Abstract Agriculture is the primary source of income for each country, serving as its mainstay. A promising study topic has been predicting wheat production based on environmental, soil, and water characteristics. Deep-learning-based algorithms are widely employed in crop prediction to extract significant crop traits. Wheat is linked to a variety of economic, societal, and health-related factors. Wheat yield forecasting and estimation on a regional scale, on the other hand, remains difficult. Two strategies for estimating wheat yield using deep learning (DL) models are presented in this study. To solve the limitations of regional forecasting, Convolutional Neural Networks (CNN) and Deep Learning Long Short-Term Memory (LSTM) technology are utilized to anticipate agricultural yields in a timely and reliable manner. country mainstay environmental soil characteristics Deeplearningbased traits economic societal healthrelated health related factors scale hand difficult DL (DL CNN (CNN ShortTerm Short Term LSTM (LSTM manner

in this study, an experiment was conducted to investigate the optimum lateral spacing and irrigation frequency for subsurface drip irrigated okra in the semi-arid region of Haryana (India). Two lateral spacings (45 cm and 60 cm) and four irrigation frequencies (daily, after 1,2 and 3 days) were selected to grow okra in the Kharif season of 2019 and 2020. The effect on soil water dynamics, growth parameters, efficiency and yield were assessed using equal amounts of water under all the treatments on the basis of pan evaporation. The results from the study depict that the overall soil moisture decreased laterally, but increased vertically downward with the increase in the irrigation interval. On the basis of soil water dynamics, plant growth parameters, efficiency and yield of okra, it was concluded that subsurface drip irrigation with daily irrigation at 45 cm lateral spacing gives better performance than all other treatments in sandy loam soil. The present study highlights the significance of proper irrigation frequency and lateral spacing for maximum production of okra. Using these guidelines, the income of okra growers/farmers in the semi-arid region may be increased by choosing the best frequency and lateral spacing of subsurface drip irrigation.

Abstract: Human population growth in combination with changing patterns of global food consumption under climate change is posing formidable challenge to attaining sustainable global food security. Besides being economically viable sources of plant based protein for human consumption, pulses are also beneficial for the environment owing to their inherent capacity of nitrogen fixation. Hence, further development of pulses has become imperative in the vigorously transitional global scenario where flourishing anthropogenic activities are triggering irreplaceable depletion of natural resources. During past years, considerable attention has been given on the use of next generation sequencing for enriching the genomic resources in pulse crops including high-throughput DNA markers, candidate gene(s) and QTLs for predicting plant phenotypes, and whole genome sequences. With refinements in DNA sequencing technologies and computational analytical tools, the rapidly grown numbers of sequenced pulse genomes offer novel insights on crop evolution and breeding history. Integration of new-generation genomic and phenomic tools with generation acceleration procedures like genomic selection and speed breeding could greatly accelerate progress in pulses genetic improvement. The present review discusses current status and future scope of using next-generation breeding approaches in pulses that will cause not only an increase in the rate of developing climate-resilient superior cultivars but also help to reach to goal of global food security.

Plant extracts containing heteroatoms can be used as corrosion inhibitors as they are non-polluting, cheap and eco-friendly. The present work focuses on Asparagus Racemosus (Shatavari) as a corrosion inhibitor of aluminium in acidic medium. The techniques that have been used include weight loss method, quantum chemical analysis and scanning electron microscopy. It was seen that at 4000 ppm concentration of inhibitor the corrosion inhibition efficiency was 72.28%. The inhibition efficiency increased with increase in concentration. The best description of adsorption isotherm was seen to follow Langmuir adsorption isotherm. In order to elucidate the reactivity and molecular structure of inhibitor, quantum chemical parameters were utilized. The surface properties of the metal specimen were determined by SEM.