Evolution and application of digital technologies to predict crop type and crop phenology in agriculture. Issue 1 (21st May 2021)

• Record Type: Journal Article
• Authors: Potgieter, Andries B; Zhao, Yan; Zarco-Tejada, Pablo J; Chenu, Karine; Zhang, Yifan; Porker, Kenton; Biddulph, Ben; Dang, Yash P; Neale, Tim; Roosta, Fred; Chapman, Scott
• Editors: Long, Steve
• Is Part Of: In silico plants. Volume 3: Issue 1(2021)
• Publication Date: 2021-05-21
• Subjects: Big data -- crop modelling -- digital technologies -- drone -- food security -- machine learning -- precision agriculture -- satellite imagery -- subfield scale -- UAV; Plant physiology -- Periodicals; Botany -- Periodicals; Botany -- Mathematical models -- Periodicals; Crop science -- Periodicals; 580
• DOI: 10.1093/insilicoplants/diab017
• Languages: English
• ISSNs: 2517-5025

Abstract

Abstract: The 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.