6 Toward a sustainable future: a computational intelligence fusion framework of color and darknet features for the classification of crop leaf diseases
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Irfan Haider
Abstract
In countries such as Pakistan and India, agriculture plays a key role in the national economy. However, diseases at the initial stage significantly impact the quality and quantity of food production. Therefore, it is essential to develop a computerized technique for the automated recognition crops leaf diseases at the early stage. In this chapter, we present a deep learning and color feature-based fused architecture for the classification of crop leaf diseases. In the proposed framework, the contrast of the original leaf images is enhanced in the initial step to improve the visualization of infected regions. After that, color and Darknet-53 features are extracted and fused using a serial approach. The extracted features are analyzed and refined using an optimization technique called Henry gas solubility optimization algorithm. The selected features are then passed to neural network classifiers for final classification performance. The experimental process was conducted on two datasets such as wheat and cotton that are collected from Kaggle database. In these datasets, the proposed framework achieved the accuracy of 94.6% and 95.3%, respectively. Detailed ablation studies and t-test were performed and the proposed framework improved the accuracy and precision rate.
Abstract
In countries such as Pakistan and India, agriculture plays a key role in the national economy. However, diseases at the initial stage significantly impact the quality and quantity of food production. Therefore, it is essential to develop a computerized technique for the automated recognition crops leaf diseases at the early stage. In this chapter, we present a deep learning and color feature-based fused architecture for the classification of crop leaf diseases. In the proposed framework, the contrast of the original leaf images is enhanced in the initial step to improve the visualization of infected regions. After that, color and Darknet-53 features are extracted and fused using a serial approach. The extracted features are analyzed and refined using an optimization technique called Henry gas solubility optimization algorithm. The selected features are then passed to neural network classifiers for final classification performance. The experimental process was conducted on two datasets such as wheat and cotton that are collected from Kaggle database. In these datasets, the proposed framework achieved the accuracy of 94.6% and 95.3%, respectively. Detailed ablation studies and t-test were performed and the proposed framework improved the accuracy and precision rate.
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- List of contributors IX
- 1 Leveraging computational intelligence and mathematical modeling for sustainable future in agriculture: a unified paradigm for recognizing tomato leaf diseases 1
- 2 Digital dawn: how immersive technologies are shaping a sustainable future 37
- 3 Sustainable intelligence: ethical issues in the evolution of intelligent systems 57
- 4 Energy sustainability and computational intelligence based routing protocols in WSN: an analytical survey 75
- 5 Harnessing the metaverse for healthcare innovation: exploring predictive analytics and AI-driven personalization 91
- 6 Toward a sustainable future: a computational intelligence fusion framework of color and darknet features for the classification of crop leaf diseases 109
- 7 Sustainable computing approaches for complex medical image analysis: a neurodiagnostic perspective 139
- 8 Sustainability with artificial intelligence: obstacles, opportunities, and research agenda 161
- 9 Smart solutions to a sustainable future 177
- 10 Ethical issues in intelligent systems for sustainability 195
- 11 CPS: cyber-physical system security for the Industrial Internet of Things in smart grid 215
- 12 Integrating mathematical computing and deep learning for efficient monkeypox skin lesion detection: A pathway to sustainable health solutions 253
- 13 Leveraging heuristics based on CK Metrics Suite for quality enhancement in sustainable quantum software development 271
- 14 Intelligent systems for fire management and sustainability 297
- 15 Transparent and sustainable AI for brain tumor detection: from conventional to hybrid models in predictive healthcare 323
- Index 349
- Mathematical Methods in the Digital Age
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- List of contributors IX
- 1 Leveraging computational intelligence and mathematical modeling for sustainable future in agriculture: a unified paradigm for recognizing tomato leaf diseases 1
- 2 Digital dawn: how immersive technologies are shaping a sustainable future 37
- 3 Sustainable intelligence: ethical issues in the evolution of intelligent systems 57
- 4 Energy sustainability and computational intelligence based routing protocols in WSN: an analytical survey 75
- 5 Harnessing the metaverse for healthcare innovation: exploring predictive analytics and AI-driven personalization 91
- 6 Toward a sustainable future: a computational intelligence fusion framework of color and darknet features for the classification of crop leaf diseases 109
- 7 Sustainable computing approaches for complex medical image analysis: a neurodiagnostic perspective 139
- 8 Sustainability with artificial intelligence: obstacles, opportunities, and research agenda 161
- 9 Smart solutions to a sustainable future 177
- 10 Ethical issues in intelligent systems for sustainability 195
- 11 CPS: cyber-physical system security for the Industrial Internet of Things in smart grid 215
- 12 Integrating mathematical computing and deep learning for efficient monkeypox skin lesion detection: A pathway to sustainable health solutions 253
- 13 Leveraging heuristics based on CK Metrics Suite for quality enhancement in sustainable quantum software development 271
- 14 Intelligent systems for fire management and sustainability 297
- 15 Transparent and sustainable AI for brain tumor detection: from conventional to hybrid models in predictive healthcare 323
- Index 349
- Mathematical Methods in the Digital Age
