Precision Agriculture for Sustainable Food Resources
Precision agriculture for sustainable food resources uses digital technologies to increase crop yields and profitability, while reducing inputs for growing crops (fertilizer, herbicides, insecticides, water, land, manpower).
Digital technologies support sustainable food resources by using newer in-field sensors, data collection, the Internet-of-Things (IoTs), data analysis digital platforms, and cloud computing resources.
Topics:
- Variable Rate Equipment and Precision Agriculture
- Digital In-Field Technologies
- Cloud Computing & Edge Computing
- Data Management & Digital Analytics
Variable Rate Equipment and 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
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.
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)…
And the development of a digital agriculture solution that aggregates a cloud platform and agricultural data with sensors, drones, satellites, AI, ML…
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)…
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.
For more information, please refer to Education and PrecisionAg.
- Last Modified: May 17, 2024