The classification of hyperspectral images has become an essential task in agricultural analysis, as it assesses the quality, chemical composition, and overall health of produce. This study concentrates on the analysis of the hyperspectral images of apples to distinguish and classify pure apples and those subjected to fertilizers in different concentration levels. This classification is fundamental to ensuring food quality and optimizing fertilizer input and precision agriculture practices. The dataset did not initially satisfy requirements owing to the small sample size, which rendered it difficult to train any robust generalizable model. A conditional GAN variation (CGAN) has been proposed for data augmentation. This approach effectively handled class imbalance by generating high-quality, category-specific synthetic images that enriched the dataset with realistic and diverse samples. For classification, the ConvNeXt architecture was employed, integrated with the Simple Attention Module (SimAM) to enhance feature refinement and extraction. The SimAM module allowed the model to focus on the most relevant areas of hyperspectral images while suppressing unimportant information, leading to a highly refined representation of features. The proposed classification model achieved an impressive accuracy of 99.75%, showcasing its exceptional ability to distinguish between different categories of apples, including untreated ones and those with varying fertilizer concentrations. This high level of accuracy underscores the model’s robustness and reliability in agricultural image analysis.

Formation control plays a vital role in coordinating multi-agent systems and swarm robotics, enabling collaboration in applications such as autonomous vehicles, robotic swarms, and distributed sensing. This paper introduces the formation-control problem, highlights its challenges, and compares centralized and decentralized schemes. We review recent advances and analyze popular algorithms, then propose a hybrid framework that combines leader–follower tracking with an artificial potential field (APF) safety layer. In three-UAV tests, the followers cross paths and one encounters a static obstacle. We run multiple simulations across scenarios with obstacles and varying formations. Results show the hybrid controller maintains the required formation while avoiding inter-agent collisions. Using quantitative metrics, we find the leader–follower baseline achieves the lowest formation error but has the most safety violations, whereas APF greatly improves safety at the cost of higher error. The hybrid combines these strengths—delivering APF-level safety with lower error and negligible runtime overhead—providing a practical balance between precise formation keeping and robust collision avoidance.

Developing hyperspectral image classification models has long been hindered by problems regarding spectral redundancy and complexity, as well as the limitations of previously proposed transformer methods. We present AssociativeFormer, an architecture designed to address hyperspectral analysis by merging memory association and efficient spatial-spectral processing. New features of the architecture include: Sparse memory attention with Top-K Routing that reduces computational complexity from quadratic to linear scaling while retaining its ability to discriminate effectively. The versatile convolution tokenization allows the architecture to capture multi-scale spatial-spectral features and filters from dynamically generated convolution tokens. The associative memory provided by a Hopfield network allows real time storage and retrieval of patterns, while the Gompertz Linear Unit (GoLU) activation decreases impediments on the gradient flow to create a more useful representation of the features. The network's global workspace design preserves inter-layer information for independent flow and spatial-spectral dependency tracking. The AssociativeFormer model delivers state-of-the-art accuracy with linear complexity and competitive inference speed, providing a superior accuracy–efficiency trade-off compared to existing hyperspectral classification methods.

Energy efficiency and reliable communication are persistent challenges in underwater Internet of Things (UIoT) sensor networks due to limited resources and variable underwater acoustic conditions. Efficient routing is therefore needed to overcome these challenges and ensure reliable, long-term underwater monitoring. In this paper, an energy-efficient and scalable routing system for underwater networks is proposed, called the Grid-Based Fuzzy Logic Optimal Routing Protocol (GFLORP). The proposed protocol divides the underwater monitoring region into logical blocks and establishes a grid-based layout, enabling organized communication and localized coordination. A fuzzy inference system is used to select cluster heads considering residual energy, link quality, and node degree. To ensure reliable data delivery to the sink, it employs a multi-hop forwarding technique. The simulation results show that GFLORP outperforms the existing state-of-the-art methods in terms of network lifetime, energy consumption, packet delivery ratio, and end-to-end delay. The results show that GFLORP performs well in both sparse and dense deployment scenarios, making it a suitable protocol for dynamic and scalable underwater IoT ecosystems.