Background Aboveground biomass (AGB) is a widely used agronomic parameter for characterizing crop growth status and predicting grain yield. The rapid and accurate estimation of AGB in a non-destructive way is useful for making informed decisions on precision crop management. Previous studies have investigated vegetation indices (VIs) and canopy height metrics derived from Unmanned Aerial Vehicle (UAV) data to estimate the AGB of various crops. However, the input variables were derived either from one type of data or from different sensors on board UAVs. Whether the combination of VIs and canopy height metrics derived from a single low-cost UAV system can improve the AGB estimation accuracy remains unclear. This study used a low-cost UAV system to acquire imagery at 30 m flight altitude at critical growth stages of wheat in Rugao of eastern China. The experiments were conducted in 2016 and 2017 and involved 36 field plots representing variations in cultivar, nitrogen fertilization level and sowing density. We evaluated the performance of VIs, canopy height metrics and their combination for AGB estimation in wheat with the stepwise multiple linear regression (SMLR) and three types of machine learning algorithms (support vector regression, SVR; extreme learning machine, ELM; random forest, RF). Results Our results demonstrated that the combination of VIs and canopy height metrics improved the estimation accuracy for AGB of wheat over the use of VIs or canopy height metrics alone. Specifically, RF performed the best among the SMLR and three machine learning algorithms regardless of using all the original variables or selected variables by the SMLR. The best accuracy ( R 2 = 0.78, RMSE = 1.34 t/ha, rRMSE = 28.98%) was obtained when applying RF to the combination of VIs and canopy height metrics. Conclusions Our findings implied that an inexpensive approach consisting of the RF algorithm and the combination of RGB imagery and point cloud data derived from a low-cost UAV system at the consumer-grade level can be used to improve the accuracy of AGB estimation and have potential in the practical applications in the rapid estimation of other growth parameters.

Low-altitude aerial imaging, an approach that can collect large-scale plant imagery, has grown in popularity recently. Amongst many phenotyping approaches, unmanned aerial vehicles (UAVs) possess unique advantages as a consequence of their mobility, flexibility and affordability. Nevertheless, how to extract biologically relevant information effectively has remained challenging.Here, we present AIRMEASURER, an open-source and expandable platform that combines automated image analysis, machine learning and original algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low-cost UAVs in rice (Oryza sativa) trials.We applied the platform to study hundreds of rice landraces and recombinant inbred lines at two sites, from 2019 to 2021. A range of static and dynamic traits were quantified, including crop height, canopy coverage, vegetative indices and their growth rates. After verifying the reliability of AirMeasurer-derived traits, we identified genetic variants associated with selected growth-related traits using genome-wide association study and quantitative trait loci mapping.We found that the AIRMEASURER-derived traits had led to reliable loci, some matched with published work, and others helped us to explore new candidate genes. Hence, we believe that our work demonstrates valuable advances in aerial phenotyping and automated 2D/3D trait analysis, providing high-quality phenotypic information to empower genetic mapping for crop improvement.

Agricultural heritage sites have been gaining popularity as tourism destinations. The arrival of large numbers of tourists, however, has created serious challenges to these vulnerable ecosystems. In particular, water resources are facing tremendous pressure. Thus, an assessment of tourism water footprint is suggested before promoting sustainable tourism. This paper uses the bottom-up approach to construct a framework on the tourism water footprint of agricultural heritage sites. The tourism water footprint consists of four components, namely accommodation water footprint, diet water footprint, transportation water footprint and sewage dilution water footprint. Yuanyang County, a representative of the Honghe Hani rice terraces, was selected as the study area. Field surveys including questionnaires, interviews and participant observation approaches were undertaken to study the tourism water footprint and water capacity of the heritage site. Based on the results, OPEN ACCESSSustainability 2015, 7 15549 measures to improve the tourism water capacity have been put forward, which should provide references for making policies that aim to maintain a sustainable water system and promote tourism development without hampering the sustainability of the heritage system. The sewage dilution water footprint and the diet water footprint were top contributors to the tourism water footprint of the subject area, taking up 38.33% and 36.15% of the tourism water footprint, respectively, followed by the transportation water footprint (21.47%). The accommodation water footprint had the smallest proportion (4.05%). The tourism water capacity of the heritage site was 14,500 tourists per day. The water pressure index was 97%, indicating that the water footprint was still within the water capacity, but there is a danger that the water footprint may soon exceed the water capacity. As a consequence, we suggest that macro and micro approaches, including appropriate technologies, awareness enhancement and diversified tourism product development throughout the whole year that can alleviate the water pressure at critical times, could be taken to optimize the water management of the heritage sites.

Plant phenomics bridges the gap between traits of agricultural importance and genomic information. Limitations of current field-based phenotyping solutions include mobility, affordability, throughput, accuracy, scalability and the ability to analyse big data collected. Here, we present a large-scale phenotyping solution that combines a commercial backpack LiDAR device and our analytic software, CropQuant-3D, which have been applied jointly to phenotype wheat (Triticum aestivum) and associated 3D trait analysis. The use of LiDAR can acquire millions of 3D points to represent spatial features of crops, and CropQuant-3D can extract meaningful traits from large, complex point clouds. In a case study examining the response of wheat varieties to three different levels of nitrogen fertilisation in field experiments, the combined solution differentiated significant genotype and treatment effects on crop growth and structural variation in canopy, with strong correlations with manual measurements. Hence, we demonstrate that this system could consistently perform 3D trait analysis at a larger scale and more quickly than heretofore possible and addresses challenges in mobility, throughput, and scalability. To ensure our work could reach non-expert users, we developed an open-source graphical user interface for CropQuant-3D. We therefore believe that the combined system is easy-to-use and could be used as a reliable research tool in multi-location phenotyping for both crop research and breeding. Furthermore, together with the fast maturity of LiDAR technologies, the system has the potential for further development in accuracy and affordability, contributing to the resolution of the phenotyping bottleneck and exploiting available genomic resources more effectively.