A Multi-factor based Grading Evaluations for Pasture : A Fuzzy Data Fusion Approach

Recent advancements in the Internet of Things (IoT) have opened new possibilities for digital agriculture, significantly impacting the animal husbandry economy and grassland ecosystem stability. Traditional grassland evaluation methods are often time-consuming, labor-intensive, and prone to inaccuracies. To address these challenges, we propose a novel framework that employs a fuzzy data fusion approach for grassland evaluation. This method considers multiple factors—temperature, humidity, light, air pressure, and the Normalized Difference Vegetation Index (NDVI)—with weights determined through the Analytic Hierarchy Process (AHP). Our framework integrates an environmental monitoring system that leverages NB-IoT, sensor technology, and image processing techniques. Experimental results demonstrate that our method captures more comprehensive grassland information and overcomes the limitations of single-factor assessments, thereby enhancing the reliability and accuracy of grassland grade evaluations. By providing real-time, precise, and holistic assessments, the integration of AHP and fuzzy data fusion significantly improves the overall reliability of grassland evaluations.