• Rural Digital Europe
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• 2013-2022close
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Previous works indicate that data fusion, compared to single data modelling can improve the assessment of soil attributes using spectroscopy. In this work, two different kinds of proximal soil sensing techniques i.e., mid-infrared (MIR) and X-ray fluorescence (XRF) spectroscopy were evaluated, for assessment of seven fertility attributes. These soil attributes include pH, organic carbon (OC), phosphorous (P), potassium (K), magnesium (Mg), calcium (Ca) and moisture contents (MC). Three kinds of spectra fusion (SF) (spectra concatenation) approaches of MIR and XRF spectra were compared, namely, spectra fusion-Partial least square (SF-PLS), spectra fusion-Sequential Orthogonalized Partial least square (SF-SOPLS) and spectra fusion-Variable Importance Projection-Sequential Orthogonalized Partial least square (SF-VIP-SOPLS). Furthermore, the performance of SF models was compared with the developed single sensor model (based on individual spectra of MIR and XRF). Compared with the results obtained from single sensor model, SF models showed improvement in the prediction performance for all studied attributes, except for OC, Mg, and K prediction. More specifically, the highest improvement was observed with SF-SOPLS model for pH [R2p = 0.90, root mean square error prediction (RMSEP) = 0.15, residual prediction deviation (RPD) = 3.30, and ratio of performance inter-quantile (RPIQ) = 3.59], successively followed by P (R2p = 0.91, RMSEP = 4.45 mg/100 g, RPD = 3.53, and RPIQ = 4.90), Ca (R2p = 0.92, RMSEP = 177.11 mg/100 g, RPD = 3.66, and RPIQ = 3.22) and MC (R2p = 0.80, RMSEP = 1.91%, RPD = 2.31, RPIQ = 2.62). Overall the study concluded that SF approach with SOPLS attained better performance over the traditional model developed with the single sensor spectra, hence, SF is recommended as the best SF method for improving the prediction accuracy of studied soil attributes. Moreover, the multi-sensor spectra fusion approach is not limited for only MIR and XRF data but in general can be extended for complementary information fusion in order to improve the model performance in precision agriculture (PA) applications.

Complex service robotics scenarios entail unpredictable task appearance both in space and time. This requires robots to continuously relocate and imposes a trade-off between motion costs and efficiency in task execution. In such scenarios, multi-robot systems and even swarms of robots can be exploited to service different areas in parallel. An efficient deployment needs to continuously determine the best allocation according to the actual service needs, while also taking relocation costs into account when such allocation must be modified. For large scale problems, centrally predicting optimal allocations and movement paths for each robot quickly becomes infeasible. Instead, decentralized solutions are needed that allow the robotic system to self-organize and adaptively respond to the task demands. In this paper, we propose a distributed and asynchronous approach to simultaneous task assignment and path planning for robot swarms, which combines a bio-inspired collective decision-making process for the allocation of robots to areas to be serviced, and a search-based path planning approach for the actual routing of robots towards tasks to be executed. Task allocation exploits a hierarchical representation of the workspace, supporting the robot deployment to the areas that mostly require service. We investigate four realistic environments of increasing complexity, where each task requires a robot to reach a location and work for a specific amount of time. The proposed approach improves over two different baseline algorithms in specific settings with statistical significance, while showing consistently good results overall. Moreover, the proposed solution is robust to limited communication and robot failures.