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The fraction of absorbed photosynthetically active radiation (FAPAR) and the photosynthesis rate (Pn) of maize canopies were identified as essential photosynthetic parameters for accurately estimating vegetation growth and productivity using multispectral vegetation indices (VIs). Despite their importance, few studies have compared the effectiveness of multispectral imagery and various machine learning techniques in estimating these photosynthetic traits under high vegetation coverage. In this study, seventeen multispectral VIs and four machine learning (ML) algorithms were utilized to determine the most suitable model for estimating maize FAPAR and Pn during the kharif and rabi seasons at Tamil Nadu Agricultural University, Coimbatore, India. Results demonstrate that indices such as OSAVI, SAVI, EVI-2, and MSAVI-2 during the kharif and MNDVIRE and MSRRE during the rabi season outperformed others in estimating FAPAR and Pn values. Among the four ML methods of random forest (RF), extreme gradient boosting (XGBoost), support vector regression (SVR), and multiple linear regression (MLR) considered, RF consistently showed the most effective fitting effect and XGBoost demonstrated the least fitting accuracy for FAPAR and Pn estimation. However, SVR with R2 = 0.873 and RMSE = 0.045 during the kharif and MLR with R2 = 0.838 and RMSE = 0.053 during the rabi season demonstrated higher fitting accuracy, particularly notable for FAPAR prediction. Similarly, in the prediction of Pn, MLR showed higher fitting accuracy with R2 = 0.741 and RMSE = 2.531 during the kharif and R2 = 0.955 and RMSE = 1.070 during the rabi season. This study demonstrated the potential of combining UAV-derived VIs with ML to develop accurate FAPAR and Pn prediction models, overcoming VI saturation in dense vegetation. It underscores the importance of optimizing these models to improve the accuracy of maize vegetation assessments during various growing seasons.

The exploitation of drought-tolerant plant-associated bacteria in sustainable agriculture is considered potential for boosting plant growth and development under adverse environments featured by low water potentials. Herein, the present study aimed to decode the drought-tolerant root-associated bacteria from little millet (Panicum sumatrense L.). A sum of 25 bacterial isolates was obtained from roots of little millet (Var. ATL1) grown under induced drought conditions (-10 bars). They were initially assessed for their capacity to tolerate the drought up to – 36.6 bars (-3.6 MPa) on PEG (PEG 6000) infused agar plates and of which, 9 isolates (LRS1, LRS2, LRS6, LRS9, LRS14, LRS17, LRS22, LRS23, LRS25) were selected. Further, the cultures were assessed for their plant growth-promoting (PGP) traits such as the production of 1-aminocyclopropane-1-carboxylate deaminase (ACCd), exopolysaccharide (EPS), siderophore, indole acetic acid (IAA), ammonia, and hydrogen cyanide (HCN) and plant nutrition properties including phosphorous, potassium, and zinc. Of three isolates, LRS2 produced the highest ACCd (147 mol α- ketobutyrate mg protein–1h–1), IAA (15.89 μg mL–1), siderophore (54.85 % ) and P solubilization compared to other isolates. Further, the potential isolates LRS2, LRS22 and LRS23 were identified as Bacillus albus, Bacillus amyloliquefaciens and Enterobacter sp. respectively based on 16S rRNA sequence analysis. Biotization of little millet seeds (ATL1) with these strains showed that the inoculation of B. albus LRS2 elevated the plant germination (33 %), shoot length (13.5 %), and root length (25.2 %) followed by LRS23 with plant germination (22 %), shoot length (12.2 %) and root length (13.2 %) under induced drought stress (-0.45 MPa). Besides, metabolite profiling of B. albus LRS2 under induced stress analyzed in GC–MS yielded several drought-tolerant metabolites belonging to the class viz., organic acids, fatty acids, amino acid and its derivatives, organoheterocyclic compounds, and benzenoids. The compounds responsible for drought tolerance such as phenol, proline, fumaric acid, ascorbic acid, and gibberellic acid are more pronounced under induced drought stress (PEG 6000) which would aid in drought stress tolerance and facilitate plant health and fitness. These results implied that B. albus LRS2, a root-associated drought-tolerant endophytic bacteria, would enhance plant growth under drought stress, and lay a foundation for developing a novel bioinoculant for mitigating abiotic stress and promoting sustainable little millet production.

Bacterial endophytes thriving in the apoplast could be tailored to withstand environmental challenges, thereby contributing to plant stress resilience. Developing plant growth-promoting (PGP) bacteria resilient to drought is crucial for sustainable agriculture and improved crop production under stress conditions. Thus, the present study aimed to decode the drought-tolerant apoplast-associated PGP bacteria from little millet (Panicum sumatrense L.) and profile its functional traits including the metabolites for PGP and drought resilience. The drought tolerant PGP bacterial isolates from apoplastic fluids of little millet (Var. ATL1) grown under stressed conditions (-10 bars) on polyethylene glycol (PEG) (PEG 6000) infused agar plates revealed that a total of 12 isolates, were initially screened for their ability to tolerate drought up to –36.6 bars (–3.6 MPa) and of which, 6 isolates (LAS1, LAS2, LAS4, LAS6, LAS9, and LAS10) were selected. Further, screening for their plant growth-promoting (PGP) traits such as 1-aminocyclopropane-1-carboxylate deaminase (ACCd) production, exopolysaccharide production, potassium (K), phosphorous (P), and Zinc (Zn) nutrition, indole acetic acid (IAA) production, siderophore production, ammonia, and hydrogen cyanide (HCN) exhibited higher PGP potentials in LAS10 followed by LAS2 and LAS4. Of three potential isolates (LAS10, LAS2, and LAS4), LAS10 produced the highest ACCd (147 n moles α- ketobutyrate mg–1h–1), IAA (15.9 μg mL–1), siderophore (54.9 % units) and P solubilization compared to other isolates. Further, isolate LAS10 was identified as Bacillus amyloliquefaciens based on 16S rRNA sequence analysis. Metabolite profiling of B. amyloliquefaciens LAS10 in GC–MS under induced stress conditions yielded several drought-tolerant metabolites belonging to the class viz., organic acids, fatty acids, amino acid and its derivatives, organoheterocyclic compounds, and benzenoids. The compounds responsible for drought tolerance such as phenol, proline, fumaric acid, ascorbic acid, and gibberellic acid are more pronounced under induced drought stress (PEG 6000) which would aid in drought stress tolerance and facilitate plant health and fitness. These results implied that B. amyloliquefaciens LAS10, an apoplast-associated drought-tolerant endophytic bacteria, would enhance plant growth under drought stress, and can be further extrapolated as a newer bio inoculant for abiotic stress mitigation and sustainable production in little millet.