Growth and antioxidant responses to water stress in eggplant MAGIC population parents, F1 hybrids and a subset of recombinant inbred lines.

BACKGROUND: The generation of new eggplant (Solanum melongena L.) cultivars with drought tolerance is a main challenge in the current context of climate change. In this study, the eight parents (seven of S. melongena and one of the wild relative S. incanum L.) of the first eggplant MAGIC (Multiparent Advanced Generation Intercrossing) population, together with four F1 hybrids amongst them, five S5 MAGIC recombinant inbred lines selected for their genetic diversity, and one commercial hybrid were evaluated in young plant stage under water stress conditions (30% field capacity; FC) and control conditions (100% FC). After a 21-day treatment period, growth and biomass traits, photosynthetic pigments, oxidative stress markers, antioxidant compounds, and proline content were evaluated. RESULTS: Significant effects (p < 0.05) were observed for genotype, water treatments and their interaction in most of the traits analyzed. The eight MAGIC population parental genotypes displayed a wide variation in their responses to water stress, with some of them exhibiting enhanced root development and reduced foliar biomass. The commercial hybrid had greater aerial growth compared to root growth. The four F1 hybrids among MAGIC parents differed in their performance, with some having significant positive or negative heterosis in several traits. The subset of five MAGIC lines displayed a wide diversity in their response to water stress. CONCLUSION: The results show that a large diversity for tolerance to drought is available among the eggplant MAGIC materials, which can contribute to developing drought-tolerant eggplant cultivars.

Deep learning-based prediction of plant height and crown area of vegetable crops using LiDAR point cloud.

Remote sensing has been increasingly used in precision agriculture. Buoyed by the developments in the miniaturization of sensors and platforms, contemporary remote sensing offers data at resolutions finer enough to respond to within-farm variations. LiDAR point cloud, offers features amenable to modelling structural parameters of crops. Early prediction of crop growth parameters helps farmers and other stakeholders dynamically manage farming activities. The objective of this work is the development and application of a deep learning framework to predict plant-level crop height and crown area at different growth stages for vegetable crops. LiDAR point clouds were acquired using a terrestrial laser scanner on five dates during the growth cycles of tomato, eggplant and cabbage on the experimental research farms of the University of Agricultural Sciences, Bengaluru, India. We implemented a hybrid deep learning framework combining distinct features of long-term short memory (LSTM) and Gated Recurrent Unit (GRU) for the predictions of plant height and crown area. The predictions are validated with reference ground truth measurements. These predictions were validated against ground truth measurements. The findings demonstrate that plant-level structural parameters can be predicted well ahead of crop growth stages with around 80% accuracy. Notably, the LSTM and the GRU models exhibited limitations in capturing variations in structural parameters. Conversely, the hybrid model offered significantly improved predictions, particularly for crown area, with error rates for height prediction ranging from 5 to 12%, with deviations exhibiting a more balanced distribution between overestimation and underestimation This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications. However, the prediction quality is relatively low at the advanced growth stage, closer to the harvest. In contrast, the prediction quality is stable across the three different crops. The results indicate the presence of a robust relationship between the features of the LiDAR point cloud and the auto-feature map of the deep learning methods adapted for plant-level crop structural characterization. This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications.

Identification of Quantitative Trait Loci and Candidate Genes Controlling Seed Dormancy in Eggplant (Solanum melongena L.).

Seed dormancy is a life adaptation trait exhibited by plants in response to environmental changes during their growth and development. The dormancy of commercial seeds is the key factor affecting seed quality. Eggplant seed dormancy is controlled by quantitative trait loci (QTLs), but reliable QTLs related to eggplant dormancy are still lacking. In this study, F2 populations obtained through the hybridization of paternally inbred lines with significant differences in dormancy were used to detect regulatory sites of dormancy in eggplant seeds. Three QTLs (dr1.1, dr2.1, and dr6.1) related to seed dormancy were detected on three chromosomes of eggplant using the QTL-Seq technique. By combining nonsynonymous sites within the candidate regions and gene functional annotation analysis, nine candidate genes were selected from three QTL candidate regions. According to the germination results on the eighth day, the male parent was not dormant, but the female parent was dormant. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the expression of nine candidate genes, and the Smechr0201082 gene showed roughly the same trend as that in the phenotypic data. We proposed Smechr0201082 as the potential key gene involved in regulating the dormancy of eggplant seeds. The results of seed experiments with different concentrations of gibberellin A3 (GA3) showed that, within a certain range, the higher the gibberellin concentration, the earlier the emergence and the higher the germination rate. However, higher concentrations of GA3 may have potential effects on eggplant seedlings. We suggest the use of GA3 at a concentration of 200-250 mg·L-1 to treat dormant seeds. This study provides a foundation for the further exploration of genes related to the regulation of seed dormancy and the elucidation of the molecular mechanism of eggplant seed dormancy and germination.

Amelioration of phytotoxic impact of biosynthesized zinc oxide nanoparticles: Plant growth promoting rhizobacteria facilitates the growth and biochemical responses of Eggplant (Solanum melongena) under nanoparticles stress.

The consistently increasing use of zinc oxide nanoparticles (ZnONPs) in crop optimization practices and their persistence in agro-environment necessitate expounding their influence on sustainable agro-environment. Attempts have been made to understand nanoparticle-plant beneficial bacteria (PBB)- plant interactions; the knowledge of toxic impact of nanomaterials on soil-PBB-vegetable systems and alleviating nanotoxicity using PBB is scarce and inconsistent. This study aims at bio-fabrication of ZnONPs from Rosa indica petal extracts and investigates the impact of PBB on growth and biochemical responses of biofertilized eggplants exposed to phyto-synthesized nano-ZnO. Microscopic and spectroscopic techniques revealed nanostructure, triangular shape, size 32.5 nm, and different functional groups of ZnONPs and petal extracts. Inoculation of Pseudomonas fluorescens and Azotobacter chroococcum improved germination efficiency by 22% and 18% and vegetative growth of eggplants by 14% and 15% under NPs stress. Bio-inoculation enhanced total chlorophyll content by 36% and 14 %, increasing further with higher ZnONP concentrations. Superoxide dismutase and catalase activity in nano-ZnO and P. fluorescens inoculated eggplant shoots reduced by 15-23% and 9-11%. Moreover, in situ experiment unveiled distortion and accumulation of NPs in roots revealed by scanning electron microscope and confocal laser microscope. The present study highlights the phytotoxicity of biosynthesized ZnONPs to eggplants and demonstrates that PBB improved agronomic traits of eggplants while declining phytochemicals and antioxidant levels. These findings suggest that P. fluorescens and A. chroococcum, with NPs ameliorative activity, can be cost-effective and environment-friendly strategy for alleviating NPs toxicity and promoting eggplant production under abiotic stress, fulfilling vegetable demands.