Soil salinity is one of the major abiotic constraints in agricultural ecosystems worldwide. High salinity levels have negative impacts on plant growth and yield, and affect soil physicochemical properties. Salinity also has adverse effects on the distribution and abundance of soil microorganisms. Salinity problems have previously been addressed in research, but most approaches, such as breeding for salt tolerant varieties and soil amelioration, are expensive and require years of efforts. Halotolerant plant growth-promoting rhizobacteria (HT-PGPR) secrete secondary metabolites, including osmoprotectants, exopolysaccharides, and volatile organic compounds. The importance of these compounds in promoting plant growth and reducing adverse effects under salinity stress has now been widely recognised. HT-PGPR are emerging as effective biological strategies for mitigating the harmful effects of high salinity; improving plant growth, development, and yield; and remediating degraded saline soils. This review describes the beneficial effects and growth-promoting mechanisms of various HT-PGPR, which are carried out by maintaining ion homeostasis, increasing nutrient availability, and the producing secondary metabolites, osmoprotectants, growth hormones, and volatile organic compounds. Exploring suitable HT-PGPR and applications in agriculture production systems can play a crucial role in reducing the adverse impacts of salinity stress and sustainable crop productivity.

Rice is a vital crop for global food security, but its production is vulnerable to various diseases. Early detection and treatment of rice diseases are crucial to minimise yield losses. Convolutional neural networks (CNNs) have shown great potential for disease detection in plant leaves, but training CNNs requires large datasets of labelled images, which can be expensive and time-consuming. Here, we have experimented a 3-Stage CNN architecture with a transfer learning approach that utilises a pre-trained CNN model fine-tuned on a small dataset of rice disease images. The proposed approach significantly reduces the required training data while achieving high accuracy. We also incorporated deep learning techniques such as progressive re-sizing and parametric rectified linear unit (PReLU) to enhance rice disease detection. Progressive re-sizing improves feature learning by gradually increasing image size during training, while PReLU reduces overfitting and enhances model performance. The proposed approach was evaluated on a dataset of 8883 and 1200 images of disease and healthy rice leaves, respectively, achieving an accuracy of 94% when subjected to the 10-fold cross-validation process, significantly higher than other methods. These simulation results for disease detection in rice prove the feasibility and efficiency and offer a cost-effective, accessible solution for the early detection of rice diseases, particularly useful in developing countries with limited resources that can significantly contribute toward sustainable food production.

The nutrient and biomass supply capacity of green manure (GM) and its complimentary and synergistic relationship with chemical nutrients is needed for a sustainable rice–baby corn cropping system in the eastern part of North India. A two-year field study was performed to assess the effect of GM and nitrogen management (NM) on yield attributes (YA), the yield of rice, and their residual (R) effect with the half-recommended dose of fertilizers on the succeeding baby corn crop. The combination of GM and N levels had significant effects on rice yield, which also influenced the succeeding baby corn crop. A higher number of tillers/m², panicle length (cm), number of grains/panicles, panicle weight (g), grain yield (Mg/ha), straw yield (Mg/ha), and biological yield (Mg/ha) were found with Sesbania aculeata incorporated at 45 DAS (SA), which was statistically on par with water hyacinth 5 t/ha dry weight basis (WH) during both years of the field study. Among the rates of nitrogen fertilizers, 100% RDN (50% N through FYM + 50% N through inorganic) (RDN₁₀₀) was the best treatment with a corresponding increase in all YA and yields of rice. It has been proven that integrated nitrogen management (INM), or the use of organic material along with fertilizer, is an effective way of managing nitrogen. In the present investigation, green manuring and integrated nitrogen management on rice sustainably increased the yield attributes and yield of the succeeding baby corn. These results illustrated the complementary effects of summer green manuring in conjunction with INM in maximizing the yield attributes and yields of rice, its residual effect on succeeding baby corn, and the economics of the system.