How Optical Sensors and Deep Learning Enhance the Production Management in Smart Agriculture (en Inglés) - Jibo Yue;Meiyan Shu;Chengquan Zhou

The advent of smart agriculture marks a paradigm shift from experience-driven to data-driven decision-making, fundamentally reshaping centuries-old farming practices. At the heart of this transformation lies the synergistic integration of optical sensors and deep learning (DL) technologies. Modern optical sensors range from cost-effective RGB cameras and near-infrared-modified consumer devices to sophisticated multispectral, hyperspectral, and fluorescence imaging systems. When combined with these powerful DL approaches, the datasets are transformed into actionable intelligence, enabling precise yield forecasting at the individual plant or plot level, the automated phenotyping of thousands of genotypes per day, and the real-time variable-rate application of water, fertilizers, and pesticides.

This Reprint, entitled "How Optical Sensors and Deep Learning Enhance the Production Management in Smart Agriculture," assembles thirteen high-quality contributions that exemplify the current state of the art and illuminate viable paths forward. The collected works span staple crops (rice, maize, wheat), high-value horticultural species, and complex real-world field scenarios. By bridging the gap between algorithmic sophistication and agricultural practicality, this Special Issue not only documents tangible progress but also establishes a benchmark and inspiration for the next wave of optical-DL innovations in global food production.