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Biosystematics of Triticeae : Volume II. Genera: Secale, Tritiosecale, Pseudosecale, Eremopyrum, Henrardia, Taeniantherum, Heteranthelium, Crithopsis, and Hordeum / / by Chi Yen, Junliang Yang
| Biosystematics of Triticeae : Volume II. Genera: Secale, Tritiosecale, Pseudosecale, Eremopyrum, Henrardia, Taeniantherum, Heteranthelium, Crithopsis, and Hordeum / / by Chi Yen, Junliang Yang |
| Autore | Yen Chi |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (474 pages) |
| Disciplina | 584.926 |
| Altri autori (Persone) |
YangJunliang
WangJirui ZhaoHui ZhangQin TanChao |
| Soggetto topico |
Agriculture
Plants - Evolution Evolution (Biology) Plant Evolution Evolutionary Biology |
| ISBN |
9789811580574
981158057X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Biosystematics of Secale -- Chapter 2. Biosystematics of Triticale -- Chapter 3. Biosystematics of Pseudosecale -- Chapter 4. Biosystematics of Eremopyrum -- Chapter 5. Biosystematics of Henrardia -- Chapter 6. Biosystematics of Taeniatherum -- Chapter 7. Biosystematics of Heteranthelium -- Chapter 8. Biosystematics of Crithopsis -- Chapter 9. Biosystematics of Hordeum. |
| Record Nr. | UNINA-9910917779203321 |
Yen Chi
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Precision agriculture technology for crop farming / / edited by Qin Zhang ; contributors Hermann Auernhammer [and twenty one others]
| Precision agriculture technology for crop farming / / edited by Qin Zhang ; contributors Hermann Auernhammer [and twenty one others] |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Boca Raton, FL : , : CRC Press, , [2016] |
| Descrizione fisica | 1 online resource (372 p.) |
| Disciplina |
338.1
631 |
| Soggetto topico | Precision farming - Technological innovations - United States |
| ISBN |
1-000-21898-8
0-429-15968-4 1-4822-5108-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1 A history of precision agriculture / David Franzen and David Mulla -- chapter 2. Sensing technology for precision crop farming / Marvin L. Stone and William R. Raun -- chapter 3. Data processing and utilization in precision agriculture / Chunjiang Zhao, Liping Chen, Guijun Yang, and Xiaoyu Song -- chapter 4. Control of precision agriculture production / Qin Zhang -- chapter 5. Intelligent agricultural machinery and field robots / Shufeng Han, Brian L. Steward, and Lie Tang -- chapter 6. Precision agriculture in large-scale mechanized farming / Chenghai Yang, Ruixiu Sui, and Won Suk Lee -- chapter 7. A systems approach to community-based precision agriculture / Sakae Shibusawa -- chapter 8. Precision agriculture in china: sensing technology and application / Hong Sun and Minzan Li -- chapter 9. Good agricultural practices, quality, traceability, and precision agriculture / Josse De Baerdemaeker and Wouter Saeys -- chapter 10. State of the art and future requirements / Hermann Auernhammer and Markus Demmel. |
| Record Nr. | UNINA-9910410650003321 |
| Boca Raton, FL : , : CRC Press, , [2016] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Soil and crop sensing for precision crop production / / edited by Minzan Li, Chenghai Yang, Qin Zhang
| Soil and crop sensing for precision crop production / / edited by Minzan Li, Chenghai Yang, Qin Zhang |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (331 pages) |
| Disciplina | 016.016 |
| Collana | Agriculture Automation and Control |
| Soggetto topico | Agricultural innovations |
| ISBN | 3-030-70432-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Contents -- About the Editors -- Contributors -- Chapter 1: Soil and Crop Sensing for Precision Crop Production: An Introduction -- 1.1 History of Agriculture, from Agriculture 1.0 to Agriculture 4.0 -- 1.2 Modern Agriculture Technologies -- 1.2.1 Precision Agriculture -- 1.2.2 Digital Agriculture -- 1.2.3 Smart Agriculture -- 1.3 New Challenges in Agriculture -- 1.3.1 IoT in Agriculture -- 1.3.2 Big Data in Agriculture -- 1.3.3 Cloud Computing in Agriculture -- 1.4 Overview of Soil and Crop Sensing Technologies for Precision Crop Production -- 1.4.1 Information of Soil and Crop for Precision Crop Production -- 1.4.2 Soil and Crop Sensing Technologies for Precision Crop Production -- 1.5 Summary -- References -- Chapter 2: Sensing Technology of Soil Physical Properties -- 2.1 Sensing Technology of Soil Texture -- 2.1.1 Introduction of Soil Texture -- 2.1.2 Sensing Technology for Soil Texture and Particle Size -- 2.2 Sensing Technology of Soil Water Content -- 2.2.1 Measurement of Soil Water Content with Dielectric Parameters -- 2.2.2 Measurement of Soil Water Content with Neutron Meter -- 2.2.3 Measurement of Soil Water Content with Near-Infrared Spectroscopy -- 2.3 Sensing Technology of Soil Porosity and Bulk Density -- 2.3.1 Introduction of Soil Porosity and Bulk Density -- 2.3.2 Sensing Instruments of Soil Porosity and Bulk Density -- 2.4 Sensing Technology of Soil Compaction -- 2.4.1 Measurement of Soil Compaction with Penetrometer -- 2.4.2 On-the-Go Measurement of Soil Compaction -- 2.5 Sensing Technology of Soil Cation Exchange Capacity -- 2.5.1 Introduction of Soil Cation Exchange Capacity -- 2.5.2 Sensing Method of Soil Cation Exchange Capacity -- 2.5.3 Sensing Technologies of Soil Cation Exchange Capacity -- References -- Chapter 3: Theories and Methods for Soil Nutrient Sensing.
