Pradeep Mishra

• Ph.D (Ag. Statistics)
• Professor (Assistant) at Jawaharlal Nehru Krishi Vishwa Vidyalaya

This study analyses the trends and randomness in soybean area, production, and yield over a 54-year period for Madhya Pradesh and India. The Test of Randomness was applied to examine whether the data followed a random pattern or exhibited systematic behaviour. Results revealed that all parameters: area, production and yield showed significant devia...

Quality of life in general population before and during pandemic is topic need to be address by researcher in terms of mobility, self-care, usual activities, pain/discomfort and anxiety/depression. The study was carried out among Saudi population. Data were collected from general population using questionnaire during the period from 22 August 2021...

The present paper explores the instability and growth in area, production, and yield of pearl millet and maize in India. For this, the Cuddy-Della Valle (CDV) instability index and compound annual growth rate (CAGR) are evaluated using secondary time series data on area, production, and yield of pearl millet and maize pertaining to the period of th...

Artificial intelligence and the Internet of Things (IoT) are revolutionizing agriculture by enabling intelligent systems capable of real-time monitoring, control, and visualization of farm operations. These technologies conserve soil fertility, reduce water, pesticide, and herbicide use, and improve labour efficiency. However, poor data quality, re...

This book aims to explore the groundbreaking intersection of artificial intelligence (AI) and agriculture, focusing on how innovative technologies can be harnessed to create sustainable and resilient food systems. As global challenges such as climate change, population growth, and resource scarcity intensify, this book seeks to provide a comprehens...

This study employs Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models to forecast tea production, cultivated area, and yield in India using historical data from 1918 to 2023. The dataset was preprocessed through normalization, exploratory analysis, and division into training and testing subsets. Performance metrics, includ...

This study examines trends in potato production across five major global producers (China, India, Russia, Ukraine and the US) using annual data from 1961 to 2022. We evaluate the T-ARMA, ARIMA-ARCH, Weibull and score-driven models to forecast production from 2023 to 2030. The results identify the T-ARMA model as optimal for China and India (validat...

The daily climate data collected for Hisar district between November 1, 1977 and April 30, 2022, has been analyzed and presented in this study. The data set was divided into two parts: training and testing data. This study presents the results of ARIMA, state space, and seasonal Holt-Winters models fitted for maximum temperature, minimum temperatur...

This study aims to analyze the impact of the food security crisis on the travel and tourism sector, focusing on the role of targeted digital advertising, such as Google Ads, in enhancing the sector's resilience. The study explores how digital marketing can be used to steer tourist demand, increase traveler awareness of sustainable destinations, and...

The daily climate data collected for Hisar district between November 1, 1977 and April 30, 2022, has been analyzed and presented in this study. The data set was divided into two parts: training and testing data. This study presents the results of ARIMA, state space, and seasonal Holt-Winters models fitted for maximum temperature, minimum temperatur...

Time series analysis using machine learning is vital for forecasting in commodity sciences. This research leverages advanced machine learning models for time series forecasting of fish production at both state and national levels in India. The study developed and compared traditional models, like the autoregressive integrated moving average (ARIMA)...

This study examined trends, sustainability, and forecasted potato area, production, and productivity in India and Rajasthan from 1970 to 2030. Annual potato production data was analyzed using an autoregressive integrated moving average (ARIMA) model. The models were trained using data from 1970 to 2020 and assessed with a validation set from 2021 t...

The purpose of this study is to estimate and predict onion wholesale price volatility using statistical and machine learning algorithms. Traditional models like ARIMA and GARCH were compared against advanced machine learning techniques like SVM, LSTM, and CNN, as well as hybrid approaches like ACF-LSTM, RF-LSTM, ACF-CNN, and RF-CNN. The performance...

Mango is a bulky and nutritious fruit crop and it had high demand for it's delicious taste, soothing aroma, varietal diversity and wide adoptability to meet food and nutritional security. India is the largest producer of mango among that West Bengal is one of the contributors in the Indian mango 351 basket. Malda district alone contributes more tha...

