A more accurate crop yield estimation is essential for optimizing agricultural productivity and resource management. Traditional machine learning models, such as linear regression and convolutional neural networks (CNNs), often struggle to integrate multimodal data sources effectively, limiting their predictive accuracy. In this study, we propose the AgriTransformer model, a transformer-based model that enhances crop yield prediction by leveraging attention mechanisms for multimodal data fusion. The AgriTransformer model incorporates tabular agricultural data and vegetation indices (VI), allowing dynamic feature interaction and improved interpretability. Experimental results have demonstrated that AgriTransformer significantly outperforms conventional approaches, achieving an R² of 0.919, compared to 0.884 for the best-performing linear regression model. The findings highlight the importance of structured tabular data in yield estimation, while VI serves as a complementary feature that increases the prediction capability and confidence. This study highlights the potential of transformer-based architectures in precision agriculture, offering a scalable and adaptable framework for crop yield forecasting. The AgriTransformer model enhances predictive accuracy and generalization across diverse agricultural conditions by prioritizing relevant features through attention mechanisms.

In recent years testing autonomous vehicles on public roads has become a reality. However, before having autonomous vehicles completely accepted on the roads, they have to demonstrate safe operation and reliable interaction with other traffic participants. Furthermore, in real situations and long term operation, there is always the possibility that diverse components may fail. This paper deals with possible sensor faults by defining a federated sensor data fusion architecture. The proposed architecture is designed to detect obstacles in an autonomous vehicle's environment while detecting a faulty sensor using SVM models for fault detection and diagnosis. Experimental results using sensor information from the KITTI dataset confirm the feasibility of the proposed architecture to detect soft and hard faults from a particular sensor.

Road driving environments are complex, unstructured and highly changeable. A safe driving is, thus, becoming quite challenging task, in particular from the view point of development and deployment of autonomous vehicles-based urban transport systems. In that context, the reliable perception appears as a one of the main enabling strategies in developing safe autonomous driving. Currently, many autonomous vehicles are being tested on public roads with the objective of demonstrating the capability of operating in real world situations. A big effort has been focused towards creating fault-free autonomous vehicles. Nevertheless, fault tolerant perception for autonomous vehicles still needs to be further developed in order to create autonomous vehicles capable of driving under real road traffic conditions since on-board vehicle sensors may fail due to bad calibration, erroneous readings, physical or electrical failures, etc. A multi-sensor based vehicle architecture is a logical response to this issue. While the multi-sensor concept often relates to the strategy of using a variety of sensor types, this research has been focused on to the case when all sensors are vision sensors, either identical or different from each other. This paper proposes a Fault Tolerant Perception paradigm that deals with possible sensor faults by defining the Federated Data Fusion Architecture designed to detect a faulty sensor and reduce its impact on to the safe autonomous driving. The proposed architecture minimises the influence of faulty data allowing the system to enter in a tolerated error state, where a recovery action can be performed to avoid failures. The developed architecture was then adapted towards meeting requirements of the KITTI Vision Benchmark Suite. Experimental results demonstrated the feasibility of the developed fault tolerant perception paradigm to successfully detect early faulty data from a singular sensor and to minimise the influence of that faulty sensor in the fusion process.

Crop monitoring and diagnosis are crucial for efficient agricultural production, and unmanned aerial vehicle (UAV) Remote Sensing can assist in achieving this goal. This article offers an automated Systematic Literature Review (SLR) of UAV Remote Sensing for crop monitoring and diagnosis. This review analyzes the primary scientific applications and trends in this area using Deep Learning techniques to automatically select relevant articles and validate them through full reading. The SLR collected over 800 papers, of which 64 met the selection process. The articles selected by Deep Learning classifiers were successfully cataloged with high accuracy in pre-selecting articles for review. F1 scores of 93% were achieved in tests with unpublished examples for the classifier model. The review of the 64 primary studies reported a peak in UAV Remote Sensing applications in 2020, attributed to the increasing diffusion of precision farming practices with technological equipment. The UAV Remote Sensing application objectives included crop monitoring, pest and disease detection, yield prediction, and plant nutrition. Artificial Intelligence, particularly Machine Learning and Deep Learning, are widely used for UAV Remote Sensing analysis. The NDVI is the most applied vegetation index for crop condition assessment and monitoring. The proposed solution for automating the literature selection process for precision agriculture-related scientific articles can be used in other areas that require extensive literature review.

Many robust sensor fusion strategies have been developed in order to reliably detect the surrounding environments of an autonomous vehicle. However, in real situations there is always the possibility that sensors or other components may fail. Thus, internal modules and sensors need to be monitored to ensure their proper function. This paper introduces a general view of a perception architecture designed to detect and classify obstacles in an autonomous vehicle's environment using a fault tolerant framework, whereas elaborates the object detection and local fusion modules proposed in order to achieve the modularity and real-time process required by the system.