3.1 Laboratory Measurement of Soil Nutrients -- 3.1.1 Detection of Soil Nitrogen Content in Laboratory -- 3.1.2 Detection of Soil Phosphorus Content in Laboratory -- 3.1.3 Detection of Soil Kalium Content in Laboratory -- 3.1.4 Detection of Soil Organic Matter Content in Laboratory -- 3.2 Spectral Technology for Soil Nutrient Sensing -- 3.2.1 Vis/NIR Spectral Sensing Technology for Soil Nutrients -- 3.2.2 Mid-infrared Spectral Sensing Technology for Soil Nutrients -- 3.2.3 LIBS Sensing Technology for Soil Nutrients -- 3.2.4 Multispectral and Hyperspectral Imaging Sensing Technology for Soil Nutrients -- 3.3 Instruments of Soil Nutrient Detection -- 3.3.1 Portable Instruments of Soil Nutrient Detection -- 3.3.2 Airborne Equipment of Soil Nutrient Detection -- 3.3.3 Satellite-Based Equipment for Soil Nutrient Detection -- 3.3.4 Sensors in Internet of Things of Soil Nutrient Detection -- 3.4 Summary -- References -- Chapter 4: Application of Soil Sensing in Precision Agriculture -- 4.1 Tractor-Mounted Soil Analysis System Based on Vis-NIR Spectroscopy -- 4.1.1 Soil Sensing Instruments Based on Vis-NIR Spectroscopy -- 4.1.2 Tractor-Mounted Soil Analysis Systems -- 4.1.3 Soil Analysis System (SAS) Series -- 4.1.4 Analysis of Calibration Model for Multiple Soil Properties -- 4.2 Application of Tractor-Mounted Soil Analysis System in Precision Agriculture -- 4.2.1 Site-Specific Soil Mapping and Interpretation of Agricultural Fields -- 4.2.2 Decision-Making for Crop Precision Farming -- 4.3 Measurement and Application of Soil EC in Precision Agriculture -- 4.3.1 Soil EC Measurement: Theory and Method -- 4.3.2 On-the-Go Measurement System of Soil EC -- 4.3.3 Application of Soil EC in Precision Agriculture -- References -- Chapter 5: Theories and Methods for Spectroscopy-Based Crop Nutrient Sensing. 5.1 Spectral Characteristics and Vegetation Indices of Crop Nutrients -- 5.1.1 Canopy Spectral Characteristics of Crop Nutrients -- 5.1.2 Vegetation Indices of Crop Nutrients -- 5.2 Estimation of Leaf Nitrogen Accumulation in Wheat Based on Hyperspectral Sensing -- 5.2.1 Analysis of Canopy Spectral Characteristics -- 5.2.2 Spectral Monitoring Models for Crop Nutrients -- 5.3 Real-Time Diagnosis of Crop Growth -- 5.3.1 Diagnosis of Crop Nutrient Status Based on Nutrient Balance Principle -- 5.3.2 Diagnosis of Crop Nutrient Status Based on Nitrogen Index Method -- 5.3.3 Diagnosis of Crop Nutrient Status Based on Indicator Difference Method -- 5.3.3.1 Dynamic Changes of Leaf Area Index -- 5.3.3.2 Dynamic Changes of Spectral Index -- 5.4 Ground-Based, UAV-Borne, and Satellite Remote Sensing for Crop Nutrient Management -- 5.4.1 Ground-Based Monitoring Systems for Crop Nutrient Management -- 5.4.2 UAV-Borne Monitoring System for Crop Nutrient Management -- 5.4.3 Satellite-Based Remote Sensing Systems for Crop Nutrient Management -- References -- Chapter 6: Remote Sensing Technologies for Crop Disease and Pest Detection -- 6.1 Introduction -- 6.2 Remote Sensing Platforms and Systems for Disease and Pest Detection -- 6.2.1 Satellite Sensors for Disease and Pest Detection -- 6.2.2 Manned Aircraft-Based Imaging Systems for Disease and Pest Detection -- 6.2.2.1 Multispectral Imaging Systems Based on Industrial Cameras -- 6.2.2.2 Multispectral Imaging Systems Based on Consumer-Grade Cameras -- 6.2.2.3 Hyperspectral Cameras -- 6.2.3 Unmanned Aircraft-Based Imaging Systems for Disease and Pest Detection -- 6.3 Practical Methodologies for Crop Disease Detection and Management -- 6.3.1 Image Selection and Acquisition for Crop Disease Detection -- 6.3.2 Image Processing and Prescription Map Creation for Crop Disease Management. 