Systematic Review of Cloud-Based Irrigation System with Nutrient Delivery and AI Driven Crop Growth Optimization

Abstract

The demand for sustainable agricultural practices has grown significantly due to the global challenges of water scarcity, inefficient resource use, and increasing food demand. Traditional irrigation systems, while widespread, are often inefficient and do not account for the nutrient needs of crops, leading to overconsumption of water and inadequate nutrient distribution. This research proposes the development of an integrated, cloud-based irrigation system that combines nutrient delivery with water management data, guided by Artificial Intelligence (AI) technologies. The system leverages data collected by various sources to inspect critical environmental conditions, including soil water content, soil nutrient status, ambient temperature, and atmospheric moisture levels, which are continuously observed. Using advanced machine learning models, it optimizes both water and nutrient delivery by predicting the precise needs of crops as per requirements. The goal is to improve resource efficiency, increase crop yields, and promote sustainable agriculture. The system's architecture is designed to be adaptive, responding dynamically to changing environmental conditions and crop requirements. Preliminary simulations demonstrate the system’s capability to reduce water usage by up to 40% and improve nutrient uptake efficiency by 30%, showcasing its potential for scalable, datadriven agricultural solutions. This study is an important move toward modern smart farming, helping both food availability and environmental protection.

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