Abstract: This research presents a detailed investigation of the manner in which state-of-the-art computer vision technologies are employed to assist agricultural landscape monitoring and vegetative area analysis. The approach that has been presented comprises of a multi-step procedure. Before applying complex feature extraction algorithms, the procedure begins with significant picture processing to assure the correctness of the data. Subsequently, diverse agricultural characteristics are separated using segmentation methods, and important traits are grouped together using clustering algorithms. In order to assess these solutions' performance and establish their resilience in aiding with appropriate farm management practices and boosting environmental monitoring abilities, rigorous testing is undertaken. This research greatly enhances the topic of eco-agriculture by presenting new ideas and viable solutions that employ computer infrastructures given by cloud computing.
Keywords: Farm monitoring; vegetation analysis; computer vision; image preprocessing; feature extraction; clustering; segmentation; Cloud computing; Remote sensing; Precision agriculture; Data analytics; Machine learning; Geospatial analysis; Satellite imagery; Environmental monitoring; IoT (Internet of Things); Big data; Digital agriculture; Sustainable farming; Land use classification; Crop health assessment; Spatial data processing.
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DOI:
10.17148/IJARCCE.2024.13473
