Multivariate Data Analysis Assisted Mining of Nutri-rich Genotypes from North Eastern Himalayan Germplasm Collection of Perilla (Perilla frutescens L.).

Understanding the nutritional diversity in Perilla (Perilla frutescens L.) is essential for selecting and developing superior varieties with enhanced nutritional profiles in the North Eastern Himalayan (NEH) region of India. In this study, we assessed the nutritional composition of 45 diverse perilla germplasm collected from five NEH states using standard protocols and advanced analytical techniques. Significant variability was observed in moisture (0.39-11.67%), ash (2.59-7.13%), oil (28.65-74.20%), protein (11.05-23.15%), total soluble sugars (0.34-3.67%), starch (0.01-0.55%), phenols (0.03-0.87%), ferric reducing antioxidant power (0.45-1.36%), palmitic acid (7.06-10.75%), stearic acid (1.96-2.29%), oleic acid (8.11-13.31%), linoleic acid (15.18-22.74%), and linolenic acid (55.47-67.07%). Similarly, significant variability in mineral content (ppm) was also observed for aluminium, calcium, cobalt, chromium, copper, iron, potassium, magnesium, manganese, molybdenum, sodium, nickel, phosphorus, and zinc. Multivariate analyses, including hierarchical clustering analysis (HCA) and principal component analysis (PCA), revealed the enriched nutritional diversity within the germplasm. Correlation analysis indicated significant positive and negative relationships between nutritional parameters, indicating potential biochemical and metabolic interactions present in the perilla seeds. TOPSIS-based ranking identified promising genotypes for functional foods, pharmaceuticals, and nutritional applications. This study provides a first in-depth report of the nutritional composition and diversity of perilla germplasm in the NEH region, thus aiding in the identification of superior varieties for food and nutritional diversification and security.

Finger millet (Eleusine coracana L.): from staple to superfood-a comprehensive review on nutritional, bioactive, industrial, and climate resilience potential.

MAIN CONCLUSION: This review discusses the Finger millet's rich nutritional profile, bioactive potential, and industrial applications, combined with its climate resilience, which make it a promising crop for enhancing food security and promoting sustainable agriculture. This review also highlights its significant potential to address malnutrition and mitigate climate change impacts. The emergence of Finger millet from "poor man's staple food" to "a nutrient rich cereal" has encouraged the need to explore this crop at a wider scale. It is a highly significant crop due to its rich nutritional and bioactive profile, diverse biological activities, and promising industrial applications, along with the high climate resilience. This comprehensive review evaluates its nutritional composition by comparing favorably with other cereals and millets and emphasizing its potential to address malnutrition and enhance food security. Furthermore, it explores the phytochemical/bioactive potential and strategies to enhance their bioavailability followed biological activities of Finger millet by highlighting its various health-promoting properties. The review also discusses industrial potential of finger millet including its role in nutraceutical and functional food production, as well as bioenergy generation. In addition, role of Finger millet as a climate-resilient crop; specifically, the available genetic resources and identification of genes and quantitative trait loci (QTLs) associated with major stress tolerance traits have also been discussed. By providing a comprehensive synthesis of existing knowledge, this study offers valuable insights for researchers, policymakers, and stakeholders engaged in efforts to promote sustainable agriculture, enhance food and nutrition security, and mitigate the impacts of climate change.

Attention module incorporated transfer learning empowered deep learning-based models for classification of phenotypically similar tropical cattle breeds (Bos indicus).

Accurate breed identification in dairy cattle is essential for optimizing herd management and improving genetic standards. A smart method for correctly identifying phenotypically similar breeds can empower farmers to enhance herd productivity. A convolutional neural network (CNN) based model was developed for the identification of Sahiwal and Red Sindhi cows. To increase the classification accuracy, first, cows's pixels were segmented from the background using CNN model. Using this segmented image, a masked image was produced by retaining cows' pixels from the original image while eliminating the background. To improve the classification accuracy, models were trained on four different images of each cow: front view, side view, grayscale front view, and grayscale side view. The masked images of these views were fed to the multi-input CNN model which predicts the class of input images. The segmentation model achieved intersection-over-union (IoU) and F1-score values of 81.75% and 85.26%, respectively with an inference time of 296 ms. For the classification task, multiple variants of MobileNet and EfficientNet models were used as the backbone along with pre-trained weights. The MobileNet model achieved 80.0% accuracy for both breeds, while MobileNetV2 and MobileNetV3 reached 82.0% accuracy. CNN models with EfficientNet as backbones outperformed MobileNet models, with accuracy ranging from 84.0% to 86.0%. The F1-scores for these models were found to be above 83.0%, indicating effective breed classification with fewer false positives and negatives. Thus, the present study demonstrates that deep learning models can be used effectively to identify phenotypically similar-looking cattle breeds. To accurately identify zebu breeds, this study will reduce the dependence of farmers on experts.

A comprehensive review on nutritional, nutraceutical, and industrial perspectives of perilla (Perilla frutscens L.) seeds - An orphan oilseed crop.

There is a growing need to mainstream orphan or underutilized crops to enhance nutritional security and sustainable agriculture. Among these, Perilla frutescens L. is an important crop due to its rich nutritional and phytochemical content which makes it significant in nutrition, medicine, and industrial sector. Perilla seeds are mainly rich in ω-3 fatty acids, dietary fiber, amino acids, vitamins, and minerals, high α-linolenic acid, which contributes to their health benefits. This review explores the nutritional profile of perilla seeds and highlights its unique composition compared to other oilseed crops. It also analyzes the phytochemical components of perilla seeds and their various biological activities, including antioxidant, antidiabetic, antiobesity, cardioprotective, anticancer, antimicrobial, neuroprotective, and anti-inflammatory effects. These activities demonstrate the potential of perilla seeds in both pharmaceutical and food sectors. The review also covers recent advancements in genomics and transgenic research discussing potential areas for crop improvement. Additionally, it explores the use of perilla seeds in functional foods, blending perilla oil with other oils, and their applications in enhancing product formulations. This review offers valuable insights for researchers, students, policymakers, environmentalists, and industry professionals by detailing the potential of perilla seeds across various sectors. The findings support sustainable agriculture, crop diversification, and innovative product development, thus contributing to the integration of perilla into mainstream agriculture.