Abstract
The efficient application of phytochemical products in agriculture is a complex issue that demands optimised sprayers and variable rate technologies, which rely on advanced sensing systems to address challenges such as overdosage and product losses. This work developed a system capable of processing different tree canopy parameters to support precision fruit farming and environmental protection using intelligent spraying methodologies. This system is based on a 2D light detection and ranging (LiDAR) sensor and a Global Navigation Satellite System (GNSS) receiver integrated into a sprayer driven by a tractor. The algorithm detects the canopy boundaries, allowing spray only in the presence of vegetation. The spray volume spared evaluates the system's performance compared to a Tree Row Volume (TRV) methodology. The results showed a 28% reduction in the overdosage of spraying product. The second step in this work was calculating and adjusting the amount of liquid to apply based on the tree volume. Considering this parameter, the saving obtained had an average value for the right and left rows of 78%. The volume of the trees was also monitored in a georeferenced manner with the creation of a occupation grid map. This map recorded the trajectory of the sprayer and the detected trees according to their volume.

Abstract
Nutrient quantification in hydroponic systems is essential. Reagent-less spectral quantification of nitrogen, phosphate and potassium faces challenges in accessing information-rich spectral signals and unscrambling interference from each constituent. Herein, we introduce information equivalence between spectra and sample composition, enabling extraction of consistent covariance to isolate nutrient-specific spectral information (N, P or K) in Hoagland nutrient solutions using orthogonal covariance modes. Chemometrics methods quantify nitrogen and potassium, but not phosphate. Orthogonal covariance modes, however, enable quantification of all three nutrients: nitrogen (N) with R = 0.9926 and standard error of 17.22 ppm, phosphate (P) with R = 0.9196 and standard error of 63.62 ppm, and potassium (K) with R = 0.9975 and standard error of 9.51 ppm. Including pH information significantly improves phosphate quantification (R = 0.9638, standard error: 43.16 ppm). Results demonstrate a direct relationship between spectra and Hoagland nutrient solution information, preserving NPK orthogonality and supporting orthogonal covariance modes. These modes enhance detection sensitivity by maximizing information of the constituent being quantified, while minimizing interferences from others. Orthogonal covariance modes predicted nitrogen (R = 0.9474, standard error: 29.95 ppm) accurately. Phosphate and potassium showed strong interference from contaminants, but most extrapolation samples were correctly diagnosed above the reference interval (83.26%). Despite potassium features outside the knowledge base, a significant correlation was obtained (R = 0.6751). Orthogonal covariance modes use unique N, P or K information for quantification, not spurious correlations due to fertilizer composition. This approach minimizes interferences during extrapolation to complex samples, a crucial step towards resilient nutrient management in hydroponics using spectroscopy.

Abstract
As the need for replacing fossil and other non-renewable energy sources with renewables becomes more critical and urgent, wind energy appears to be among the two or three best choices for the short and medium time frames. The dominance of wind energy as the first choice in many regions, leads to an increasing impact of wind power quality on the overall grid. Wind energy’s inherent intermittent nature, both in intensity and longevity, could be an impediment to its adoption unless utility operators have the tools to anticipate the impact and integrate wind resources seamlessly by increasing or reducing its contribution to the overall capacity of the grid. The wind forecasting science is well established and has been the subject of serious study in multiple fields such as fluid dynamics, statistical analysis and numerical simulation and modeling. With the renewed interest and dependence on wind as a major energy source, these efforts have increased exponentially. One of the areas that shows great promise in developing improved forecasting tools, is the category of “Biological Inspired Optimization Techniques. The study presented in this paper is the result of a study to survey and assess an array of forecasting models and algorithms. © MIR Labs, www.mirlabs.net/ijcisim/index.html

Abstract
Agricultural robots are exposed to adverse conditions reducing the components' lifetime. To reduce the number of inspection, repair and maintenance activities, we propose using audio-based systems to diagnose and detect anomalies in these robots. Audio-based systems are non-destructive/intrusive solutions. Besides, it provides a significant amount of data to diagnose problems and for a wiser scheduler for preventive activities. So, in this work, we installed two microphones in an agricultural robot with a mowing tool. Real audio data was collected with the robotic mowing tool operating in several conditions and stages. Besides, a Sound-based Anomalies Detector (SAD) is proposed and tested with this dataset. The SAD considers a short-time Fourier transform (STFT) computation stage connected to a Support Vector Machine (SVM) classifier. The results with the collected dataset showed an F1 score between 95% and 100% in detecting anomalies in a mowing robot operation.

Abstract
Developing ground robots for agriculture is a demanding task. Robots should be capable of performing tasks like spraying, harvesting, or monitoring. However, the absence of structure in the agricultural scenes challenges the implementation of localization and mapping algorithms. Thus, the research and development of localization techniques are essential to boost agricultural robotics. To address this issue, we propose an algorithm called VineSLAM suitable for localization and mapping in agriculture. This approach uses both point- and semiplane-features extracted from 3D LiDAR data to map the environment and localize the robot using a novel Particle Filter that considers both feature modalities. The numeric stability of the algorithm was tested using simulated data. The proposed methodology proved to be suitable to localize a robot using only three orthogonal semiplanes. Moreover, the entire VineSLAM pipeline was compared against a state-of-the-art approach considering three real-world experiments in a woody-crop vineyard. Results show that our approach can localize the robot with precision even in long and symmetric vineyard corridors outperforming the state-of-the-art algorithm in this context.