Selection of Robots in Precision Agriculture Using Multi-Criteria Decision-Making Methods

This article is about choosing the best robot for controlling weeds. It uses a special method that combines fuzzy CRITIC and fuzzy CoCoSO to make decisions based on many different factors. The researchers examined six different robots and considered ten criteria, including performance, cost, and environmental impact. They asked experts for their opinions, using a special scale that accounts for uncertainty and different perspectives. The fuzzy CRITIC method was used to determine the importance of each criterion, and then the fuzzy CoCoSO method was applied to rank the robots from best to worst. By considering all these factors, the study aims to identify the optimal robot for weed control. The decision-making process is complex, but this method helps make it clearer and more effective. The results indicate that the autonomous AI-powered robot with laser weed removal represents the best solution due to its superior performance in terms of precision, autonomy, and environmental acceptability. Validation of the results was conducted through comparison with other fuzzy multi-criteria methods (TOPSIS, MARCOS, ARAS, and SAW), revealing a high degree of consistency among the rankings. Additionally, sensitivity analysis based on variations in the weights of the most influential criteria confirmed the robustness of the model, with only limited changes observed in the middle positions of the ranking. The findings are helpful for determining how to choose the right agricultural robots and provide important guidance for decision-makers regarding the use of technology and the advancement of sustainable agriculture. This can support better choices in the digitalization and transformation of farming practices.