Search Results (9)

Large-scale rainfed cropping systems (broadacre agriculture) face intensifying climate and resource stresses that undermine yield stability and farm livelihoods. Remote sensing (RS) offers critical tools for improving resilience by monitoring crop performance—productivity, phenology, and environmental stress—across large areas and timeframes. This review aims to synthesize methodological advances over the past two decades in applying RS for broadacre crop monitoring and to identify key challenges and integration opportunities. Peer-reviewed studies across diverse crops and regions were systematically examined to evaluate the strengths, limitations, and emerging trends across the three RS application themes. The review finds that (1) RS enables spatially explicit yield estimation from regional to paddock scales, with vegetation indices (VIs) and phenology-adjusted metrics closely correlated with yield. (2) Time-series analyses of RS data effectively capture phenological transitions critical for forecasting, supported by advances in curve fitting, sensor fusion, and machine learning. (3) Thermal and multispectral indices support the early detection of abiotic (drought, heat, salinity) and biotic (pests, disease) stresses, though specificity remains limited. Across themes, methodological silos and sensor integration barriers hinder holistic application. Emerging approaches, such as multi-sensor/scale fusion, RS–crop model data assimilation, and operational and big data integration, provide promising pathways toward resilience-focused decision support. Future research should define quantifiable resilience metrics, cross-theme predictive integration, and accessible tools to guide climate adaptation. Full article

Root-lesion nematodes (Pratylenchus spp.) reduce the yield and quality of cereal crops in Australia. Eleven of the ~90 species characterised are present in Australia, with those determined as economic pests of broadacre agriculture costing an estimated AUD 250 million annually. Two species, P. curvicauda and P. quasitereoides, recently re-described, were isolated from fields located in the grainbelt of Western Australia, but little is known about their distribution in the region surveyed in this study. To investigate this and possible co-infestations with other Pratylenchus spp., we surveyed seven commercial wheat, barley, and oat farms near Katanning, Cancanning, Kenmare, Duranillin, Darkan, and a barley seed-bulk nursery near Manjimup, all in the southwest grainbelt of Western Australia. Morphological and molecular characterisation of Pratylenchus spp. extracted from soil and plant roots indicated all fields surveyed were infested. Both P. quasitereoides and P. curvicauda were present as single or mixed populations with P. penetrans and/or P. neglectus, although they were not found in the same field. Analyses of the D2–D3 sequences of the identified nematodes indicated that the species found in Australia were distinct, particularly P. quasitereoides and P. curvicauda. This work suggests P. curvicauda is likely to be present more widely in the WA grainbelt. Expanding molecular diagnostic testing for Pratylenchus species in the region to account for both nematodes is urgently needed so effective management can be implemented. Full article

Protected cropping produces more food per land area than field-grown crops. Protected cropping includes low-tech polytunnels utilizing protective coverings, medium-tech facilities with some environmental control, and high-tech facilities such as fully automated glasshouses and indoor vertical farms. High crop productivity and quality are maintained by using environmental control systems and advanced precision phenotyping sensor technologies that were first developed for broadacre agricultural and can now be utilized for protected-cropping applications. This paper reviews the state of the global protected-cropping industry and current precision phenotyping methodology and technology that is used or can be used to advance crop productivity and quality in a protected growth environment. This review assesses various sensor technologies that can monitor and maintain microclimate parameters, as well as be used to assess plant productivity and produce quality. The adoption of precision phenotyping technologies is required for sustaining future food security and enhancing nutritional quality. Full article

Intercropping is considered by its advocates to be a sustainable, environmentally sound, and economically advantageous cropping system. Intercropping systems are complex, with non-uniform competition between the component species within the cropping cycle, typically leading to unequal relative yields making evaluation difficult. This paper is a review of the main existing metrics used in the scientific literature to assess intercropping systems. Their strengths and limitations are discussed. Robust metrics for characterising intercropping systems are proposed. A major limitation is that current metrics assume the same management level between intercropping and monocropping systems and do not consider differences in costs of production. Another drawback is that they assume the component crops in the mixture are of equal value. Moreover, in employing metrics, many studies have considered direct and private costs and benefits only, ignoring indirect and social costs and benefits of intercropping systems per se. Furthermore, production risk and growers’ risk preferences were often overlooked. In evaluating intercropping advantage using data from field trials, four metrics are recommended that collectively take into account all important differences in private costs and benefits between intercropping and monocropping systems, specifically the Land Equivalent Ratio, Yield Ratio, Value Ratio and Net Gross Margin. Full article

