Anbil Dharmalingam Agricultural College and Research Institute

Root System Architecture (RSA) is a pivotal trait for enhancing drought resilience in rice, as it directly influences water uptake efficiency. Key RSA traits, including deeper rooting, root length density, and root growth angle, contribute significantly to stress adaptation. However, the root system often referred to as the “hidden half,” remains underutilized in conventional breeding programs despite being the primary site of stress perception and a core component of the plant’s drought response. This review provides an integrative, mechanism-driven synthesis that links root architecture, anatomical adaptations, hormonal and molecular signaling pathways, and translational breeding strategies for drought tolerance in rice, thereby bridging the gap between mechanistic understanding and breeding application. It highlights recent advances in identifying drought-responsive genes, including Deeper Rooting 1, and discusses the role of forward genetics in uncovering sequence variations associated with favorable RSA traits. Emerging image-based high-throughput phenotyping platforms and non-invasive technologies such as X-ray computed tomography and magnetic resonance imaging have enabled detailed characterization of root development and function. By integrating RSA-driven insights from high-throughput phenotyping and molecular genetics into breeding pipelines, rice improvement programs can achieve durable drought tolerance, yield stability, and resilience to climatic variability. Furthermore, the review examines adaptive mechanisms of rice roots under water-deficit conditions and outlines prospective strategies to accelerate genetic enhancement toward developing next-generation climate-resilient rice cultivars.

Seaweed biostimulants have gained considerable attention as sustainable inputs for improving crop growth due to their diverse biochemical composition. Efficacy of seaweed extracts is determined by seaweed species and the extraction method employed. In the present study, five seaweed species were subjected to seven distinct extraction processes and were applied in soil to evaluate the effects on the early vegetative growth of tomato seedlings. Influence of these extracts on growth, physiological performance, root structural traits, and biomass partitioning was systematically assessed. Among the species tested, Kappaphycus alvarezii and Ulva lactuca consistently exhibited superior performance, particularly when extracted using alkali II (0.1 N KOH). Enhanced seedling growth is primarily associated with improvements in biomass, photosynthetic rate, stomatal conductance, chlorophyll content, leaf area, and root structural characteristics. Biochemical characterization of the six best performing extracts revealed distinct yet complementary profiles, with Ulva alkali II extracts enriched in phenolic compounds, carotenoids, and proteins, whereas Kappaphycus alkali II extracts were comparatively richer in carbohydrates, soluble sugars, starch, and lipids. These biochemical attributes coincided with increased photosynthetic pigment and soluble protein contents in treated tomato seedlings. Redundancy analysis revealed that physiological enhancements by the high performing extracts, were closely associated with greater shoot mass fraction than root and leaf fractions. Overall, the findings demonstrate that alkali II extracts of Ulva and Kappaphycus are the most promising formulations for promoting early vegetative growth of tomato seedlings highlighting their potential application as effective seaweed based biostimulants.

Online forums have become essential resources for information sharing and collaboration, but they also face the challenge of negative and offensive user feedback. To maintain a polite and safe online environment, an effective comment toxicity model must be developed. This project creates a model of comment toxicity using Deep Learning (DL) and Python. Using a DL architecture, the suggested model evaluates text data and classifies comments into non-hazardous and harmful categories. The model leverages powerful Natural Language Processing (NLP) methods, including word embeddings and recurrent neural network (RNN), to extract the semantic and contextual information from comments. Additionally, attention processes and CNN (CNN) are employed to enhance the model's performance. Several key components of the process include data preparation, feature engineering, model creation and training, and assessment. Python is a prominent programming language in the data science industry that is used to construct the workflow. Two open-source libraries that provide the resources needed to efficiently build and train DL models are TensorFlow and Kera’s. To evaluate the effectiveness of the model, a large dataset of tagged comments is used, which includes both hazardous and non-toxic remark occurrences. Recall, accuracy, precision, and F1-score are just a few of the assessment metrics used to gauge the model's performance. The project's output will improve content moderation systems by enabling platforms to recognize and report offensive comments as soon as they are posted. This work provides a practical and scalable approach DL-based comment toxicity detection, assisting online communities in fostering a more inviting and safer environment for its members.

Fish processing industries generate large quantities of by-products, including heads, skin, bones and viscera, which are often underutilized and contribute to environmental pollution. Among these wastes, fish viscera are a promising source of proteolytic enzymes with significant industrial potential. Fish-derived proteases exhibit desirable properties such as high catalytic activity, functionality at low temperatures and stability over a wide range of pH conditions, making them suitable for applications in food processing, detergents, leather treatment, pharmaceuticals and waste management. This review critically examines the potential of fish visceral waste as a sustainable source of industrial proteases, with emphasis on enzyme sources, recovery approaches, purification strategies, biochemical characteristics and industrial applications. The review further discusses the advantages of utilizing fish visceral proteases over conventional microbial enzymes, particularly in terms of sustainability, waste valorisation and circular bioeconomy development. In addition, current limitations and challenges associated with enzyme extraction, stability and large-scale commercialization are highlighted. By integrating recent advancements in the field, this review highlights the effective utilization of fish processing waste and emphasizes the potential of fish visceral proteases as eco-friendly biocatalysts for sustainable industrial applications.

Phosphorus (P) is a primary limiting macronutrient in tropical and subtropical rice production. While total soil P may be high, over 50 % of global agricultural soils suffer from low bioavailable orthophosphate due to strong chemical fixation with aluminium (Al) and iron (Fe) oxides in acidic soils, or calcium (Ca) in alkaline soils. Developing and deploying P-efficient rice genotypes capable of maintaining growth and yield under low soil P availability is a key priority for sustainable agriculture. A pot culture experiment was carried out at the Radio Isotope Laboratory, Department of Soil Science and Agricultural Chemistry, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu. The treatment comprised factor A: rice genotypes viz.,TNRH-180, CB08504, CB06732 and ADT 47 and factor B: P levels viz., 0, 25 and 50 kg P ha-1.Phosphorus was applied in the form of the radioisotope ³²P, the experiment aimed to evaluate and quantify yield performance, phosphorus use efficiency (PUE) and the native P supplying capacity of low P soils. The findings indicated that the rice genotypes viz.,TNRH-180 recorded higher yield attributes, yield and PUE viz., productive tillers (8.4 nos.), panicle length (24.1 cm), grains per panicle (167.7 nos.), test weight (17.2 g), grain yield (22.3 g/pot) and PUE (23.72 %) and was followed by CB06732 respectively.