Enhancing Cucumber Leaf Curl Disease Detection in Smart Farming: A Hybrid Ensemble Learning Approach = 스마트 농업에서 오이 잎말림병 탐지 강화: 하이브리드 앙상블 학습 접근 방식

다국어 초록 (Multilingual Abstract)

ABSTRACT
The detection of plant diseases is crucial for ensuring high crop quality and yields. However, identifying diseases manually, particularly those visible on leaves, can be time-consuming and expensive. In this study, a hybrid model was used to build a cucumber curl leaf detection system. The main objective of the research was to find the best machine learning classification algorithm and identify the most accurate classifier. The proposed hybrid model combines Support Vector Machine (SVM), K-nearest neighbor (KNN), and Decision Tree (DT) classifiers as base classifiers to develop a hybrid model. Real-time generated datasets were used for prediction, where the Support Vector Machine achieved around 96.37% accuracy, the Decision Tree achieved 98%, and the K-nearest neighbor achieved an accuracy of 97%. Additionally, our proposed hybrid model achieved a classification accuracy of 98.4% on the same dataset. The model can be used for early-stage identification of cucumber curl leaf disease, reducing identification time with high accuracy.

목차 (Table of Contents)

• CHAPTER 1 1
• 1. INTRODUCTION 1
• 1.1. Introduction 1
• 1.2. The IoT architecture .3
• 1.2.1. RFID Tags . 3
• 1.2.2. RFID tag that is active . 3
• 1.2.3. RFID tag that is Passive . 4
• 1.2.4. Networks of Wireless Sensors (WSNs) 4
• 1.2.5. Sensing/Actuations 4
• 1.3. Application of IoT .4
• 1.3.1. Medical and Healthcare 4
• 1.3.2. Smart Home 5
• 1.3.3. Smart City 5
• 1.3.4. Smart Grid . 5
• 1.3.5. Smart Farming . 6
• 1.3.6. Smart Industry7
• 1.3.7. Smart Cities7
• 1.4. Problem Statement 8
• 1.5. Objectives8
• 1.6. Proposed Solution 8
• 1.7. Thesis Outlines 9
• CHAPTER 2: 11
• 2. REVIEW OF LITERATURE 11
• 2.1. Review of Literature 11
• 2.2. Architecture of Smart Agriculture 13
• 2.3. Intelligent Watering System 14
• 2.4. Strawberry Disease Detection System14
• 2.5. Prediction of Bitter Melon Crop Yield .14
• 2.6. Disease Prediction in Potato Farms. 15
• 2.7. Prediction System for Rice Disease 15
• 2.8. Event Prediction for Peach Frost. 15
• 2.9. Fungal Diseases Prediction System 16
• 2.10. Plan of Work 16
• 2.11. Heterogeneity16
• 2.12. Services' Quality (QoS) 17
• 2.13. Secure Environment.17
• 2.14. Communication Protocols.17
• 2.15. Transmission Management17
• 2.16. Availability and Reliability17
• 2.17. Data Storage, Processing and Visualization18
• 2.18. Scalability.18
• CHAPTER 3: 20
• 3. RESEARCH METHODOLOGY 20
• 3.1. Proposed IoT Bases System 20
• 3.2. The Proposed Architecture .22
• 3.3. Alert System 21
• 3.4. Actual Information Gathering and Monitoring .23
• 3.5. Research Work Plan 23
• 3.6. Implemented Prototype 24
• 3.7. System Architecture 25
• 3.8. DHT11 Sensor Node .25
• 3.9. Sensor of Soil Moisture. 26
• CHAPTER 4: 27
• 4. RESULTS AND DISCUSSION 27
• 4.1 K Nearest neighbor Classifier 27
• 4.2 Support Vector Machine (SVM) 28
• 4.3 Decision Tree .29
• 4.4. Proposed System .31
• 4.5. Hybrid Method for Model Selection Proposed System33
• 4.6. Implementation of the System35
• 4.7. Performance Analysis with the Proposed System.36
• CHAPTER 5: 39
• 5. CONCLUSION AND FUTURE WORK 39
• 5.1. conclusions .39
• 5.2 Future Work 39
• REFERENCES 40