Using Artificial Intelligence in Education 4.0

Subsistence farmers and global food security depend on sufficient food production, which aligns with the UN's “Zero Hunger,” “Climate Action,” and “Responsible Consumption and Production” sustainable development goals. In addition to already available methods for early disease detection and classification facing overfitting and fine feature extraction complexities during the training process, how early signs of green attacks can be identified or classified remains uncertain. Most pests and disease symptoms are seen in plant leaves and fruits, yet their diagnosis by experts in the laboratory is expensive, tedious, labor-intensive, and time-consuming. Notably, how plant pests and diseases can be appropriately detected and timely prevented is a hotspot paradigm in smart, sustainable agriculture remains unknown. In recent years, deep transfer learning has demonstrated tremendous advances in the recognition accuracy of object detection and image classification systems since these frameworks utilize previously acquired knowledge to solve similar problems more effectively and quickly. Therefore, in this research, we introduce two plant disease detection (PDDNet) models of early fusion (AE) and the lead voting ensemble (LVE) integrated with nine pre-trained convolutional neural networks (CNNs) and fine-tuned by deep feature extraction for efficient plant disease identification and classification. The experiments were carried out on 15 classes of the popular PlantVillage dataset, which has 54,305 image samples of different plant disease species in 38 categories. Hyperparameter fine-tuning was done with popular pre-trained models, including DenseNet201, ResNet101, ResNet50, GoogleNet, AlexNet, ResNet18, EfficientNetB7, NASNetMobile, and ConvNeXtSmall. We test these CNNs on the stated plant disease detection and classification problem, both independently and as part of an ensemble. In the final phase, a logistic regression (LR) classifier is utilized to determine the performance of various CNN model combinations. A comparative analysis was also performed on classifiers, deep learning, the proposed model, and similar state-of-the-art studies. The experiments demonstrated that PDDNet-AE and PDDNet-LVE achieved 96.74% and 97.79%, respectively, compared to current CNNs when tested on several plant diseases, depicting its exceptional robustness and generalization capabilities and mitigating current concerns in plant disease detection and classification.

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Artificial Intelligence Algorithms as a Precursor for Personalized Learning and Education

Klinck-van der Merwe,

Goosen

2024

Advances in Computational Intelligence and Robotics

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The purpose of the study reported on in this chapter is to enhance the personalized learning experiences of school learners by applying a rigorous online Learning Analytics (LA) framework, while one of the objectives of the study include to provide a descriptive and structured framework for educators to follow, when embarking on a learning intervention. Against the background of real-world applications of Artificial Intelligence (AI) innovation, the main focus of the chapter will be on AI algorithms for personalized learning and education.

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Using Artificial Intelligence in Education 4.0

Cited by 3 publications

References 36 publications

Applying Systems Thinking for Designing Immersive Virtual Reality Learning Experiences in Education

Applying Systems Thinking for Designing Immersive Virtual Reality Learning Experiences in Education

Using transfer learning-based plant disease classification and detection for sustainable agriculture

Artificial Intelligence Algorithms as a Precursor for Personalized Learning and Education

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