Hyperspectral imaging spectrometers mounted on unmanned aerial vehicle (UAV) can capture high spatial and spectral resolution to provide cotton crop nitrogen status for precision agriculture. The aim of this research was to explore machine learning use with hyperspectral datacubes over agricultural fields. Hyperspectral imagery was collected over a mature cotton crop, which had high spatial (~5.2 cm) and spectral (5 nm) resolution over the spectral range 475–925 nm that allowed discrimination of individual crop rows and field features as well as a continuous spectral range for calculating derivative spectra. The nominal reflectance and its derivatives clearly highlighted the different treatment blocks and were strongly related to N concentration in leaf and petiole samples, both in traditional vegetation indices (e.g., Vogelman 1, R² = 0.8) and novel combinations of spectra (R² = 0.85). The key hyperspectral bands identified were at the red-edge inflection point (695–715 nm). Satellite multispectral was compared against the UAV hyperspectral remote sensing’s performance by testing the ability of Sentinel MSI to predict N concentration using the bands in VIS-NIR spectral region. The Sentinel 2A Green band (B3; mid-point 559.8 nm) explained the same amount of variation in N as the hyperspectral data and more than the Sentinel Red Edge Point 1 (B5; mid-point 704.9 nm) with the lower 10 m resolution Green band reporting an R² = 0.85, compared with the R² = 0.78 of downscaled Sentinel Red Edge Point 1 at 5 m. The remaining Sentinel bands explained much lower variation (maximum was NIR at R² = 0.48). Investigation of the red edge peak region in the first derivative showed strong promise with RIDAmid (R² = 0.81) being the best index. The machine learning approach narrowed the range of bands required to investigate plant condition over this trial site, greatly improved processing time and reduced processing complexity. While Sentinel performed well in this comparison and would be useful in a broadacre crop production context, the impact of pixel boundaries relative to a region of interest and coarse spatial and temporal resolution impacts its utility in a research capacity. Full article

Research has shown the multitude of applications that Internet of Things (IoT), cloud computing, and forecast technologies present in every sector. In agriculture, one application is the monitoring of factors that influence crop development to assist in making crop management decisions. Research on the application of such technologies in agriculture has been mainly conducted at small experimental sites or under controlled conditions. This research has provided relevant insights and guidelines for the use of different types of sensors, application of a multitude of algorithms to forecast relevant parameters as well as architectural approaches of IoT platforms. However, research on the implementation of IoT platforms at the commercial scale is needed to identify platform requirements to properly function under such conditions. This article evaluates an IoT platform (IRRISENS) based on fully replicable microservices used to sense soil, crop, and atmosphere parameters, interact with third-party cloud services for scheduling irrigation and, potentially, control irrigation automatically. The proposed IoT platform was evaluated during one growing season at four commercial-scale farms on two broadacre irrigated crops with very different water management requirements (rice and cotton). Five main requirements for IoT platforms to be used in agriculture at commercial scale were identified from implementing IRRISENS as an irrigation support tool for rice and cotton production: scalability, flexibility, heterogeneity, robustness to failure, and security. The platform addressed all these requirements. The results showed that the microservice-based approach used is robust against both intermittent and critical failures in the field that could occur in any of the monitored sites. Further, processing or storage overload caused by datalogger malfunctioning or other reasons at one farm did not affect the platform’s performance. The platform was able to deal with different types of data heterogeneity. Since there are no shared microservices among farms, the IoT platform proposed here also provides data isolation, maintaining data confidentiality for each user, which is relevant in a commercial farm scenario. Full article

