Digital Transformations Enable Sustainable Food Resources
Digital transformations enable sustainable food resources through digital platforms, cloud computing, and the Internet-of-Things (IoTs)…
Following are the the topics for Digital Transformations:
- Digital Platforms, Digital Data Systems & Open Data
- Internet of Things (IoTs)
- Digital Monitoring Systems
- Cloud Computing & Edge Computing
- Digital In-Field Technologies
- Precision Agriculture
- Data Management & Digital Analytics
Digital Transformations Enable Sustainable Food Resources: Digital Platforms, Digital Data Systems & Open Data
Climate change impacts on crop production, food security and supply chains and be better managed by using digital technologies and Open Data. These systems offer the ability to track items, collect real-time data, and forecast future scenarios.
Agriculture is using Big Data, Open Data, Data Analytics, Machine Learning (ML), Artificial Intelligence (AI), Cloud Services and Mobile Applications to help agricultural producers better monitor, manage, and control their operations.
Digital Platforms:
- Data, Raw Data, Big Data, Open Data, Cloud Apps, Algorithms, Analytics Tools, Open Source Tools, Analysis, Insight, Security, Research Publications…
Input / Output Devices for Data:
- Satellites, GIS, Sensors, Mapping Tools, Internet, Internet of Things, Mobile Phones, Mobile Apps, Communication Protocols…
Data Analytics & Applications from global to local:
- Climate parameters
- Soil parameters
- Precision agriculture
- (IoTs ) Internet of Things…
Internet of Things (IoTs)
Intelligent devices embedded with electronics and sensors are being used in home appliances, wearables, vehicles, industrial and agricultural areas.
Cloud-based and / or local applications contain data gathered from any combination of satellites, drones, mobile phones, a variety of instruments and sensors for monitoring, analysis, management decision making, and reporting purposes.
Various communication protocols can be used to connect and transfer the data among the intelligent devices, mobile phones, satellites and data reservoirs for analysis and reporting: (GPS, Cellular, 5G, Bluetooth, Near Field Communication (NFC), Bluetooth Low Energy (BLE), Zigbee, Z-Wave, and 6LoWPAN among other protocols.
There are many agricultural applications for intelligent devices in both crop and livestock production. One example is field sensors that can measure one or several important crop-growing parameters – such as soil moisture & temperature.
The field, farming, and management data collected are being integrated with big data and analyzed using sophisticated open software tools in cloud services. The output helps evaluate and select the optimal management practices such as production inputs.
An example of a digital agriculture solution that aggregates a cloud platform, agricultural data, sensors, drones, satellites, AI, ML…
Digital Monitoring Systems
Planetary Monitoring via Satellites:
- Atmosphere, Climate, Soils, Land, Water, Oceans, Clouds, Cryosphere, Vegetation, Biodiversity, Crops, Vegetation Indices, Emissions, Extreme Weather Events…
- Consumer Monitoring, Economic activity, Trends…
Field Monitoring using Satellites and Local Sensors:
- Daily Satellite Imagery
- Crop Characteristics, Temperature, Rainfall, Soil Moisture, Pests & Disease Outbreaks…
For more information and examples, see
Cloud Computing & Edge Computing
The Cloud provides huge computing power, redundant data centres, massive amounts of data storage, and microservices.
In Microservices, a large application is built as a suite of modular components or services. For example, Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), and Security as a Service (SecaaS). These cloud platform services are often available on a ‘pay-as-you-go’ model. Examples include:
- Microsoft Azure, Google Cloud Platform (GCP), Amazon Web Services (AWS)…
Docker is a set of Platform as a Service (PaaS) products that uses OS-level virtualization to deliver software in packages called containers. Containers bundle their own software, libraries and configuration files, and communicate with each other through defined channels. A docker can be developed on a laptop, and then run at production scale on any cloud platform. For example, an inventory application can be deployed to run on Windows or Linux operating systems.
Kubernetes is a container orchestration system that efficiently coordinates clusters of nodes at scale. For example, load sharing among servers.
Edge Computing systems analyze data close to where it was created. Edge computing reduces the amount of traffic on the overall network, reduces the load on cloud operations, and reduces costs. Examples include:
- Microsoft Azure IoT, Microsoft Azure IoT Edge, Microsoft Azure Edge Zones, Microsoft Azure Private Edge Zones (5G)…
Digital In-Field Technologies
Various sensors are used to collect data, such as temperature and soil moisture. Various devices are used to communicate among devices, and transfer data to local and cloud-based databases for analysis and decision making:
- Global Positioning Satellites (GPS)
- Real-Time Kinematics (RTK)
- Global Navigation Satellite System (GNSS)
- On-Farm Weather Stations
- Communication Protocols
- Connectivity devices – routers
- Sensors (temperature, rainfall, humidity, soil moisture, crop health, pest & disease outbreaks…)
- Variable Rate Equipment (Precision, planting, chemical application…)
- Drones / UAV
- Robotics (Harvesting…)
Various communication protocols can be used to connect and transfer the data among the intelligent devices, mobile phones, satellites and data reservoirs for analysis and reporting:
- Short Range: NFC, Bluetooth, Bluetooth Low Energy (BLE), Zigbee, WiFi, DSRC, CV2X
- Proprietary LPWAN for sensors and battery devices: LoRa, Sigfox, Ingenu
- Cellular: 3G, 4G LTE
- Standards Low-Power WAN (LPWAN): LTE-M, NB-IoT
- Cellular 5G
- Satellite: LEO (GPS, Satellite phone), MEO, GEO
- … among other protocols.
Digital Systems and the Internet of Things (IoTs) for Monitoring
Key features of Digital systems are the ability to collect real-time data, track activities and forecast future scenarios.
Agriculture is rapidly adopting digital systems such as the Internet of Things (IoTs). These systems help improve profitability and advance sustainable food production
In IoT applications, various sensors are used to collect data, such as temperature and soil moisture. Other devices communicate and exchange data. Both local and cloud-based databases are used for comprehensive analysis. The outputs improve decision making.
Digital systems, including the Internet of Things (IoTs), are being used for supply chains, precision agriculture, in-field monitoring, global monitoring, edge computing, cloud computing and other applications.
Output from these digital systems can improve management decision making, increase productivity, increase profitability, and help protect food security.
Data Management & Digital Analytics
Data analysis using Machine Learning (ML) and Artificial Intelligence (AI) techniques for modelling and projecting:
- Planting
- Growing Degree Days (GDDs)
- Growth stages
- Pest & Insect
- Disease
- Risk Management
Supplies data for management decisions, comparisons, and record keeping.
Precision Agriculture
Precision Agriculture uses Variable Rate Equipment to deploy the specified application of inputs based on sustainable farm management practices. Examples of inputs include water, fertilizer, seed, pesticides, herbicides…
- Planting rates
- Fertilizer application
- Chemical application
- Pest & disease treatment…
- Harvesting…
Precision agriculture maximizes the use of resources and minimizes use of energy, pollution, and waste. It maximizes the efficiency of workers.
Tractors equipped with monitors and monitoring systems are used to plant or deliver the right amount in the right place at the right time.
And on the way are the use of self-driving and electric tractors.
For more information, please refer to Education.
- Last Modified: May 17, 2024