Tutorial 6 : Precision agriculture smart farming Flashcards
Global positioning system
a satellite-based navigation system that allows precise determination of geographical locations on earth
variable rate technology
technology used in agriculture to vary the rate of application of inputs such as fertilisers, pesticides, and irrigation water according to specific special requirements within a field
Yield monitoring and mapping
technologies used to measure/record crop yields in agricultural fields, often with the aid of GPS and sensors, and to create spatial maps showing variations in yield across the field
remote sensing
information about an object without making physical contact with it, through sensors on aircraft or satellites
unmanned aerial vehicles
aircraft operated without a human pilot on board, commonly known as drones, are used in various applications including aerial photography, surveying, monitoring in agriculture
geographic information systems
system designed to capture, store, manipulate, analyse, manage and present spatial/geographic data
automated steering system
technology that enables vehicles or machinery to automatically steer along a predetermined path, often using GPS or other positioning systems
precision farming software
software applications designed to assist farmers in managing their operations with a high degree of precision, often integrating data from various sources such as GPS, sensors, remote sensing
drones
unmanned aerial vehicles are used for various purposes including data collection, surveillance, and monitoring in agriculture
connectivity protocols
standards and protocols governing the communication between devices and systems in precision agriculture, ensuring interoperability and compatibility and also keeps data safe
telematics
the technology of sending, receiving, and storing information related to remote objects, typically vehicles via telecommunication devices
(fast and simultaneously)
machine vision technology
technology that enables machines to visually perceive their environment, often through cameras and image processing algorithms
artificial intelligence
a simulation of human intelligence processes by machines, typically involving tasks such as learning, reasoning, problem-solving
machine learning
a subset of AI that focuses on the development of algorithms and statistical models that enable computers to perform specific tasks without being explicitly programmed
deep learning
a subset of machine learning that uses neural networks with many layers that go deeper and deeper and deeper to learn from large amounts of data
decision support system
computer-based systems that assist decision-makers in making choices by providing relevant information and analysis
fourth agricultural revolution
the ongoing transportation of agriculture through technological advancements such as precision agriculture, robotics, and data-driven decision-making
agriculture 4.0
another term for the fourth agricultural revolution, emphasising the role of digital technologies and connectivity in transforming agricultural practices
digital farming
farming practices that leverage digital technologies and data analytics to optimise production, resource use, and decision-making (smart farming)
robotics and autonomous systems
technologies involving the use of robots and automated systems to perform tasks in agricultural settings, such as planting, harvesting, and monitoring
high-throughput phenotyping
the rapid and automated measurement of plant traits, often using technologies such as sensors, imaging, and machine learning, to accelerate breeding and crop improvement effort
big data analysis
the process of analysing large and complex data sets to uncover patterns, trends, and insights that can inform decision-making in agriculture
satellite remote sensing
the use of satellite-based sensors to collect data about the Earth’s surface, atmosphere, and oceans, often used for monitoring and managing agricultural resources
nanotechnology
manipulation of matter in nanoscale, often used in agriculture for applications such as crop protection, nutrient delivery and soil remediation
precision nano-agriculture
the application of nanotechnology in agriculture to enhance precision farming practices, such as targeted delivery of agrochemicals and sensors for monitoring soil and plant health and sustainability
Eddy covariance
method used to measure the exchange of gasses, momentum and energy between the earth’s surface and the atmosphere providing insights into ecosystem processes such as carbon cycling and evapotranspiration
RGB cameras
used for a wide range of applications in agriculture
evaluating crop development through spectral vegetation indices, identifying weeds, pests, and diseases, machine guidance, real-time spot spraying, and robotic weeding
multispectral cameras
can offer more information compared to RGB cameras because they can include information from Near-infrared and the red edge (this spectral vegetation indices based on these bands is better for correlation with crop vigour compared to RGB bands)
hyperspectral cameras
can offer the highest amount of information because of its ability to collect data in more than 50 bands. Accordingly, hyperspectral cameras have been used for weed identification, weed resistance to herbicides, disease detection and thermal micro-dosing
thermal cameras
can be influenced by weather conditions and the change in altitude in case they are used with aerial platforms, while they must have adequate spectral or measurement resolution for appropriate can be influenced by weather conditions and the change in measurement of canopy temperature
optical sensors
detect light intensity and convert light rays into an electrical output. Used to sense pressure displacement level, temperature level,…
In agriculture, it is mainly used to measure the soil properties such as PH, organic elements, phenotyping and more
advantage of precision agriculture
- increased productivity
- reduced costs
- enhanced sustainability
- increased land values- better harvestability
disadvantage of precision agriculture
- initial costs of technology adoption
- technical complexity
- challenges related to data management and interpretation
- potential for increased reliance on technology
- knowledge needed
- not accessible to everyone
- maintenance (not very sustainable)
- loss of jobs (not sustainable in the social aspect)
smart farming
technology that relies on its implementation with the use of AI and the IoT in cyber-physical farm management
