Visual Search Interactive Model for Artificial Intelligence Robotics Model forthe Agricultural Field Analysis
DOI:
https://doi.org/10.69996/jcai.2024021Keywords:
Auxiliary Field, Machine Learning, Clustering, k-means, Classification, Artificial IntelligenceAbstract
The Visual Search Interactive Model for Artificial Intelligence (AI) is designed to enhance the
efficiency and effectiveness of visual data analysis across various applications. By leveraging advanced
computer vision techniques and machine learning algorithms, this model enables AI systems to interpret
and analyze visual information in real-time, facilitating tasks such as object recognition, image
classification, and scene understanding. The interactive nature of the model allows users to engage with
the AI, refining searches and improving outcomes through iterative feedback. This paper introduces the
Auxiliary Clustering k-means Machine Learning (AC k-means ML) model, designed to enhance
agricultural efficiency through advanced data analysis and robotic integration. The study evaluates the
performance of the AC k-means ML model using a dataset comprising 1,950 samples, achieving an
overall accuracy of 91.5% and a precision of 89.2%. Key performance metrics such as F1 scores averaged
88.6%, with the highest individual cluster accuracy reaching 96% for Cluster 10. In addition to data
classification, the model facilitated the completion of 250 tasks with a remarkable success rate of 92%,
while maintaining an average task completion time of 15.4 minutes and an energy consumption of just 0.5
kWh per task. The implementation of the AC k-means ML model resulted in a 15% increase in crop yield
and substantial cost savings estimated at $2,000. With a user satisfaction score averaging 8.7 and an
adaptability score of 9.0, the findings indicate that the integration of machine learning and robotics
significantly optimizes agricultural processes, promoting sustainability and efficiency in farming practices.
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