The maximum quantum efficiency of photosystem II (Fv/Fm) is a widely used indicator of photosynthetic health in plants. Remote sensing of Fv/Fm using MS (multispectral) and RGB imagery has the potential to enable high-throughput screening of plant health in agricultural and ecological applications. This study aimed to estimate Fv/Fm in spring wheat at an experimental base in Hanghou County, Inner Mongolia, from 2020 to 2021. RGB and MS images were obtained at the wheat flowering stage using a Da-Jiang Phantom 4 multispectral drone. A total of 51 vegetation indices were constructed, and the measured Fv/Fm of wheat on the ground was obtained simultaneously using a Handy PEA plant efficiency analyzer. The performance of 26 machine learning algorithms for estimating Fv/Fm using RGB and multispectral imagery was compared. The findings revealed that a majority of the multispectral vegetation indices and approximately half of the RGB vegetation indices demonstrated a strong correlation with Fv/Fm, as evidenced by an absolute correlation coefficient greater than 0.75. The Gradient Boosting Regressor (GBR) was the optimal estimation model for RGB, with the important features being RGBVI and ExR. The Huber model was the optimal estimation model for MS, with the important feature being MSAVI2. The Automatic Relevance Determination (ARD) was the optimal estimation model for the combination (RGB + MS), with the important features being SIPI, ExR, and VEG. The highest accuracy was achieved using the ARD model for estimating Fv/Fm with RGB + MS vegetation indices on the test sets (Test set MAE = 0.019, MSE = 0.001, RMSE = 0.024, R² = 0.925, RMSLE = 0.014, MAPE = 0.026). The combined analysis suggests that extracting vegetation indices (SIPI, ExR, and VEG) from RGB and MS remote images by UAV as input variables of the model and using the ARD model can significantly improve the accuracy of Fv/Fm estimation at flowering stage. This approach provides new technical support for rapid and accurate monitoring of Fv/Fm in spring wheat in the Hetao Irrigation District.

The timely and accurate mapping of crop types is crucial for agricultural insurance, futures, and assessments of food security risks. However, crop mapping is currently focused on the post-harvest period, and less attention has been paid to early crop mapping. In this study, the feasibility of using Sentinel-1 (S1) and Sentinel-2 (S2) data for the earliest identifiable time (EIT) for major crops (sunflower, maize, spring wheat, and melon) was explored in the Hetao Irrigation District (HID) of China, based on the Google Earth Engine (GEE) platform. An early crop identification strategy based on the Random Forest (RF) model for HID was proposed, and the performance of the model transfer was evaluated. First, the median synthesis, linear shift interpolation, and the Savitzky–Golay (SG) filter methods were used to reconstruct the time series of S1 and S2. Subsequently, the sensitivity of different input features, time intervals, and data integration to different early crop identifications was evaluated based on the RF model. Finally, the model with optimal parameters was evaluated in terms of its transfer capacity and used for the early mapping of crops in the HID area. The results showed that the features extracted from S2 images synthesized at 10-day intervals performed well in obtaining crop EITs. Sunflower, maize, spring wheat, and melon could be identified 90, 90, 70, and 40 days earlier than the harvest date. The identification accuracy, measured by the F1-score, could reach 0.97, 0.95, 0.98, and 0.90, respectively. The performance of the model transfer is good, with the F1-score decreasing from 0 to 0.04 and no change in EIT for different crops. It was also found that the EIT of crops obtained using S1 data alone was 50–90 days later than that obtained using S2 data alone. Additionally, when S1 and S2 were used jointly, S1 data contributed little to early crop identification. This study highlights the potential of early crop mapping using satellite data, which provides a feasible solution for the early identification of crops in the HID area and valuable information for food security assurance in the region.

Soil salinization is a global agro-ecological problem and a major factor impeding agricultural development. Planting salt-tolerant plants to improve saline soils offers both ecological and economic benefits. Currently, there are few studies addressing the combined effects of salt and alkali stress. Quinoa is known for its salinity tolerance. However, research has predominantly focused on the effects of salinity stress on quinoa’s morphology and physiology, with its molecular mechanisms remaining unclear. To better understand quinoa’s response mechanisms to salinity and alkali stress, we employed RNA-seq technology to analyze transcriptomes under these conditions. We identified 1833 differentially expressed genes (DEGs) under salt stress and 2233 DEGs under alkali stress. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations revealed that quinoa responds to salt and alkali stress through similar mechanisms. Both stresses promoted sucrose synthesis, starch synthesis and catabolism, which increased the osmotic potential of quinoa leaves. Additionally, there was a regulation of the down-regulated expression of the abscisic acid receptor PYR/PYL and the up-regulated expression of the serine/threonine protein kinase (PP2C) gene in the ABA signaling pathway. Contrasting with salt tolerance, the mechanism specific to quinoa’s alkalinity tolerance involves the up-regulation of the citric acid cycle via an active γ-aminobutyric acid (GABA) branch, enhancing quinoa’s energy metabolism. In summary, our transcriptome analysis revealed key regulatory mechanisms in quinoa’s response to saline and alkaline stress. This study deepens the understanding of quinoa’s stress response mechanisms and provides theoretical references for the biological improvement of salinized soils.

