About this Research Topic

Manuscript Submission Deadline 07 November 2022

Agriculture has been the backbone of the economy in both the developing and developed world and is assisted with the use of various machineries, such as tractors, transplanters, reapers, combine harvesters and technologies such as hybrid variety, mixed cropping, crop rotation, precision agriculture etc. The use of data is rising exponentially in the agricultural and allied sectors ensuring better control of crop growth and facilitating automation. Addition of satellites, drones and ground sensors through IOT for data collection possess new challenges for faster analysis of pixel-to-pixel data. Cloud computing-based technology will act as a breakthrough and game changer for big data storage, retrieval and analysis. The free to use optical and radarsat satellite images with high spatial resolution will provide new platform for real and near real-time crop growth monitoring. The parallel computing technique or cloud will help immensely in selecting a particular remote sensing image with required specifications and analyzing them for better crop monitoring. Cloud-connected wireless sensors will capture data from the field followed by machine learning algorithms based on real-time analysis for better understanding of vegetation and crop growth. Cloud computing technology will also be very helpful to centralize all-agricultural related data banks (soil-related, weather, crop, farmers, technology, agriculture marketing, fertilizers and pesticide information) in the cloud. Time series remote sensing images will show historical agricultural information leading to better production decisions.

Being a biological entity, fast processing of the information will enable farmers to respond precisely and in a timely manner for better crop growth and yield, whilst maintaining crop quality. Various Cloud platforms include Google Earth Engine (GEE), Akasai, IBM Bluemix, Microsoft Azure, Amazon Web Services, Alibaba etc.

The scope of this Research Topic includes the application of Cloud computing based image processing in agriculture and allied sectors. The theme may include the following sub-themes:

• SAR/optical data applications and analysis in agriculture allied sectors;
• Real or near real-time crop monitoring and yield estimation;
• Climate change impact in water resources and Watershed management;
• Flood, drought and precipitation monitoring;
• Crop scouting and variable rate crop input application; and
• Animal movement, agro-forestry and fishery resources management.

We would like to acknowledge Dr. Chiranjit Singha has acted as Topic coordinator and has contributed to the preparation of the proposal for this Research Topic

Keywords: precision agriculture; crop monitoring; crop yield; water resources


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Agriculture has been the backbone of the economy in both the developing and developed world and is assisted with the use of various machineries, such as tractors, transplanters, reapers, combine harvesters and technologies such as hybrid variety, mixed cropping, crop rotation, precision agriculture etc. The use of data is rising exponentially in the agricultural and allied sectors ensuring better control of crop growth and facilitating automation. Addition of satellites, drones and ground sensors through IOT for data collection possess new challenges for faster analysis of pixel-to-pixel data. Cloud computing-based technology will act as a breakthrough and game changer for big data storage, retrieval and analysis. The free to use optical and radarsat satellite images with high spatial resolution will provide new platform for real and near real-time crop growth monitoring. The parallel computing technique or cloud will help immensely in selecting a particular remote sensing image with required specifications and analyzing them for better crop monitoring. Cloud-connected wireless sensors will capture data from the field followed by machine learning algorithms based on real-time analysis for better understanding of vegetation and crop growth. Cloud computing technology will also be very helpful to centralize all-agricultural related data banks (soil-related, weather, crop, farmers, technology, agriculture marketing, fertilizers and pesticide information) in the cloud. Time series remote sensing images will show historical agricultural information leading to better production decisions.

Being a biological entity, fast processing of the information will enable farmers to respond precisely and in a timely manner for better crop growth and yield, whilst maintaining crop quality. Various Cloud platforms include Google Earth Engine (GEE), Akasai, IBM Bluemix, Microsoft Azure, Amazon Web Services, Alibaba etc.

The scope of this Research Topic includes the application of Cloud computing based image processing in agriculture and allied sectors. The theme may include the following sub-themes:

• SAR/optical data applications and analysis in agriculture allied sectors;
• Real or near real-time crop monitoring and yield estimation;
• Climate change impact in water resources and Watershed management;
• Flood, drought and precipitation monitoring;
• Crop scouting and variable rate crop input application; and
• Animal movement, agro-forestry and fishery resources management.

We would like to acknowledge Dr. Chiranjit Singha has acted as Topic coordinator and has contributed to the preparation of the proposal for this Research Topic

Keywords: precision agriculture; crop monitoring; crop yield; water resources


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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