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JCSIS

Deep Learning for Sustainable Agriculture and Climate-Resilient Farming Practices

Sam M. K.Hakan KhanLima Hongou
Volume 4

Abstract

This paper investigates title 78. The study presents state-of-the-art techniques, leveraging recent developments in artificial intelligence and emerging technologies. Our methodology combines analytical modeling, real-time data processing, and algorithmic strategies to address domainspecific challenges. Results indicate substantial performance improvements and potential applications in real-world scenarios, particularly in enhancing operational efficiency and decision-making accuracy.

Keywords

Neural Networks, Potato Yield, Machine Learning, Data Mining, IoT

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