The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. We now release in 2021 a new version of the Global Wheat Head Detection dataset, which is bigger, more diverse, and less noisy than the GWHD_2020 version.

/pdf/global-wheat-head-detection-2021-an-improved-dataset-for-3739g9mxlj.pdf

Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods.

Unmanned Aerial Vehicles (UAVs) are increasingly being used in a high-computation paradigm enabled with smart applications in the Beyond Fifth Generation (B5G) wireless communication networks. These networks have an avenue for generating a considerable amount of heterogeneous data by the expanding number of Internet of Things (IoT) devices in smart environments. However, storing and processing massive data with limited computational capability and energy availability at local nodes in the IoT network has been a significant difficulty, mainly when deploying Artificial Intelligence (AI) techniques to extract discriminatory information from the massive amount of data for different tasks.Therefore, Mobile Edge Computing (MEC) has evolved as a promising computing paradigm leveraged with efficient technology to improve the quality of services of edge devices and network performance better than cloud computing networks, addressing challenging problems of latency and computation-intensive offloading in a UAV-assisted framework. This paper provides a comprehensive review of intelligent UAV computing technology to enable 6G networks over smart environments. We highlight the utility of UAV computing and the critical role of Federated Learning (FL) in meeting the challenges related to energy, security, task offloading, and latency of IoT data in smart environments. We present the reader with an insight into UAV computing, advantages, applications, and challenges that can provide helpful guidance for future research.

/pdf/computing-in-the-sky-a-survey-on-intelligent-ubiquitous-2q49y31f.pdf

Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0/5.0

Deep learning for wheat ear segmentation and ear density measurement: From heading to maturity

Networked Tethered Flying Platforms (NTFPs) have gained growing interest in recent years due to their versatility and cost-efficiency. These aerial platforms are linked to ground stations via tethers that supply them with continuous power and data. The main advantages of NTFPs include endurance, broad coverage, and robust backhaul capacity, allowing them to carry various types of applications. This survey provides a comprehensive overview of NTFPs from a general and wireless communication perspective. We start with a general overview of this solution by reviewing all the existing types of NTFPs, including their components, characteristics, applications, advantages, and regulations. We also describe several case studies in various fields. Then, we investigate the role of NTFPs in wireless communications and their integration in Non-Terrestrial Networks (NTNs) alongside free-flying platforms and satellites in Sixth Generation (6G) cellular technology. We present NTFPs system models and emerging works on NTFPs performance. We show how 6G key technologies will be integrated into NTFPs and discuss how NTFPs will enable 6G use cases. The related channel models to NTFPs deployment are also covered. For completeness, we provide an economic analysis of the use of NTFPs. Finally, we discuss the challenges and open problems.

/pdf/unleashing-the-potential-of-networked-tethered-flying-nfofxor3.pdf

Unleashing the Potential of Networked Tethered Flying Platforms: Prospects, Challenges, and Applications

Identification of wheat tiller based on AlexNet-feature fusion

In this paper, we propose an object detection methodology applied to Global Wheat Head Detection (GWHD) Dataset. We have been through two major architectures of object detection which are Faster R-...

Wheat Head Detection using Deep, Semi-Supervised and Ensemble Learning

In this paper, we propose an original object detection methodology applied to Global Wheat Head Detection (GWHD) Dataset. We have been through two major architectures of object detection which are FasterRCNN and EfficientDet, in order to design a novel and robust wheat head detection model. We emphasize on optimizing the performance of our proposed final architectures. Furthermore, we have been through an extensive exploratory data analysis and adapted best data augmentation techniques to our context. We use semi supervised learning to boost previous supervised models of object detection. Moreover, we put much effort on ensemble to achieve higher performance. Finally we use specific post-processing techniques to optimize our wheat head detection results. Our results have been submitted to solve a research challenge launched on the GWHD Dataset which is led by nine research institutes from seven countries. Our proposed method was ranked within the top 6% in the above mentioned challenge.

An original framework for Wheat Head Detection using Deep, Semi-supervised and Ensemble Learning within Global Wheat Head Detection (GWHD) Dataset.

Connectivity in rural areas is one of the main challenges of communication networks. To overcome this challenge, a variety of solutions for different situations are required. Optimizing the current networking paradigms is therefore mandatory. The high costs of infrastructure and the low revenue of cell sites in rural areas compared with urban areas are especially unattractive for telecommunication operators. Therefore, space, air, and ground networks should all be optimized for achieving connectivity in rural areas. We highlight the latest works on rural connectivity, discuss the solutions for terrestrial networks, and study the potential benefits of nonterrestrial networks. Furthermore, we present an overview of artificial intelligence (AI) techniques for improving space, air, and ground networks, hence improving connectivity in rural areas. AI enables intelligent communications and can integrate space, air, and ground networks for rural connectivity. We discuss the rural connectivity challenges and highlight the latest projects and research and the empowerment of networks using AI. Finally, we discuss the potential positive impacts of providing connectivity to rural communities.

Bridging the Urban-Rural Connectivity Gap through Intelligent Space, Air, and Ground Networks

We investigate the problem of unconstrained combinatorial multi-armed bandits with full-bandit feedback and stochastic rewards for submodular maximization. Previous works investigate the same problem assuming a submodular and monotone reward function. In this work, we study a more general problem, i.e., when the reward function is not necessarily monotone, and the submodularity is assumed only in expectation. We propose Randomized Greedy Learning (RGL) algorithm and theoretically prove that it achieves a $\frac{1}{2}$-regret upper bound of $\tilde{\mathcal{O}}(n T^{\frac{2}{3}})$ for horizon $T$ and number of arms $n$. We also show in experiments that RGL empirically outperforms other full-bandit variants in submodular and non-submodular settings.

Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback

Federated learning is an emerging machine learning paradigm that enables devices to train collabo-ratively without exchanging their local data. The clients participating in the training process are a random subset selected from the pool of clients. The above procedure is called client selection which is an important area in federated learning as it highly impacts the convergence rate, learning efﬁciency, and generalization. In this work, we introduce client ﬁltering in federated learning (FilFL), a new approach to optimize client selection and training. FilFL ﬁrst ﬁlters the active clients by choosing a subset of them that maxi-mizes a speciﬁc objective function; then, a client selection method is applied to that subset. We provide a thorough analysis of its convergence in a heterogeneous setting. Empirical results demonstrate several beneﬁts to our approach, including improved learning efﬁciency, accelerated convergence, 2 - 3 × faster, and higher test accuracy, around 2 - 10 percentage points higher.

Fares Fourati

Papers

Wheat Head Detection using Deep, Semi-Supervised and Ensemble Learning

An original framework for Wheat Head Detection using Deep, Semi-supervised and Ensemble Learning within Global Wheat Head Detection (GWHD) Dataset.

Bridging the Urban-Rural Connectivity Gap through Intelligent Space, Air, and Ground Networks

Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback

FilFL: Accelerating Federated Learning via Client Filtering