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No full textNot AvailableThe research was conducted at ICAR-National Research Centre for Grapes, Pune during October, 2023 to March, 2024 to investigates the effect of varying leaf retention on leaf area index (LAI), photosynthetic activities, yield and quality of Crimson Seedless grapes grafted on Dogridge rootstock. Five treatments of different numbers of leaves retained on each shoot above the bunch (10, 12, 14, 16 and more than 16 leaves) were evaluated. Parameters measured included leaf area, leaf area index, photosynthetically active radiation (PAR), photosynthetic rate, stomatal conductance, chlorophyll content, average bunch weight, berry weight, yield per vine, total soluble solids (TSS) and acidity. The results showed that leaf area per leaf ranged from 140.60 cm² in the treatment with more than 16 leaves to 156.40 cm² in the 12-leaf treatment. The highest yield per vine (12.89 kg) and optimum berry quality were observed with 12 leaves above the bunch. Increased leaf area improved nutrient assimilation and berry growth but excessive leaf retention led to decreased light penetration and photosynthetic efficiency. These findings suggest that maintaining 12 leaves with 1782.70 cm2 leaf area above the bunch is optimal for balancing yield (12.89 kg/vine) and berry quality in Crimson Seedless grapes under the tropical conditions.Not Availabl
DDC: Deep Distribution Classifier, A Convolutional Neural Network-based Approach for Identifying Data Distributions
No full textNot AvailableIn domains such as the stock market and manufacturing, there’s a growing demand for faster and more accurate data distribution identification methods due to the rapid generation of vast volumes of data, highlighting the need for enhanced real-time decision-making capabilities. Traditional methods of identifying data distributions often rely on manual inspection, limited statistical tests and time-consuming analysis, leading to inefficiencies and
inaccuracies in classification. In this scenario, the presented research offers a novel approach leveraging Deep Learning (DL) models to automate the process. The presented methodology also enables faster and more accurate identification of data distributions by the generation of synthetic data points and training of the DL model for identifying different distribution types. The primary objective of this study is to develop a DL model that categorizes
data points into specific distributions based on an input dataset. Moreover, for model training and evaluation, a total of 1000 datasets are generated, each comprising 1000 data points. The study considers five distributions (Normal, Uniform, Exponential, Log-normal and Beta distribution), with 200 datasets generated (with randomly selected parameters) for each distribution. In the study, the DL model is trained first, and later, the model is
evaluated on a separate test (unseen) dataset. Then, its performance in classifying the distributions is assessed based on metrics such as accuracy and loss. The study results demonstrate the effectiveness of the proposed approach in accurately classifying the distribution of data points, providing valuable insights into the application of DL for distribution classification tasks. The proposed method enhances scalability, robustness and efficiency by harnessing the power of convolutional neural networks and advanced preprocessing techniques.Not Availabl
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No full textNot AvailableCorrelation and path analysis are useful selection aids for the plant breeder to understand the complex
interactions among various factors that influence crop growth and yield. The data were collected from a diverse
set of 150 genotypes, consisting of 129 restorers, 15 maintainers and 6 checks in three different environments
(Kharif 2021@ICAR-IIRR, Hyderabad, Kharif, 2021@ Agricultural College, Tirupati and Rabi 21-22@ICARIIRR,
Hyderabad) on 10 different component traits. The analysis included three data sets along with their
pooled values. All of the component traits demonstrated a positive correlation with single plant yield, either
significant or non-significantly in environment wise analysis as well as in the pooled analysis. The analysis
revealed that certain traits, namely plant height, total number of tillers per plant, productive tillers per plant
and spikelet fertility showed a significant positive correlation with single plant yield while the rest of the traits
exhibited a positive non-significant correlation. Based on the results, plant height, biomass, and harvest index
play a key role in determining final yield, as they have a strong positive correlation with single plant yield and
exert positive direct effects on it. Therefore, prioritizing these traits during selection could be an effective
approach for the indirect selection of increased grain yield.Not Availabl
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No full textNot AvailableFall armyworm is a destructive insect pest in maize farming and has expanded widely throughout various agroecological zones, which threatens food security. An experiment was carried out at the Winter Nursery (ICAR-IIMR, Hyderabad) field to study the occurrence of fall armyworm on maize single cross hybrid DHM 117 across different sowing dates during kharif and rabi seasons of 2021-22. Weekly observations were made on a whole-plot basis to record the number of plants damaged, the number of larvae, and egg masses per plant. Among the six sowing dates, the crop was sown on
2nd August 2021, had a relatively lower mean per cent of infestation range (4.02% - 80.37%), a minimum larval count per plant range (0.01 - 0.24), and the least number of egg masses per plant range (0.00 - 0.11). The findings will be helpful in the construction of forecasting models, facilitating the formulation of eco-friendly management tactics to manage fall armyworms in maize.Not Availabl
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No full textNot AvailableModern agriculture relies on pesticides to increase crop yields, but these chemicals are also hazardous. Although conventional sprayers were designed for effective pest management, they nonetheless pollute the environment and endanger operators’ health. Unmanned aerial vehicle (UAV)-based sprayers overcome the aforesaid problem and can precisely target the areas that need treatment and difficult to reach for human operators. This study evaluated a UAV-based spraying system in a cotton field, employing imaging techniques such as Laser Droplet Analyzer, Deposit Scan, ImageJ, and Drop leaf. Furthermore, the system was optimized using response surface methodology, and deposition predictive analysis was conducted using a hybrid GWO-ANN approach. The volume median diameter, number median diameter, relative span, and uniformity coefficient were in the range of 95–248 μm, 65–174 μm, 0.8–1.7 %, and 1.3–1.7 %, respectively. Optimizing the working speed (3.3 m/s), working height (1.0 m), and discharge rate (2.0 L/min) resulted in a droplet density of 50.3 droplets/cm2, deposition of 0.20 μL/cm2, and coverage of 9.27 %. The GWO-ANN prediction model yielded R2, RMSE, and MAE values of 0.878, 0.01729, and 0.01368, respectively. Optimizing operational parameters through multiple measurement techniques enhance flexibility and effectiveness of UAV-based spraying system, facilitating wider deployment in remote agricultural areas for agrochemical applications.Not Availabl
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No full textNot AvailableThere is a decline in share of backyard eggs to total egg production in the state. Number of desi
chicken germplasm is decreasing whereas improved varieties chicken population is increasing.
