Agronomy

23 pages, 4041 KB

Open AccessArticle

Detection of Phosphorus Deficiency Using Hyperspectral Imaging for Early Characterization of Asymptomatic Growth and Photosynthetic Symptoms in Maize

by Sutee Kiddee, Chalongrat Daengngam, Surachet Wongarrayapanich, Jing Yi Lau, Acga Cheng and Lompong Klinnawee

Agronomy 2026, 16(8), 772; https://doi.org/10.3390/agronomy16080772 (registering DOI) - 8 Apr 2026

Abstract

Phosphorus (P) deficiency severely limits maize growth and yield, yet early detection remains challenging, as visible symptoms appear only after prolonged starvation. This study evaluated the capability of hyperspectral imaging (HSI) combined with machine learning to detect P deficiency in maize seedlings at both symptomatic and pre-symptomatic stages. Two greenhouse experiments were conducted: a long-term pot system under high and low P conditions and a short-term hydroponic experiment with three P concentrations of 500, 100, and 0 μmol/L phosphate (Pi). After long-term P deficiency, significant reductions in shoot biomass and Pi content were observed, while root biomass increased and nutrient profiles were altered. Hyperspectral signatures revealed distinct wavelength-specific differences across visible, red-edge, and near-infrared (NIR) regions, with P-deficient leaves showing lower reflectance in green and NIR regions but higher reflectance in the red band. A multilayer perceptron machine learning model achieved 99.65% accuracy in discriminating between P treatments. In the short-term experiment, P deficiency significantly reduced tissue Pi content within one week without affecting pigment composition or photosynthetic parameters. Despite the absence of visible symptoms, hyperspectral measurements detected subtle spectral changes, particularly in older leaves, enabling classification accuracies of 80.71–84.56% in the first week and 85.88–90.98% in the second week of P treatment. Conventional vegetation indices showed weak correlations with Pi content and failed to detect early P deficiency. These findings demonstrate that HSI combined with machine learning can effectively detect P deficiency before visible symptoms emerge, offering a non-destructive, rapid diagnostic tool for precision nutrient management in maize production systems. Full article

(This article belongs to the Special Issue Nutrient Enrichment and Crop Quality in Sustainable Agriculture)

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25 pages, 4466 KB

Open AccessArticle

Integrated Multi-Omics Profiling Elucidates the Molecular Mechanisms of Salt Stress Adaptation in Tartary Buckwheat (Fagopyrum tataricum)

by Yi Yuan, Zilong Liu, Yunzhe He, Liming Men, Zhihui Chen, Guoqing Dong and Dengxiang Du

Agronomy 2026, 16(8), 771; https://doi.org/10.3390/agronomy16080771 (registering DOI) - 8 Apr 2026

Abstract

Soil salinization is a major threat to global crop production. Tartary buckwheat (Fagopyrum tataricum), valued for its hardiness in marginal environments, provides an excellent system for studying plant salt tolerance. Using an integrated multi-omics approach, we deciphered the physiological, metabolic, and transcriptional responses of Tartary buckwheat to prolonged NaCl stress. Physiological profiling confirmed membrane damage alongside osmotic adjustment via proline accumulation and a phased antioxidant response. Metabolomics revealed extensive reprogramming, with dynamic enrichment in pathways of flavonoid biosynthesis, lipid metabolism, and the TCA cycle. Transcriptomics delineated a time-specific cascade from early signaling to late defense activation. Critical regulators within ABA and MAPK signaling pathways showed fine-tuned, divergent expression; for instance, SnRK2.3 was suppressed while specific PP2Cs were induced, and FtMAPK10 was dramatically up-regulated. Integrated analysis demonstrated coordinated induction of osmoprotectant synthesis (e.g., galactinol and betaine pathways) and a rewiring of central carbon metabolism. Our findings reveal a sophisticated, multi-layered adaptation strategy in Tartary buckwheat, integrating enhanced osmolyte production, antioxidant defense, membrane remodeling, and metabolic reprogramming, orchestrated by key signaling networks. This study provides a comprehensive molecular framework for salt tolerance and identifies valuable genetic targets for improving crop resilience. Full article