Drought stress seriously threatens human food security, and enhancing crops’ drought tolerance is an urgent problem to be solved in breeding. Quinoa is known for its high nutritional value and strong drought tolerance, but its molecular mechanism in response to drought stress is still unclear. In this study, we used drought-tolerant (D2) and drought-sensitive (ZK1) quinoa varieties, and PEG-6000 was used to simulate drought stress in quinoa seedlings. Phenotypic and physiological biochemical indicators were measured during the seedling stage, and LC-MS was used for a metabolite analysis of drought stress to explore the drought tolerance mechanism of quinoa under drought stress. With the intensification of drought stress, chlorophyll content gradually increased, and D2 reached its maximum at W4, an increase of 49.85% compared with W1. The total chlorophyll content, photosynthesis rate, and stomatal conductance of ZK1 were significantly lower than D2 under moderate and severe drought stress. Metabolomic results showed that a total of 1295 positive ion mode (pos) metabolites and 914 negative ion mode (neg) metabolites were identified. Of these, 12(R)-HETE, phosphatidylcholine, monogalactose diester (MGDG), and stachyose up-regulated expression under drought stress. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that unsaturated fatty acid biosynthesis and glycerophospholipid metabolism pathways were significantly enriched. In summary, our results elucidate that quinoa responds to drought stress by accumulating chlorophyll and sugars, activating unsaturated fatty acid metabolism, and protecting the photosynthetic system. These findings provide new insights for the breeding of drought-tolerant quinoa varieties and the study of drought tolerance mechanisms.

Non-destructive monitoring of chlorophyll content through Soil Plant Analysis Development (SPAD) values is essential for precision agriculture in water-limited regions. However, current estimation methods using spectral information alone face significant limitations in sensitivity and transferability under variable irrigation conditions. While integrating canopy structural parameters with spectral data represents a promising solution, systematic investigation of this approach throughout the entire growth cycle of spring wheat under different irrigation regimes remains limited. This study evaluated three machine learning algorithms (Random Forest, Support Vector Regression, and Multi-Layer Perceptron) for SPAD estimation in spring wheat cultivated in the Hetao Irrigation District. Using a split-plot experimental design with two irrigation treatments (conventional: four irrigations; limited: two irrigations) and five nitrogen levels (0–300 kg·ha⁻¹), we analyzed ten vegetation indices derived from Unmanned Aerial Vehicle (UAV) multispectral imagery, with and without Leaf Area Index (LAI) integration, across six growth stages. Results demonstrated that incorporating LAI significantly improved SPAD estimation accuracy across all algorithms, with Random Forest exhibiting the most substantial enhancement (R² increasing from 0.698 to 0.842, +20.6%; RMSE decreasing from 5.025 to 3.640, −27.6%). Notably, LAI contributed more significantly to SPAD estimation under limited irrigation conditions (R² improvement: +17.6%) compared to conventional irrigation (+11.0%), indicating its particular value for chlorophyll monitoring in water-stressed environments. The Green Normalized Difference Vegetation Index (GNDVI) emerged as the most important predictor (importance score: 0.347), followed by LAI (0.213), confirming the complementary nature of spectral and structural information. These findings provide a robust framework for non-destructive SPAD estimation in spring wheat and highlight the importance of integrating canopy structural information with spectral data, particularly in water-limited agricultural systems.

Quinoa (Chenopodium quinoa Willd.) is an annual broadleaf plant belonging to the Amaranthaceae family. It is a nutritious food crop and is considered to be drought-tolerant, but drought is still one of the most important abiotic stress factors limiting its yield. Quinoa responses to drought are related to drought intensity and genotype. This study used two different drought-responsive quinoa cultivars, LL1 (drought-tolerant) and ZK1 (drought-sensitive), to reveal the important mechanisms of drought response in quinoa by combining physiological, transcriptomic, and metabolomic analyses. The physiological analysis indicated that Chla/Chlb might be important for drought tolerance in quinoa. A total of 1756 and 764 differentially expressed genes (DEGs) were identified in LL1 and ZK1, respectively. GO (Gene Ontology) enrichment analysis identified 52 common GO terms, but response to abscisic acid (GO:0009737) and response to osmotic stress (GO:0006970) were only enriched in LL1. KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis revealed that glycerophospholipid metabolism (ko00564) and cysteine and methionine metabolism (ko00270) ranked at the top of the list in both cultivars. A total of 1844 metabolites were identified by metabolomic analysis. “Lipids and lipid-like” molecules had the highest proportions. The DEMs in LL1 and ZK1 were mainly categorized 6 and 4 Human Metabolome Database (HMDB) superclasses, respectively. KEGG analysis revealed that the ‘α-linolenic acid metabolism’ was enriched in both LL1 and ZK1. Joint KEGG analysis also revealed that the ‘α-linolenic acid metabolism’ pathway was enriched by both the DEGs and DEMs of LL1. There were 17 DEGs and 8 DEMs enriched in this pathway, and methyl jasmonate (MeJA) may play an important role in the drought response of quinoa. This study will provide information for the identification of drought resistance in quinoa, research on the molecular mechanism of drought resistance, and genetic breeding for drought resistance in quinoa.