstract

4.1 Introduction

4.2 Materials and methods

4.3 Explanation of experiments

4.4 Results and discussion for weed detection

Chapter 5: Tutorial II: Disease detection with fusion techniques

Abstract

5.1 Introduction

5.2 Experimental setup

5.3 Optical instrumentation

5.4 Fusion of optical sensing data

5.5 Results and discussion

Chapter 6: Tutorial III: Disease and nutrient stress detection

Abstract

6.1 Introduction

6.2 Materials and methods

6.3 Results and discussion for hierarchical self organizing classifiers

Chapter 7: Tutorial IV: Leaf disease recognition

Abstract

7.1 Introduction

7.2 State of the art

7.3 Materials and methods

7.4 Application of LBP in disease recognition of infected plants

7.5 Segmentation technique

7.6 Classification process and the features extraction process

Chapter 8: Tutorial V: Yield prediction

Abstract

8.1 Introduction

8.2 Materials and methods

8.3 Experimental setup

8.4 Results and discussion

Chapter 9: Tutorial VI: Postharvest phenotyping

Abstract

9.1 Introduction

9.2 Experimental setup

9.3 Fluorescence parameters

9.4 Data analysis

9.5 Results and discussion

Chapter 10: General overview of the proposed data mining and fusion techniques in agriculture

Abstract

10.1 Practical benefits for agriculture field

10.2 Economic benefits for agricultural production

10.3 Social benefits for agricultural production

10.4 Prospects for further development of the proposed sensing techniques in the near future

Index