Abstract
AbstractThe downside risk of crop production affects the entire supply chain of the agricultural industry nationally and globally. This also has a profound impact on food security, and thus livelihoods, in many parts of the world. The advent of high temporal, spatial and spectral resolution remote sensing platforms, specifically during the last 5 years, and the advancement in software pipelines and cloud computing have resulted in the collating, analysing and application of ‘BIG DATA’ systems, especially in agriculture. Furthermore, the application of traditional and novel computational and machine learning approaches is assisting in resolving complex interactions, to reveal components of ecophysiological systems that were previously deemed either ‘too difficult’ to solve or ‘unseen’. In this review, digital technologies encompass mathematical, computational, proximal and remote sensing technologies. Here, we review the current state of digital technologies and their application in broad-acre cropping systems globally and in Australia. More specifically, we discuss the advances in (i) remote sensing platforms, (ii) machine learning approaches to discriminate between crops and (iii) the prediction of crop phenological stages from both sensing and crop simulation systems for major Australian winter crops. An integrated solution is proposed to allow accurate development, validation and scalability of predictive tools for crop phenology mapping at within-field scales, across extensive cropping areas.
Highlights
SENSING OF CROP T YPE AND CROP PHENOLOGY FROM SPACE With the burgeoning challenges that Earth is currently experiencing, mainly caused by the progressively increase in climate extremes, rapid population growth, reduction in arable land, depletion of, and competition for, natural resources, it is evident that the required increase of food production (>60 % of the current) by 2050 (Alexandratos and Bruinsma 2012) is one of the greatest tests facing humanity
This is extremely important for crops growing in arid and semi-arid areas, such as Australia, where the cropping system is highly volatile due to the variability in climate and frequent extreme weather events including extended droughts and floods (e.g. Chenu et al 2013; Watson et al 2017)
We cover (i) available earth observation (EO) platforms and products for crop-related studies, (ii) analytical approaches and sensor platforms in detecting crop phenology and discriminating between crop types, (iii) the application of machine learning (ML) algorithms in the classification of crop identification and growth dynamics, (iv) the role of crop modelling to augment crop phenology estimates from remote sensing (RS), (v) current limitations, (vi) proposed framework addressing the challenges and (vii) potential implication/s to industry
Summary
With the burgeoning challenges that Earth is currently experiencing, mainly caused by the progressively increase in climate extremes, rapid population growth, reduction in arable land, depletion of, and competition for, natural resources, it is evident that the required increase of food production (>60 % of the current) by 2050 (Alexandratos and Bruinsma 2012) is one of the greatest tests facing humanity. Accurate information on the spatial distribution and growth dynamics of cropping are essential for assessing potential risks to food security, and critical for evaluating the market trends at regional, national and even global levels (Alexandratos and Bruinsma 2012; Orynbaikyzy et al 2019) This is extremely important for crops growing in arid and semi-arid areas, such as Australia, where the cropping system is highly volatile due to the variability in climate and frequent extreme weather events including extended droughts and floods (e.g. Chenu et al 2013; Watson et al 2017). Cereal yields have plateaued over the last three decades in Australia (Potgieter et al 2016), while volatility in total farm crop production is nearly double that of any other agricultural industry (Hatt et al 2012) In this regard, digital technologies, including proximal and remote sensing (RS) systems, have a critical and significant role to play in enhancing food production, sustainability and profitability of production systems. The crop phenology when stress occurs can further exacerbate the spatial and temporal differences in yields (Flohr et al 2017; Dreccer et al 2018a; Whish et al 2020)
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