Author:
Roberto Martinez Martinez Carlos
Abstract
This article introduces an advanced decision-support software aimed at enhancing mechanized agricultural tillage practices. It emphasizes the necessity of detailed planning before sowing to efficiently utilize resources and prevent soil deterioration. The developed algorithm, harnesses the power of compatibility matrices to analyze the complex interrelationships among various factors such as soil types, crop types, and machinery options. The study collected exhaustive data on tillage practices and uses this information to create compatibility matrices, enabling an intelligent algorithm to guide decision-making processes. The central feature of this software is its ability to generate a comprehensive tillage plan in natural language, serving as a detailed guide for farmers and other stakeholders. This algorithm is incorporated into a user-friendly web application, offering stakeholders an interactive platform for decision-making. The software is thoroughly validated by domain experts to ensure its reliability and accuracy.
Reference15 articles.
1. Azab YF, Abbas HH, Jalhoum ME, Farid IM, Abdelhameed AE, Mohamed ES. Soil erosion assessment in arid region: A case study in Wadi Naghamish, northwest coast, Egypt. The Egyptian Journal of Remote Sensing and Space Science. 2021;(3):1111-1118
2. Vowels MJ. Trying to outrun causality with machine learning: Limitations of model explainability techniques for identifying predictive variables. arXiv preprint arXiv:2202.09875. 2022. pp. 2-3
3. Pearl J. The limitations of opaque learning machines. Possible Minds. 2019;:13-19
4. Gajendran MK, Kabir IF, Purohit S, Ng EY. On the limitations of machine learning (ML) methodologies in predicting the wake characteristics of wind turbines. In: Renewable Energy Systems in Smart Grid: Select Proceedings of International Conference on Renewable and Clean Energy (ICRCE) 2022. Singapore: Springer Nature Singapore; 2022. pp. 15-23
5. Gatterbauer W, Dunne C, Riedewald M. Relational diagrams: A pattern-preserving diagrammatic representation of non-disjunctive relational queries. arXiv preprint arXiv:2203.07284. 2022. pp. 8-11