Abstract
This article presents key technological advances in digital agriculture, which will have a significant impact. Artificial intelligence-based techniques, together with big data analytics, address the challenges of agricultural production in terms of productivity and sustainability. Emerging new applications will transform agriculture from the traditional farm practices to a highly automated and data-intensive industry.
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IEEE Keywords
- Agriculture ,
- Robot sensing systems ,
- Remote sensing ,
- Satellite broadcasting ,
- Task analysis ,
- Decision making ,
- Digital agriculture
Introduction
The digital revolution is transforming agriculture by using modern machinery, computerized tools, and information and communication technologies (ICTs) to improve decision making and productivity. The spread of several cutting-edge technologies, from GPS and remote sensing to big data, artificial intelligence and machine learning, robotics, and the Internet of Things (IoT), to agriculture is leading to increased yields, lower costs and reduced environmental impact. Data-driven solutions are unlocking production potential in a sustainable and resource-efficient way.
Precision agriculture management systems are allowing growers to benefit from this new tsunami of data they can gather. These systems collect, classify, and analyze vast amounts of data to detect patterns and solutions. They enable farmers to observe, comprehend, and manage the variability in their production systems by tailoring inputs to get desired outputs. GPS-controlled tractors can work around the clock, ploughing, planting, and harvesting while gathering continuous “on-the-go” georeferenced data. These self-driving vehicles can perform precise operations, with the help of GPS, Geographical Information Systems (GIS), and Variable-Rate Technology (VRT).
Weather stations supply a variety of agriculture-specific weather data. Such weather data are fed into the big data pool and promote farming decisions, such as irrigation decision making based on plant-water demand and accurate forecast of harvest dates. Remote and proximal sensing is used to capture invaluable soil and crop-related data through hyperspectral, multispectral, and thermal sensors or cameras. Unmanned aerial vehicles (UAV) for agricultural purposes have the potential to be used from analysis applications, by producing soil and field three-dimensional models, data acquisition, and crop growth monitoring, to spraying or planting applications.[1] They are delivering regularly updated high-quality data to provide insight into crop development and highlight ineffective practices to track changes in health and maturity and to identify parts of a field experiencing “hydric stress.” UAVs have also proven well suited for crop spraying, as they can apply fertilizer, herbicide, and pesticide liquids faster, more accurately, and with higher efficiency. Finally, UAV-planting systems are under development by using compressed air to fire seedpods directly into the ground.
The digitalization enables farmers to control their farms remotely and manage agricultural activities in a more effective way. In the near future, IoT will allow for automatic real-time interaction, controlling, and decision-making as agriculture sensors, actuators, and devices, as aforementioned, will all be interconnected. This will minimize human effort while saving time and increasing both yield and profit.[2] The emergence of cloud-based farm management platforms, such as the SmartFarm and Agrivi, aims to integrate these data coming multiple sources and include decision support systems. All these give growers insights for dynamic management planning, which have traditionally only been available to corporate megafarms.
REMOTE SENSING
A variety of remote sensing technologies, from proximal sensors (within 1 m distance from the monitoring object), to drones, to satellites are used by the agricultural sector, providing insight to tackle the uncertainties coming from the variations of weather conditions and management strategies. These sensors exploit vegetation’s reflectance properties and provide the opportunity to assess biomass, yield, acreage, vegetation vigor, drought stress, and phenological development, enabling early and efficient decision making in fertilization, irrigation, and pest management. Currently, the commercial availability of very high-resolution satellite data that varies in technique (active/passive, radiometer/scatterometer), spatial resolution (from submeter to kilometers), spectral range, and viewing geometry has opened up a number of new perspectives on the use of earth observation products in agricultural monitoring,[3] both at large and small scale areas. Similar information can also be retrieved by UAV remote sensing systems, which are often operated at very low altitude. Such systems use multispectral, hyperspectral, and thermal cameras that can measure heat, radiation, or light to capture a diverse electromagnetic spectrum. Although data retrieval in this case is less dependent on weather conditions, simplifying or even omitting atmospheric correction, these images cover much less area compared to the satellite products.
Technological advancements, such as analytical platforms, multispectral and hyperspectral sensors, as well as satellite data hubs that provide free and open access to satellite products [e.g., Copernicus Open Access Hub (ESA), Earth-Explorer (United States Geological Survey), National Oceanic and Atmospheric Administration (United States Department of Commerce)], can act as the stepping stone for building reliable agronomic models on the basis of existing data generated by innovative monitoring applications. Furthermore, the development of agricultural infrastructure networks that allow faster and complete mining of the agricultural information through satellite data in deeper and broader horizons is necessary to improve the quality and efficiency of agricultural monitoring. This can also accelerate the delivery of agricultural data platforms, which could provide timely comprehensive information, to guide agronomic and economic decision making. However, the limited access of the farmers to the ground truth information becomes an obstacle to evaluate the crop status under various environmental conditions. Therefore, efforts to establish networks of validation sites with the support of space agencies and/or environmental institutions are required.
Some of the challenges in agricultural remote sensing are related to standartalization of the data coming from different types of sources and different georeferenced systems, which cause problems on image projection and mapping. A space, aerial, and ground integrated structure to manage multiple sources of the remotely sensed crop parameters for agricultural data acquisition can also be the key to accurate visualization and monitoring of the crop status from multiple perspectives. The heterogeneity of satellite products in terms of spatial, spectral, temporal, and radiometric characteristics can also cause accuracy problems and be highly inadequate with the wrong approach. Finally, advanced remote sensing technologies generate a massive amount data of high volume and complexity, increasing the challenges of data storage and computation power, leading to serious issues in data management. For potential users, the wide variety of products can be confusing, and the analysis of the derived data is sometimes still too complicated.
Conclusion
Digital agriculture is developing rapidly, driven by many technological advances in the area of remote sensing, artificial intelligence, and robotic systems. These systems enable farmers to produce comprehensive, accurate, and transparent crop and livestock products, both at the national and regional levels and to get increased yield and quality, minimizing the environmental impact. However, several challenges and limitations, such as accuracy, interoperability, data storage, computation power, and farmers reluctance to adoption, need to be addressed for effective use of these technologies and widespread digital transformation of agriculture.
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KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.
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FULL Paper PDF file:
The Future of Digital Agriculture: Technologies and OpportunitiesBibliography
author
Year
2020
Title
The Future of Digital Agriculture: Technologies and Opportunities
Publish in
in IT Professional, vol. 22, no. 1, pp. 24-28, 1 Jan.-Feb. 2020,
Doi
10.1109/MITP.2019.2963412.
PDF reference and original file: Click here
Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.
Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.
Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.