6.3.3 Site-Specific Fungicide Application for Crop Disease Management -- 6.3.4 Performance and Efficacy Evaluation for Crop Disease Management -- 6.4 Challenges and Future Development -- References -- Chapter 7: Plant Phenotyping -- 7.1 Introduction -- 7.2 Sensing Instruments for Plant Phenotyping -- 7.2.1 Overview -- 7.2.2 Sensing Instrumentation for Plant Canopy -- 7.2.2.1 Plant Height Measurement -- Plant Height Measurement Based on Stereo Vision Systems -- Plant Height Measurement Based on Lidar Sensors -- Plant Height Measurement Based on Ultrasonic Sensors -- Plant Height Measurement Based on Range Cameras -- Comparison and Analysis of Plant Height Measurements -- 7.2.2.2 Leaf Parameter Measurements -- Leaf Parameter Measurement Using Color Digital Cameras and Stereo Vision System -- Leaf Parameter Measurement Using Range Cameras -- Leaf Parameter Measurement Using Spectral Sensors and Cameras -- Leaf Parameter Measurement Using Lidar/Laser Sensors -- Comparison and Analysis of Leaf Parameter Measurements -- 7.2.2.3 Chlorophyll Measurements -- Chlorophyll Measurement Based on Spectral Sensors -- Chlorophyll Measurement Based on Spectral Cameras -- Chlorophyll Measurement Based on Fluorescence Sensors -- Chlorophyll Measurement Based on Lidar/Laser Sensors -- Comparison and Analysis of Chlorophyll Measurements -- 7.2.2.4 Water Stress Measurements -- Water Stress Measurement Based on Thermometers -- Water Stress Measurement Based on Thermography -- Water Stress Measurement Based on Spectral Sensors and Cameras -- Comparison and Analysis of Water Stress Measurements -- 7.2.3 Sensing Instruments for Biomass -- 7.2.3.1 Biomass Measurement Using the Nonspectral Method -- 7.2.3.2 Biomass Measurement Using the Spectral Method -- 7.2.3.3 Combined Method for Biomass Measurement -- 7.2.3.4 Comparison and Analysis of Biomass Measurements. 7.2.4 Sensing Instrument for Plant Roots -- 7.2.4.1 Plant Root Measurement Based on Color Digital Cameras -- 7.2.4.2 Plant Root Measurement Based on X-Ray CT -- 7.2.4.3 Plant Root Measurement Using Other Sensing Techniques -- 7.2.4.4 Comparison and Analysis of Plant Root Measurements -- 7.3 Platforms for Plant Phenotyping -- 7.3.1 Ground-Based Platforms -- 7.3.1.1 In-Field Sensor Networks -- 7.3.1.2 Ground Mobile Platforms -- 7.3.2 Aerial Platforms -- 7.3.2.1 Unmanned Aerial Vehicles (UAVs) -- 7.3.2.2 Blimps -- 7.3.2.3 Manned Aerial Vehicles -- 7.3.2.4 Satellites -- 7.3.3 Indoor Platforms -- 7.3.3.1 Tissue-Level Platforms -- 7.3.3.2 Organ-/Single Plant-Level Platforms -- 7.3.3.3 Group-Level Platforms -- 7.4 Data Analytics for Plant Phenotyping -- 7.4.1 Data Preprocessing -- 7.4.1.1 Overview -- 7.4.1.2 Image Preprocessing -- 7.4.1.3 Point Cloud Preprocessing -- 7.4.2 Traditional Statistical Analysis -- 7.4.3 Machine Learning and Deep Learning -- 7.4.3.1 Machine Learning -- 7.4.3.2 Deep Learning -- 7.5 Summary -- References -- Chapter 8: Crop Sensing in Precision Agriculture -- 8.1 Introduction -- 8.2 Spectroscopy-Based Sensing Instruments for Crop Monitoring -- 8.2.1 Foundation of Spectral Sensing and Vegetation Indices in Crop Sensing -- 8.2.2 Spectral Sensing in Crop Monitoring -- 8.2.2.1 Hyper-Spectrometers for Crop Sensing -- 8.2.2.2 Portable Sensors Used in Crop Monitoring -- Portable Sensors for Leaf Measurement -- Portable Sensors for Canopy Measurement -- 8.2.3 Development of Spectroscopy-Based Systems for Crop Detection -- 8.2.3.1 Development of Hyperspectral Sensors for Crop Monitoring -- 8.2.3.2 WSN-Based Sensors for Crop Monitoring -- 8.2.3.3 An Integrated Sensor Based on Spectroscopy and Imagery -- 8.3 Image Sensing for Crop Detection -- 8.3.1 Foundation of Crop Imaging and Feature Extraction. 8.3.2 Imaging Technologies Used in Crop Detection. |
| Record Nr. | UNINA-9910592993703321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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