Feng Haikuan (22 results)

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Hardcover. Condition: new. Hardcover. The integration of optical sensors and machine learning (ML) technologies has transformed agricultural monitoring, delivering precise, real-time insights into crop health, growth patterns, and environmental interactions. These sensors-spanning multispectral, hyperspectral, and RGB cameras-capture intricate spectral signatures that detect subtle physiological shifts in crops. When paired with ML algorithms, the resulting data streams yield actionable intelligence, informing key decisions in precision agriculture, yield forecasting, and disease control.This Reprint of the second edition of the Special Issue entitled "Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring" builds upon the foundational research established by prior studies in the field. Featuring 17 original research articles, this collection tackles key methodological challenges, including data imbalance, improved model transferability, and the fusion of multisensor data for resilient monitoring. Together, these works highlight the advancing capabilities of optical sensors in acquiring high-resolution, multidimensional datasets, which ML models exploit for advanced pattern recognition and predictive modeling. Beyond refining current approaches, the studies within this Reprint pave the way for emerging innovations, such as edge computing and AI-powered automation in agricultural ecosystems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …

Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Agricultural production management is facing a new era of intelligence and automation. With developments in sensor technologies, the temporal, spectral, and spatial resolution from ground/air/space platforms have been notably improved. Optical sensors play an essential role in agriculture production management. Specifically, monitoring plant health, growth conditions, and insect infestation has traditionally involved extensive fieldwork. We believe that sensors, artificial intelligence, and machine learning are not simply scientific experiments but opportunities to make our agricultural production management more efficient and cost-effective, further contributing to the healthy development of natural-human systems. This reprint compiles the latest research on optical sensors and machine learning in agricultural monitoring, including related topics: Machine learning approaches for crop health, growth, and yield monitoring; Combined multisource/multi-sensor data to improve the crop parameters mapping; Crop-related growth models, artificial intelligence models, algorithms, and precision management; Farmland environmental monitoring and management; Ground, air, and space platforms application in precision agriculture; Development and application of field robotics; High-throughput field information survey; Phenological monitoring.…

Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The integration of optical sensors and machine learning (ML) technologies has transformed agricultural monitoring, delivering precise, real-time insights into crop health, growth patterns, and environmental interactions. These sensors-spanning multispectral, hyperspectral, and RGB cameras-capture intricate spectral signatures that detect subtle physiological shifts in crops. When paired with ML algorithms, the resulting data streams yield actionable intelligence, informing key decisions in precision agriculture, yield forecasting, and disease control.This Reprint of the second edition of the Special Issue entitled 'Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring' builds upon the foundational research established by prior studies in the field. Featuring 17 original research articles, this collection tackles key methodological challenges, including data imbalance, improved model transferability, and the fusion of multisensor data for resilient monitoring. Together, these works highlight the advancing capabilities of optical sensors in acquiring high-resolution, multidimensional datasets, which ML models exploit for advanced pattern recognition and predictive modeling. Beyond refining current approaches, the studies within this Reprint pave the way for emerging innovations, such as edge computing and AI-powered automation in agricultural ecosystems.…

Buch. Condition: Neu. Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring | Buch | Englisch | 2023 | MDPI AG | EAN 9783036597980 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…

Buch. Condition: Neu. Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring | 2nd Edition | Buch | Englisch | 2026 | MDPI AG | EAN 9783725868148 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…