The soaring world population and climate change are recurring concerns in the agricultural sector. However, novel remote sensing (RS) technologies can certainly assist in the reduction of farming impact on the environment while offering efficient solutions for crop management to support agricultural sustainability. Similarly, RS supports precision agriculture for decision-making and crop management recommendations that in return result in higher crop growth, yield, and quality. For instance, nitrogen (N) fertilizer management is of prime importance for crop yield and profitability. In this context, remote sensing technologies such as sensors and machine learning can estimate the percentage nitrogen (%N) in a specific crop, thereby making recommendations on nitrogen fertilizer application. Similarly, yield estimation and mapping through georeferencing, crop biomass estimation through LiDAR technology, ecosystem services (related to soil or water resources), and objects classification, data fusion, cloud removal, and spectral analysis of environmental changes from satellite or aerial imagery through artificial intelligence are few of the promising applications of RS technologies.
Sensors are regarded as the backbone of RS; however, satellites and other remote collection platforms are an integral part of RS. Generally, traits or features of the agricultural systems are collected through RS technologies and are analyzed to generate recommendations or predictions for one or several crop management aspects. Nevertheless, several challenges exist in data acquisition and analysis. For instance, poor spatial and spectral resolution of remotely sensed imagery, absence of digital field boundaries, inconsistencies in crop models, noise, heterogeneous data, etc. In this regard, novel technologies such as big data, smart simulation and optimization, internet of everything (IoE), cloud computing, and artificial intelligence (AI) offer substantial solutions to address these issues. On one hand they ease the multisource/multi-sensor data acquisition, while on the other hand they facilitate the establishment of various AI models and algorithms.
This Research Topic aims to bring together the latest and innovative research findings in the field of RS, with a special focus on the implementation of novel technologies for the estimation of crop condition, growth status, fertilizer management, yield estimation, and soil conditions using satellite, air-borne devices (drones), and proximal sensing platforms. The following provides a general (but not exhaustive) overview of the topics that might be relevant for this Research Topic:
1. Estimation of crop health, condition, and yield mapping and estimations
2. Crop stress assessment and monitoring
3. Remote detection of crop pests and diseases
4. Combination of multisource/multi-sensor data to improve the retrieval of crop parameters
5. Remote sensing applications for precision management, including fertilizer, pesticide, irrigation, etc.
6. Soil physical and chemical properties mapping
7. AI for sustainable agriculture
8. Big data analytics for remote sensing
9. Image processing and data-fusion technologies
10. Drones/Satellite platforms development and applications for sustainable agriculture
11. Machine learning approaches, algorithms development and validation
12. Towards smart agriculture using AI
The soaring world population and climate change are recurring concerns in the agricultural sector. However, novel remote sensing (RS) technologies can certainly assist in the reduction of farming impact on the environment while offering efficient solutions for crop management to support agricultural sustainability. Similarly, RS supports precision agriculture for decision-making and crop management recommendations that in return result in higher crop growth, yield, and quality. For instance, nitrogen (N) fertilizer management is of prime importance for crop yield and profitability. In this context, remote sensing technologies such as sensors and machine learning can estimate the percentage nitrogen (%N) in a specific crop, thereby making recommendations on nitrogen fertilizer application. Similarly, yield estimation and mapping through georeferencing, crop biomass estimation through LiDAR technology, ecosystem services (related to soil or water resources), and objects classification, data fusion, cloud removal, and spectral analysis of environmental changes from satellite or aerial imagery through artificial intelligence are few of the promising applications of RS technologies.
Sensors are regarded as the backbone of RS; however, satellites and other remote collection platforms are an integral part of RS. Generally, traits or features of the agricultural systems are collected through RS technologies and are analyzed to generate recommendations or predictions for one or several crop management aspects. Nevertheless, several challenges exist in data acquisition and analysis. For instance, poor spatial and spectral resolution of remotely sensed imagery, absence of digital field boundaries, inconsistencies in crop models, noise, heterogeneous data, etc. In this regard, novel technologies such as big data, smart simulation and optimization, internet of everything (IoE), cloud computing, and artificial intelligence (AI) offer substantial solutions to address these issues. On one hand they ease the multisource/multi-sensor data acquisition, while on the other hand they facilitate the establishment of various AI models and algorithms.
This Research Topic aims to bring together the latest and innovative research findings in the field of RS, with a special focus on the implementation of novel technologies for the estimation of crop condition, growth status, fertilizer management, yield estimation, and soil conditions using satellite, air-borne devices (drones), and proximal sensing platforms. The following provides a general (but not exhaustive) overview of the topics that might be relevant for this Research Topic:
1. Estimation of crop health, condition, and yield mapping and estimations
2. Crop stress assessment and monitoring
3. Remote detection of crop pests and diseases
4. Combination of multisource/multi-sensor data to improve the retrieval of crop parameters
5. Remote sensing applications for precision management, including fertilizer, pesticide, irrigation, etc.
6. Soil physical and chemical properties mapping
7. AI for sustainable agriculture
8. Big data analytics for remote sensing
9. Image processing and data-fusion technologies
10. Drones/Satellite platforms development and applications for sustainable agriculture
11. Machine learning approaches, algorithms development and validation
12. Towards smart agriculture using AI
