Advancing the science of sustainable urban agriculture through AI and IoT innovation.
A comprehensive study on the implementation of Convolutional Neural Networks (CNN) for identifying 15+ common plant diseases in high-humidity urban environments. Our research demonstrates a 98.2% validation accuracy across diverse crop types.
View Study DetailsAnalyzing the impact of automated pH and EC monitoring on the growth cycles of leafy greens. This study highlights a 30% reduction in water consumption compared to traditional NFT systems while maintaining optimal yield.
View Study DetailsExploring the deployment of low-power sensor arrays to map temperature and humidity fluctuations in high-density urban balcony farms. The research focuses on predictive modeling for heat-stress prevention.
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