The agricultural industry suffers from a significant amount of food waste, some of which originates from an inability to apply site-specific management at the farm-level. Snap bean, a broad-acre crop that covers hundreds of thousands of acres across the USA, is not exempt from this need for informed, within-field, and spatially-explicit management approaches. This study aimed to assess the utility of machine learning algorithms for growth stage and pod maturity classification of snap bean (cv. Huntington), as well as detecting and discriminating spectral and biophysical features that lead to accurate classification results. Four major growth stages and six main sieve size pod maturity levels were evaluated for growth stage and pod maturity classification, respectively. A point-based in situ spectroradiometer in the visible-near-infrared and shortwave-infrared domains (VNIR-SWIR; 400–2500 nm) was used and the radiance values were converted to reflectance to normalize for any illumination change between samples. After preprocessing the raw data, we approached pod maturity assessment with multi-class classification and growth stage determination with binary and multi-class classification methods. Results from the growth stage assessment via the binary method exhibited accuracies ranging from 90–98%, with the best mathematical enhancement method being the continuum-removal approach. The growth stage multi-class classification method used raw reflectance data and identified a pair of wavelengths, 493 nm and 640 nm, in two basic transforms (ratio and normalized difference), yielding high accuracies (~79%). Pod maturity assessment detected narrow-band wavelengths in the VIS and SWIR region, separating between not ready-to-harvest and ready-to-harvest scenarios with classification measures at the ~78% level by using continuum-removed spectra. Our work is a best-case scenario, i.e., we consider it a stepping-stone to understanding snap bean harvest maturity assessment via hyperspectral sensing at a scalable level (i.e., airborne systems). Future work involves transferring the concepts to unmanned aerial system (UAS) field experiments and validating whether or not a simple multispectral camera, mounted on a UAS, could incorporate < 10 spectral bands to meet the need of both growth stage and pod maturity classification in snap bean production. Full article

The FAO estimates that more than 800 million people engage in urban agriculture producing more than 15% of the world’s food. Recently, there has been a resurgence of interest in urban agriculture in many wealthy, developed cities, with new technology and agro-architecture being employed to grow food in cities at commercial scale. This has been accompanied by an increase in media coverage. Big claims are being made, including that urban agriculture can help solve food security for growing urban populations, decrease greenhouse emissions, ‘climate proof’ farms, and provide chemical free food with no risk of pests and diseases. Many of these claims need to be rigorously tested to ensure that sound investments can be made in enterprises that are financially viable and capable of delivering on claims of social and environmental benefits. Traditionally, agricultural researchers have provided biological, chemical, physical, economic and social research help broad-acre and horticulture farming increases productivity and decrease risk. Urban agriculture needs similar support as the industry grows and develops around the world. There are opportunities to improve crop yields and quality by pairing advancements in environmental controls, phenomics and automation with breeding efforts to adapt traits for architecture, development and quality (taste and nutrition) allowing a more diverse set of crops to be grown in controlled-environment farms. CSIRO is looking to apply our establish capability and skills to support the urban and vertical farming industry to contribute to the nutrition of city dwellers as urban populations continue to rise. Full article

Planners, designers, citizens, and governmental agencies are interested in creating environments that are sustainable and fulfill a wide range of economic, ecological, aesthetic, functional, and cultural expectations for stakeholders. There are numerous approaches and proposals to create such environments. One vision is the 1934 “Broadacre City” proposed by Frank Lloyd Wright for the Taliesin, Wisconsin area that was never implemented. Frank Lloyd Wright’s vision integrated transportation, housing, commercial, agricultural, and natural areas in a highly diverse pattern forming a vast urban savanna complex. He also applied his “Broadacre City” idea to the 1942 Cooperative Homesteads Community Project in Detroit, Michigan, another un-built project. This vision concerning the composition of the urban environment may be conceptually realized in the ongoing gray-field reclamation in suburban Detroit, Michigan. Recent science-based investigations, concerning the metrics to measure and evaluate the quality of designed spaces, suggest that this “Broadacre City” approach may have great merit and is highly preferred over past spatial treatments (p ≤ 0.05). These metrics explain 67 to 80% of the variance concerning stakeholder expectations and are highly definitive (p < 0.001). Full article