(This article belongs to the Special Issue Germplasm Conservation and Genetic Improvement in Tropical and Subtropical Crops)

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26 pages, 5629 KB

Open AccessArticle

Effect of Red–Blue Light Ratios on Leaf Development and Steviol Glycoside Production at Different Growth Stages in Hydroponic Stevia

by Cheng Tai Chou, Vivian Christabel, Mai Anh Le, Min-Lang Tsai and Shang-Ta Wang

Agronomy 2026, 16(8), 770; https://doi.org/10.3390/agronomy16080770 - 8 Apr 2026

Abstract

Stevia is a natural source of high-intensity sweeteners, collectively known as steviol glycosides (SG), which are approximately 300 times sweeter than sucrose and widely used as sugar substitutes. This study examines the impact of five different red-to-blue (R:B) light ratios on SG content and yield in hydroponic Stevia across four growth stages. Results indicate that the highest and lowest leaf dry weights were recorded in the R1B0 (R:B = 1:0) and R0B1 (R:B = 0:1) groups, at 2.88 and 1.98 g/plant, respectively, reflecting a 45.45% difference. The total SG content in dried leaves was highest in R0B1 (196.32 mg/g) and lowest in R1B0 (115.16 mg/g), with a 70.48% variation. The highest and lowest total SG yields (YSG) per square meter were observed in R0B1 (46.56 g/m²) and R50B37 (35.70 g/m²), differing by 30.42%. Stage-specific optimal YSG values were identified, with designated growth stages P1 (early vegetative growth phase), P2 (early leaf development phase), and P3 (late leaf development phase) favoring R4B1 and P4 (leaf senescence phase) favoring R0B1. These findings suggest an optimized lighting strategy for the four growth stages of hydroponic Stevia, sequentially applying R4B1, R4B1, R4B1 and R0B1 to enhance biomass accumulation and SG production at different developmental stages. Full article

(This article belongs to the Special Issue Advancement in Controlled Environment Agriculture (CEA) Automation and Crop Management—2nd Edition)

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42 pages, 10717 KB

Open AccessReview

Towards Stress-Resilient Canola via Genetic Engineering Approaches

by Ali Ijaz Ahmed, Aldrin Y. Cantila and Sheng Chen

Agronomy 2026, 16(8), 769; https://doi.org/10.3390/agronomy16080769 - 8 Apr 2026

Abstract

Climate change has adversely affected grain production and quality of canola, the second-largest oilseed crop, which contributes 13–16% of total vegetable oil. Multiple biotic and abiotic stresses significantly limit canola production due to rapid climate change, and conventional breeding alone is insufficient to meet global demand. Therefore, several advanced biotechnologies have been developed to cope with this change. Among these, genetic modification, gene editing, and RNA interference are particularly significant for rapid cultivar development in a cost-effective, efficient, and convenient way. Recent findings in gene editing applications have revealed “prospective sites”, highlighting regions amenable to precise editing without compromising canola plant growth or development. Pan-genome analyses have further guided gene editing target selection, enabling the validation of key stress-resilience genes across diverse canola cultivars, while the CRISPR-epigenetic regulatory connection enables targeted control of gene expression and trait modulation. A hypothetical application of genomic selection is also suggested, which could complement gene editing to accelerate the development of superior cultivars. Accordingly, this review focuses on the latest studies of genetic modification, gene editing, and RNA interference to strengthen canola resilience under rapid climate change and discusses the major concerns. Taken together, these genome-editing strategies offer precise approaches for improving biotic and abiotic stress tolerance, although careful consideration of both off-target effects and regulatory compliance remains essential for their practical implementation in canola improvement. Full article

(This article belongs to the Special Issue Crop Agronomic Traits and Performances Under Stress)

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Agronomy, Volume 16, Issue 8 (April-2 2026) – 4 articles

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