Optimizing Vegetable Fertigation with IoT and ML in Controlled Ecosystems
Abstract
This study presents an approach for optimizing fertigation in a controlled setting, utilizing Internet of Things (IoT) and Machine Learning (ML) to enhance water and fertilizer management for vegetable crops, under rain shelter conditions. Using IoT sensors, data are relayed to a cloud system, and analyzed by RF, CNN, RNN and LSTM models. These models predict essential NPK for crops, enabling automated fertigation to maintain optimal growth conditions. This also promotes resource efficiency and environmental conservation. This approach ensures that crops receive adequate water and nutrients, safeguarding yield quality, conserving water, and preserving soil health, thereby marking a pivotal advancement in sustainable farming.
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