Synthetic data at scale: a development model to efficiently leverage machine learning in agriculture.

The rise of artificial intelligence (AI) and in particular modern machine learning (ML) algorithms during the last decade has been met with great interest in the agricultural industry. While undisputedly powerful, their main drawback remains the need for sufficient and diverse training data. The collection of real datasets and their annotation are the main cost drivers of ML developments, and while promising results on synthetically generated training data have been shown, their generation is not without difficulties on their own.

A solar-driven atmospheric water extractor for off-grid freshwater generation and irrigation.

Solar-driven atmospheric water extraction (SAWE) is a sustainable technology for decentralized freshwater supply. However, most SAWE systems produce water intermittently due to the cyclic nature, with adoption hindered by complex design requirements or periodic manual operations. Herein, a fully passive SAWE system that can continuously produce freshwater under sunlight is presented.