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Optimising Cultivation Of High Value Crops Using Machine Learning And Deep Learning

Bhoomika H B, Ashitha V R, Chandrashekhar S R, Darshan K, Shravan H S

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Abstract

AbstractAreca nut (Areca catechu) and pomegranate (Punica granatum) are among the most economically important high value crops cultivated in tropical and semi-arid regions. Improving their production efficiency requires the adoption of modern, resource-efficient agricultural practices that enhance yield, fruit quality, and profitability. Areca nut growth depends largely on humid climatic conditions, well-drained soils, and effective nutrient and pest management, whereas pomegranate cultiva tion benefits from regulated irrigation, structured pruning, and balanced fertilization to achieve premium fruit standards. This study examines key strategies including high-density planting, drip irrigation, precision nutrient delivery, integrated pest and disease management, and improved post-harvest handling. The findings highlight that these approaches not only boost productivity but also support environmental sustainability, ultimately strengthening the economic resilience of farmers involved in high value crop cultivation.

Copyright

Copyright © 2025 Bhoomika H B, Ashitha V R, Chandrashekhar S R, Darshan K, Shravan H S. This is an open access article distributed under the Creative Commons Attribution License.

Paper Details
Paper ID: IJPREMS51200018181
ISSN: 2321-9653
Publisher: ijprems
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The International Journal of Progressive Research in Engineering, Management and Science is a peer-reviewed, open access journal that publishes original research articles in engineering, management, and applied sciences.

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