Editorial: Emerging contaminants and their effect on agricultural crops

Cotton production is a crucial agricultural industry, a raw material source for the textiles sector and a major source of livelihood for more than 30 million farmers globally. The yield and quality of cotton (Gossypium) are influenced by different types of stress and diseases. Deep Learning as a solution for disease prevention, detection, and management can increase the yield, reduce the cost and improve the quality of crop. This study presents a robust method using 10-fold cross-validation with the YOLOv8 DL model for precise cotton leaf disease recognition. The k-fold cross-validation mitigates overfitting by training the model on diverse data subsets, which leads to enhanced generalizability while ensuring reliable performance. The proposed method achieved 99.60% and 100% as Top_1 and Top_5 accuracy, respectively. The method also achieved a recall of 99.53%, a precision of 99.53%, and an F1 score of 99.60%. During 10 trials, the method consistently performed with an average. Top_1 and Top_5 accuracy of 98.41% and 100% respectively, recall 98.53%, precision 98.39% and F1 score 98.42%.This study is among the first to apply YOLOv8 classification with 10-fold cross-validation for multi-class cotton leaf disease identification using field-captured images.

Helicases function as key enzymes in salinity stress tolerance, and the role and function of PDH45 (pea DNA helicase 45) in stress tolerance have been reported in different crops with selectable markers, raising public and regulatory concerns. In the present study, we developed five lines of marker-free PDH45-overexpressing transgenic lines of rice (Oryza sativa L. cv. IR64). The overexpression of PDH45 driven by CaMV35S promoter in transgenic rice conferred high salinity (200 mM NaCl) tolerance in the T₁ generation. Molecular attributes such as PCR, RT-PCR, and Southern and Western blot analyses confirmed stable integration and expression of the PDH45 gene in the PDH45-overexpressing lines. We observed higher endogenous levels of sugars (glucose and fructose) and hormones (GA, zeatin, and IAA) in the transgenic lines in comparison to control plants (empty vector (VC) and wild type (WT)) under salt treatments. Furthermore, photosynthetic characteristics such as net photosynthetic rate (Pn), stomatal conductance (gs), intercellular CO₂ (Ci), and chlorophyll (Chl) content were significantly higher in transgenic lines under salinity stress as compared to control plants. However, the maximum primary photochemical efficiency of PSII, as an estimated from variable to maximum chlorophyll a fluorescence (Fv/Fm), was identical in the transgenics to that in the control plants. The activities of antioxidant enzymes, such as catalase (CAT), ascorbate peroxidase (APX), glutathione reductase (GR), and guaiacol peroxidase (GPX), were significantly higher in transgenic lines in comparison to control plants, which helped in keeping the oxidative stress burden (MDA and H₂O₂) lesser on transgenic lines, thus protecting the growth and photosynthetic efficiency of the plants. Overall, the present research reports the development of marker-free PDH45-overexpressing transgenic lines for salt tolerance that can potentially avoid public and biosafety concerns and facilitate the commercialization of genetically engineered crop plants.

As a signaling molecule, calcium plays a vital role in the intracellular signaling in plants in response to various physiological and environmental factors. Signaling processes start just after stimulus-receptor interaction, and these signals are relayed and decoded by a range of calcium-binding proteins. The majority of calcium-binding proteins comprise the EF-hand motif with a helix-loop-helix structure. This study reports genome-wide analysis of EF-hand (SiEF-hand) gene family in foxtail millet [Setaria italica (L.) P. Beauv.]. The analysis resulted in the identification of 180 SiEF-hand genes. Further, we carried out phylogenetic, gene structure and motif analysis that resulted in clustering of the SiEF-hand genes in 13 clades broadly as per different subfamilies - calmodulins (CaMs), calmodulins-like proteins (CMLs), calcium-dependent proteins (CDPKs), calcineurin B-like (CBLs), respiratory burst oxidase homologs (Rboh) and others. The domain architecture analysis highlighted 24 different domain compositions. Further, SiEF-hand genes are mapped to all nine chromosomes with uneven distribution. Cis-regulatory element analysis showed the presence of multiple stress and hormone-responsive elements in the promoter region of SiEF-hand genes. The most abundant cis-regulatory elements in SiEF-hand genes are ABRE, TGACG-motif, G-box, MYC, and MYB. Expression profiling of SiEF-hand genes in different tissues leaf, root, stem, and tassel inflorescence revealed differential expression patterns. Further, 8 SiEF-hand genes exhibited tissue-specific expression - four are expressed in roots and four in tassel inflorescences. In a nutshell, the present study provides a detailed insight into the SiEF-hand gene family and may serve as a base for further characterization of Ca²⁺ sensors.