Applications of geospatial technologies for precision agriculture

Mobushir R. Khan, Richard A. Crabbe, Naeem A. Malik, Lachlan O'Meara

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Agriculture remains one of the most important concerns for sustainable livelihood and employment. Precision Agriculture (PA) offers a sustainable management solution to meet the increasingly high demand of food and raw materials. PA is also believed to increase safety of the environment, reduce climate change effects, and improve food security. Farmers need to make timely decisions throughout the agricultural life cycle, that is, from paddock to plate. Crop management (from sowing till harvesting) is an important component of this cycle and needs apt decision by the farmers. Such decisions must be timely and precise, for example, how much of weedicide needs to be sprayed and where exactly in the fields it is needed. Geospatial technologies assist in identifying variabilities in soil, weather, water, and crop performance. These are important crop limiting factors and need to be addressed through various management operations. Geospatial technologies in combination with Internet of Things (IoT) guides farmers not only to identify these factors but also provide support in making optimal decisions. This process makes agriculture a digital entity and thus provides considerable information about the land characteristics of individual farms. Remotely sensed imagery is frequently used for mapping agricultural land use, crop yield prediction, and stress monitoring. Remote and in-situ sensors have been developed and used for mapping and monitoring of soil and water characteristics such as electrical conductivity, soil moisture, and microclimate of plants. This information is transformed into knowledge through using machine learning techniques. The potential of these machine learning techniques to monitor various types of crop stresses has been explored to a large extent during past 10 years and more recently deep learning techniques are being developed owing to the rapid advancements in computing capabilities. This chapter, firstly, introduces the need of PA in response to economic and environmental impacts of increased food production to feed about eight billion people. While introducing its need, we argue that PA needs to be adopted as a management philosophy. Secondly, enabling geospatial technologies including Geographic Information System (GIS) and Remote Sensing (RS) has been discussed in relation to their utility in PA. After giving the reader an idea about the magnitude of data obtained using GIS and RS techniques, big data analytics have been presented to show its potential for operationalizing PA. Thirdly, a state-of-the-art conceptual system of PA is described along with the practical use of geospatial technologies for its various components with practical examples from Australian Center for International Agriculture Research's (ACIAR) funded projects. However, the user can be doubtful when it comes to data sharing. Lastly, survey results have been shared with the readers to apprise them of the use of technology in agriculture relevant to PA components.

Original languageEnglish
Title of host publicationPrecision Agriculture
Subtitle of host publicationEvolution, Insights and Emerging Trends
PublisherElsevier
Pages71-83
Number of pages13
ISBN (Electronic)9780443189531
ISBN (Print)9780443189548
DOIs
Publication statusPublished - 1 Jan 2023
Externally publishedYes

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