Multi-Label Data Fusion to Support Agricultural Vulnerability Assessments

• Main Authors: Ivan Dario Lopez, Apolinar Figueroa, Juan Carlos Corrales
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Climate vulnerability assessment; climate change; crop production; data processing; data fusion; machine learning
• Online Access: https://ieeexplore.ieee.org/document/9455370/

Identifying crop species and varieties adaptable to climate change impacts is one of the main aspects of climate vulnerability assessments. This estimation involves processing, integrating, and analyzing many information sources to provide accurate and timely responses. However, designing this evaluation, examine the information gathered, and reaching agreements among all stakeholders and experts, often requires considerable effort in time, money, and people. In this study, we propose a data fusion strategy to support climate vulnerability assessments by identifying the adaptability of crops in a territory in the short term. This strategy follows the Joint Directors of Laboratories’ data fusion model guidelines. It was evaluated and validated through a case study in Colombia’s upper Cauca river basin. For this purpose, we identified Climate, Soil, Water Quality, Productive Alliances, and Production as the most relevant data sources to be integrated, and using metrics such as Mean IR, SCUMBLE, TCS, among others, we evaluated the combined datasets according to their theoretical complexity. The adaptability of crops in a territory was addressed as a multi-label learning problem, assessing the performance of different multi-label classification and multi-view multi-label classification models with both test and actual data. Comparing the predicted crops with the actual ones, we obtained a 98% similarity without considering crop ranking using the Binary Relevance approach and the Random Forest and XGBoost algorithms. Using a more exhaustive test involving order, we obtained a maximum similarity of 67% applying Binary Relevance and Random Forest.