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Deep Learning for Intelligent Systems: Challenges and Opportunities

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Special Issue Editors

Prof. Dr. Qing Li

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Guest Editor

Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Interests: multi-modal data management; data warehousing and mining; social media and web services; e-learning technologies

Dr. Wenqi Fan

E-Mail Website
Guest Editor

Department of Computing, the Hong Kong Polytechnic University, Kowloon, Hong Kong
Interests: recommender systems (RecSys); management information systems (MIS); large language models (LLMs); trustworthy AI; graph machine learning;

Special Issue Information

Dear Colleagues,

Deep learning has emerged as a powerful tool for developing intelligent systems across various domains, including computer vision, natural language processing, robotics, Internet of Things (IoT), smart cities, and healthcare, among others. This Special Issue aims to provide a comprehensive overview of the challenges and opportunities associated with applying deep learning in the context of intelligent systems.

The objective of this Special Issue is to foster a deeper understanding of the challenges faced by researchers and practitioners when utilizing deep learning techniques to build intelligent systems. We aim to explore the potential opportunities that deep learning offers in overcoming these challenges and advancing the field of intelligent systems. By collecting high-quality research papers, we intend to provide a platform for discussing novel methodologies, techniques, and applications in this rapidly evolving field.

This Special Issue aims to gather original research and review articles focusing on the recent advances, technologies, solutions, applications, and challenges in the field of deep learning for intelligent systems. Topics of interest include, but are not limited to, the following:

Deep learning for sensors;
Deep learning models and architectures for intelligent systems;
Deep learning for healthcare and medical diagnosis systems;
Deep learning for intelligent transportation systems;
Deep learning for smart cities and IoT applications;
Deep reinforcement learning for decision making in intelligent systems;
Deep learning on graphs;
Deep learning for recommender systems;
Deep learning on web and social media;
Explainable deep learning for transparent and interpretable intelligent systems;
Transfer learning and domain adaptation for intelligent systems;
Deep learning for multimodal data fusion in intelligent systems;
Deep learning for autonomous robotics and intelligent agents;
Deep learning for natural language understanding and generation in intelligent systems;
Ethical considerations and fairness in deep-learning-based intelligent systems;
Trustworthy deep learning techniques.

Prof. Dr. Qing Li
Dr. Wenqi Fan
Guest Editors

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Research

18 pages, 5506 KiB

Open AccessArticle

Optimizing Satellite Imagery Datasets for Enhanced Land/Water Segmentation

by Marco Scarpetta, Luisa De Palma, Attilio Di Nisio, Maurizio Spadavecchia, Paolo Affuso and Nicola Giaquinto

Sensors 2025, 25(6), 1793; https://doi.org/10.3390/s25061793 - 13 Mar 2025

Viewed by 497

Abstract

This paper presents an automated procedure for optimizing datasets used in land/water segmentation tasks with deep learning models. The proposed method employs the Normalized Difference Water Index (NDWI) with a variable threshold to automatically assess the quality of annotations associated with multispectral satellite images. By systematically identifying and excluding low-quality samples, the method enhances dataset quality and improves model performance. Experimental results on two different publicly available datasets—the SWED and SNOWED—demonstrate that deep learning models trained on optimized datasets outperform those trained on baseline datasets, achieving significant improvements in segmentation accuracy, with up to a 10% increase in mean intersection over union, despite a reduced dataset size. Therefore, the presented methodology is a promising scalable solution for improving the quality of datasets for environmental monitoring and other remote sensing applications. Full article

(This article belongs to the Special Issue Deep Learning for Intelligent Systems: Challenges and Opportunities)

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20 pages, 8826 KiB

Open AccessArticle

Coffee Leaf Rust Disease Detection and Implementation of an Edge Device for Pruning Infected Leaves via Deep Learning Algorithms

by Raka Thoriq Araaf, Arkar Minn and Tofael Ahamed

Sensors 2024, 24(24), 8018; https://doi.org/10.3390/s24248018 - 16 Dec 2024

Viewed by 1243

Abstract

Global warming and extreme climate conditions caused by unsuitable temperature and humidity lead to coffee leaf rust (Hemileia vastatrix) diseases in coffee plantations. Coffee leaf rust is a severe problem that reduces productivity. Currently, pesticide spraying is considered the most effective solution for mitigating coffee leaf rust. However, the application of pesticide spray is still not efficient for most farmers worldwide. In these cases, pruning the most infected leaves with leaf rust at coffee plantations is important to help pesticide spraying to be more efficient by creating a more targeted, accessible treatment. Therefore, detecting coffee leaf rust is important to support the decision on pruning infected leaves. The dataset was acquired from a coffee farm in Majalengka Regency, Indonesia. Only images with clearly visible spots of coffee leaf rust were selected. Data collection was performed via two devices, a digital mirrorless camera and a phone camera, to diversify the dataset and test it with different datasets. The dataset, comprising a total of 2024 images, was divided into three sets with a ratio of 70% for training (1417 images), 20% for validation (405 images), and 10% for testing (202 images). Images with leaves infected by coffee leaf rust were labeled via LabelImg^® with the label “CLR”. All labeled images were used to train the YOLOv5 and YOLOv8 algorithms through the convolutional neural network (CNN). The trained model was tested with a test dataset, a digital mirrorless camera image dataset (100 images), a phone camera dataset (100 images), and real-time detection with a coffee leaf rust image dataset. After the model was trained, coffee leaf rust was detected in each frame. The mean average precision (mAP) and recall for the trained YOLOv5 model were 69% and 63.4%, respectively. For YOLOv8, the mAP and recall were approximately 70.2% and 65.9%, respectively. To evaluate the performance of the two trained models in detecting coffee leaf rust on trees, 202 original images were used for testing with the best-trained weight from each model. Compared to YOLOv5, YOLOv8 demonstrated superior accuracy in detecting coffee leaf rust. With a mAP of 73.2%, YOLOv8 outperformed YOLOv5, which achieved a mAP of 70.5%. An edge device was utilized to deploy real-time detection of CLR with the best-trained model. The detection was successfully executed with high confidence in detecting CLR. The system was further integrated into pruning solutions for Arabica coffee farms. A pruning device was designed using Autodesk Fusion 360^® and fabricated for testing on a coffee plantation in Indonesia. Full article

(This article belongs to the Special Issue Deep Learning for Intelligent Systems: Challenges and Opportunities)

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