Abstract
In the context of modern flower cultivation, this study leverages the power of Support Vector Machines (SVM) to revolutionize the production process. By deploying SVM technology, the study aims to optimize various stages of flower production, with a keen focus on the ever-popular ‘Explorer’ rose variety. The SVM model showcases an exceptional precision rate, achieving a remarkable 99.21% accuracy in classifying production stages. This precision is further validated through k-fold cross-validation, resulting in an impressive average precision of 99.34%. The successful implementation of SVM underscores its potential to not only enhance the quality and profitability of rose production but also serve as a stepping stone for prospective investigations into the seamless integration of other machine learning algorithms within the realm of smart farming.
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