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HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

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HRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Hardcover. Condizione: new. Hardcover. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condizione: New. Print on Demand.

Condizione: New. Print on Demand.

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Hardcover. Condizione: new. Hardcover. 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. 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. Condizione: 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. Condizione: 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. Condizione: Neu. How Optical Sensors and Deep Learning Enhance the Production Management in Smart Agriculture | Buch | Englisch | 2026 | MDPI AG | EAN 9783725865482 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…