High-throughput phenotyping technologies have become an increasingly important topic of crop science in recent years. Various sensors and data acquisition approaches have been applied to acquire the phenotyping traits. It is quite confusing for crop phenotyping researchers to determine an appropriate way for their application. In this study, three representative three-dimensional (3D) data acquisition approaches, including 3D laser scanning, multi-view stereo (MVS) reconstruction, and 3D digitizing, were evaluated for maize plant phenotyping in multi growth stages. Phenotyping traits accuracy, post-processing difficulty, device cost, data acquisition efficiency, and automation were considered during the evaluation process. 3D scanning provided satisfactory point clouds for medium and high maize plants with acceptable efficiency, while the results were not satisfactory for small maize plants. The equipment used in 3D scanning is expensive, but is highly automatic. MVS reconstruction provided satisfactory point clouds for small and medium plants, and point deviations were observed in upper parts of higher plants. MVS data acquisition, using low-cost cameras, exhibited the highest efficiency among the three evaluated approaches. The one-by-one pipeline data acquisition pattern allows the use of MVS high-throughput in further phenotyping platforms. Undoubtedly, enhancement of point cloud processing technologies is required to improve the extracted phenotyping traits accuracy for both 3D scanning and MVS reconstruction. Finally, 3D digitizing was time-consuming and labor intensive. However, it does not depend on any post-processing algorithms to extract phenotyping parameters and reliable phenotyping traits could be derived. The promising accuracy of 3D digitizing is a better verification choice for other 3D phenotyping approaches. Our study provides clear reference about phenotyping data acquisition of maize plants, especially for the affordable and portable field phenotyping platforms to be developed.

Stalk lodging is an impediment to improving profitability and production efficiency in maize. Lodging resistance, a comprehensive indicator to appraise genotypes, requires both characterization of mechanical properties in laboratory and investigation of lodging percentage in field. However, in situ characterization of maize lodging resistance still remains poor. The aim of this study was to develop an indicator, named cumulative lodging index (CLI), based on lodging percentages at different wind speeds for evaluating lodging resistance for different maize cultivars, and to evaluate the accuracy and reliability of this method. Different cultivars showed different patterns of lodging percentage along with wind speeds. The failure wind speed (FWS) for maize ranged between 16 and 30 m s−1 across cultivars. The CLI differed between maize cultivars and showed favorable reliability (i.e. nRMSE of 5.38%). Mechanical properties of the third internode did not vary significantly between cultivars. Significant differences in the reduction index (RI) of wind speed sheltered by maize canopy were found between cultivars. Our findings implied that mobile wind machine is powerful in reproducing wind disaster that induce crop lodging. The newly-built CLI was demonstrated to be a more robust indicator than mechanical properties, FWS, and RI when evaluating lodging resistance in terms of both reliability and resolution. This study offers a new perspective for evaluating in situ lodging resistance of crops, and provides technical support for accurate identification of lodging-resistant phenotypic traits.

The quality of planting materials is the foundation for productivity, longevity, and berry quality of perennial grapevines with a long lifespan. Manipulating the nursery light spectrum may speed up the production of healthy and high-quality planting vines but the underlying mechanisms remain elusive. Herein, the effects of different monochromatic lights (green, blue, and red) on grapevine growth, leaf photosynthesis, whole-plant carbon allocation, and transcriptome reprograming were investigated with white light as control. Results showed that blue and red lights were favorable for plantlet growth in comparison with white light. Blue light repressed excessive growth, significantly increased the maximum net photosynthetic rate (Pn) of leaves by 39.58% and leaf specific weight by 38.29%. Red light increased the dry weight of the stem by 53.60%, the starch content of the leaf by 53.63%, and the sucrose content of the stem by 230%. Green light reduced all photosynthetic indexes of the grape plantlet. Photosynthetic photon flux density (PPFD)/Ci–Pn curves indicated that blue light affected photosynthetic rate depending on the light intensity and CO₂ concentration. RNA-seq analysis of different organs (leaf, stem, and root) revealed a systematic transcriptome remodeling and VvCOP1 (CONSTITUTIVELY PHOTOMORPHOGENIC 1), VvHY5 (ELONGATED HYPOCOTYL5), VvHYH (HY5 HOMOLOG), VvELIP (early light-induced protein) and VvPIF3 (PHYTOCHROME INTERACTING FACTOR 3) may play important roles in this shoot-to-root signaling. Furthermore, the correlation network between differential expression genes and physiological traits indicated that VvpsbS (photosystem II subunit S), Vvpsb28 (photosystem II subunit 28), VvHYH, VvSUS4 (sucrose synthase 4), and VvALDA (fructose-bisphosphate aldolase) were pertinent candidate genes in responses to different light qualities. Our results provide a foundation for optimizing the light recipe of grape plantlets and strengthen the understanding of light signaling and carbon metabolism under different monochromatic lights.

As a perennial plant with long productive span of 30–50 years, grapevine may experience cross-lifespan climate change, which can modify wine quality and challenge viticultural sustainability. Therefore, it is essential to evaluate the viticultural suitability by considering both current and future climate conditions. To this end, a maximum entropy model was proposed to delimitate potentially suitable areas for viticulture based on multi-source data in a novel wine region, Ningxia, China, considering both current and future climate conditions. Firstly, we combined traditional data of climate, soil, and topography with remote sensing data to screen predictors that best characterize current geographical distribution of vineyards. Then, we used those predictors to assess current suitability (2001–2020) in Ningxia. The results indicated altitude, aridity index during April–September (K0409), precipitation during July–September (P0709), normalized difference vegetation index during July–September (NDVI0709), soil organic carbon (SOC), and precipitation in September (P09) were key predictors to assess potential suitability for viticulture, and their threshold values ranged from 1075 m to 1648 m, 2.93 to 4.83, 103.1 mm to 164.1 mm, 0.1 to 0.89, 0.07 g/kg to 11 g/kg and 28.4 mm to 45.0 mm, respectively. Suitability maps revealed a total suitable area of 12029 km², among which the highly and moderately suitable areas accounted for 6.1% and 23.1%, respectively. Finally, the alteration in proportion of potential suitable areas due to changing climate was estimated. The potential suitable areas varied from 8742 km² to 10623 km² over the next 40 years (2022–2060) and decreased to 8826–9184 km² under a short-term sustainability (suitable only during current–2040). To further consider long-term and sustainable development of the wine industry (current–2060), total suitable areas dropped by 26.7–29.2% under different climate scenarios compared with current suitable areas (2001–2020). The conclusions provide indispensable guidance for vineyard zoning considering long-term climate change.