Supply of improved germplasm, other inputs, technical and financial support along with market
linkage should be created for rural and tribal farmers of the state. Farmers should take advantage
of government schemes and support of local institution in backyard poultry production. There is
niche market in the urban area for rural chicken produces where farmers can get premium price.Not Availabl
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No full textNot AvailableThis study describes clam drying using various dryers (Open sun, solar tunnel dryer, solar-LPG dryer, infrared
dryer). The parameters like moisture content of clam after drying, drying efficiency, rate of drying, energy
consumption, proximate composition, microbial load, and sensory characteristics of dried clam were analyzed
and also studied the economic aspects of clam drying using various dryers. The moisture content of clam reduced
drastically to the level ranging from 9 17 %wb in various dryers. Protein, ash, and fat content of dried clam
were increased and moisture content was reduced compared to fresh clam under all drying methods. Drying
reduced the microbial load of clam under various drying methods compared to fresh clam. Sensory parameters of
dried clam are highly acceptable in infrared drying followed by solar-LPG hybrid drying. In general, infrared
drying has resulted in the best drying, physical, quality, microbiological, and sensory characteristics of clam but
economical indicator values were less than solar-LPG drying. The selection of a suitable drying method is not
only based on the quality and drying characteristics but also by considering the economical feasibility of the
dryers. Hence, it can be concluded that the solar-LPG dryer was found to the best drying method for clam drying
with the final moisture content of 12.23 %wb, drying efficiency of 35.86 %, maximum drying rate of 1.26 (kg
water/kg dry matter. h), specific energy consumption of 4.73 (kWh/kg of water evaporated), shrinkage of 16.52
%, rehydration ratio of 2.25 %, payback period of 1.14 years, and benefit-cost ratio of 2.Not Availabl
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No full textNot AvailableThe Calibration Approach proposed by Deville and Sarndal (1992) is one of the other techniques widely used for making efficient use of auxiliary information in survey estimation. Beyond the population parameters, there are sub-populations or domains for which estimates are needed to be generated now days. In this study, a domain calibration estimator for domain total were developed under two stage sampling design with assumptions of availability of auxiliary information at both PSU and SSU level of selection. The variance of the estimators and the corresponding variance estimators were also found. Both the proposed estimators were verified through limited simulation studies by generating an artificial population in R software. Various combinations of PSU and SSU sample sizes were drawn from each of the domains to draw the conclusions. Through the simulation study, it was found that all the proposed calibration estimators under two stage sampling design were performing at par with the Horvitz-Thompson estimator of the domain total under two stage sampling design with respect to the criteria of percent relative bias and performing consistently better than the HT estimator for the criteria of percent relative root mean square error.ICAR-IASRI, New Delh
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No full textNot AvailableMachine learning is revolutionizing sample surveys by improving data collection, analysis, and utilization. It combines advanced statistical techniques
with computational algorithms to enhance survey sampling methods and data
quality. Machine learning algorithms optimize survey sample design by identifying relevant variables, detecting patterns, and constructing efficient sampling strategies. They also assist in preprocessing and cleaning survey data,
automatically detecting errors, imputing missing values, and handling outliers. Moreover, machine learning enables predictive modeling and estimation
in sample surveys, leveraging large-scale data to generate models that predict
outcomes, estimate population parameters, and uncover complex relationships
among variables. Integrating machine learning into survey practices leads to
more efficient and informative surveys, benefiting decision-making processes
across various domains. Overall, machine learning has the potential to transform sample surveys, enabling more accurate predictions and estimations and
improving the overall effectiveness of surveys. The application of machine
learning in sample surveys and its potential future applications are described
in the study.Not Availabl
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No full textNot AvailableAdvancements in information sciences can play a vital role in strengthening the nation’s
sustainable agriculture goals. In this direction, we propose a framework for a text-based query-response
generation system to cope with the demand for timely help to the nationwide Indian farmers. One of
the major challenges in designing such systems is constructing a knowledge base that can answer plantprotection-related questions from a diverse population of farmers. To tackle this problem, the past eight
years’ call-log records from the countrywide farmers’ helpline network are collected and processed to
construct the required knowledge base. Additionally, three response-retrieval models with approximate
matching and spatial-based searching functionality are developed to administer the user input questions and
extract relevant answers from the base. To validate the performance of the proposed framework, a diversified
question bank consisting of 755 queries covering 151 crops in India is compiled. Three metrics (Accuracy
Percentage, Crop-weighted Performance Score, and Average Response-retrieval time) are considered for the
models’ assessment. Experimental results show that AgriResponse is a practical framework in real-world
applications, with the different retrieval models useful for different scenarios.Not Availabl