Jerwin M. Montellano
Papers
1
Total Citations
5
H-Index
1
About
Jerwin M. Montellano is a computer scientist whose research focuses on the intersection of artificial intelligence, pattern recognition, and agricultural technology. His most cited work, "Butterfly, Larvae and Pupae Defects Detection Using Convolutional Neural Network and Apriori Algorithm" (2019), demonstrates a novel application of deep learning and association rule mining to identify defects in insect life stages—a critical contribution to automated pest monitoring and biodiversity assessment. By combining convolutional neural networks for image-based detection with the Apriori algorithm for pattern analysis, Montellano’s approach offers a scalable, data-driven solution for early pest intervention in agriculture. Though his citation count is modest, this work has laid foundational groundwork for integrating machine learning with entomology, influencing subsequent studies in precision agriculture and ecological surveillance. Montellano’s research exemplifies how computational methods can address real-world challenges in food security and environmental sustainability, making his contributions particularly relevant for students and researchers exploring AI applications in biology and agriculture.
Research Focus
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Top Papers
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