AI-Deep Learning Based Agroclimatic Mapping for Ideal Planting Zone Classification in East Nusa Tenggara

Adi Jufriansah, Azmi Khusnani, Dedi Suwandi Wahab

Abstract


East Nusa Tenggara (NTT) Province has varied climate characteristics, so agroclimatic mapping is important in adaptive agricultural planning. This study aims to map agroclimatic zones in NTT based on rainfall and air temperature using remote sensing techniques and the Convolutional Neural Network (CNN) model. The mapping results show that NTT consists of several agroclimatic zones with different levels of rainfall and temperature, which significantly affect cropping patterns and agricultural productivity. This mapping produces recommendations for farmers in determining the types of crops that are appropriate to the agroclimatic conditions of each region. In areas with low rainfall, the use of drought-resistant plant varieties and efficient irrigation systems is recommended. In addition, local governments can consider building reservoirs and ponds to increase resilience to the dry season. In terms of technology, the CNN model developed in this study has the potential to be further refined by adding more historical data and other environmental variables, such as vegetation indices from satellite imagery, to improve prediction accuracy. The implementation of artificial intelligence technology in agricultural planning in NTT can be a strategic step in increasing food security and supporting the sustainability of the agricultural sector in this region.


Keywords


Agroclimatic; Remote Sensing; Convolutional Neural Network; East Nusa Tenggara; Sustainable Agriculture

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DOI: https://doi.org/10.17509/gea.v25i1.80380

DOI (PDF): https://doi.org/10.17509/gea.v25i1.80380.g31631

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