In a first, we used Preference Ranking Organization Method and Geometrical Analysis for Interactive Aid (PROMETHEE-GAIA) tool in agricultural research for identifying the best management decision with weakening factors for cultivation of mustard following rice under fifteen regimes of conservation agriculture (CA) practices, because of its versatil...

This study investigated an advanced approach to enhancing security and privacy in healthcare by incorporating artificial intelligence (AI)-based strategies to detect and mitigate data poisoning attacks. The proposed method combined unified learning, homomorphic encryption, and autoencoder-based anomaly detection. It ensured that models were trained...

This study investigated an advanced approach to enhancing security and privacy in healthcare by incorporating artificial intelligence (AI)-based strategies to detect and mitigate data poisoning attacks. The proposed method combined unified learning, homomorphic encryption, and autoencoder-based anomaly detection. It ensured that models were trained...

The primary goal of this research was to evaluate the forecasted behavior of cheese production and total uses in Russia from 1988 to 2020. As a result of a supply-demand imbalance, cheese imports from other nations were necessary to close the gap. Before creating the model, the training and testing sets were split. For both data series, the linear...

The aim of this study is to analyse potato cultivation in South Asian Association of Regional Cooperation (SAARC) countries from 1961 to 2022, based entirely on secondary data from the Food and Agriculture Organization. By employing the ARIMA model, the research forecasts potato area and production up to 2030, with ARIMA (1, 1, 5) identified as the...

The study aims to develop a multivariate statistical model to classify potato yield based on weather variables. The potato yields of India from years 1950 to 2021 were classified into two and three categorical groups based on yield adjusted for trend. Annual and seasonal weather variables such as minimum temperature, maximum temperature and rainfal...

Potato is a food crop at a global scale, bearing a hefty importance for the food security and nutrition of millions of people worldwide. Nonetheless, some obstacles have to be overcome in the cultivation of potatoes, such as susceptibility to a number of diseases that affect quality and yield. Thus, sound disease management approaches are critical...

This paper introduces AI-PotatoGuard, an artificial intelligence (AI) tool which enhances the management of diseases in potatoes through the use of generative models and convolutional neural networks (CNN). In contrast to traditional practices, AI-PotatoGuard is a tool which provides the ability to detect potatoes in the early stages of the disease...

Crop-weather relationship of finger millet varieties under varying environments at Keonjhar, Odisha

The wholesale price index (WPI) is a crucial economic indicator that provides insights into the pricing dynamics of different goods within a country, especially potato commodities. In this study, we tried to build a hybrid machine learning model technique for predicting the volatile price index of potato. We introduced the Random Forest-Convolution...

The cultivation of potatoes is one of the most important parts of the world’s agricultural system, so forecasting methods that can precisely predict the direction of production are needed. We focus on the area of optimization techniques herein in this study and develop a particular use of metaheuristic algorithms applied to improve predictive model...

Please cite this article as: A. Lama, S. Ray, T. Biswas et al., Python code for modeling ARIMA-LSTM architecture with random forest algorithm, Software Impacts (2024), doi: https://doi. Abstract: Over conventional statistical models, machine learning mechanisms are establishing themselves as a potential area for modeling and forecasting complex tim...

To model and forecast complex time series data, machine learning has become a major field. This machine learning study examined Moscow rainfall data's future performance. The dataset is split into 65% training and 35% test sets to build and validate the model. We compared these deep learning models using the Root Mean Square Error (RMSE) statistic....

The present study aims to develop yield forecast models for the Sugarcane crop of the Coimbatore district in Tamilnadu using two different techniques namely Variables and Months in Discriminant function analysis. For this, the Sugarcane yield data for 57 years along with the monthly data on seven weather variables have been taken. For applying disc...

Weather has a profound influence on crop growth, development and yield. The present study deals with the use of weather parameters for sugarcane yield forecasting. Machine learning techniques like K- Nearest Neighbors (KNN) and Random Forest model have been used for sugarcane yield forecasting. Weather parameters namely maximum temperature and mini...

The study aimed to compare ARIMA and Holt's models for predicting coconut metrics in Kerala. The coconut data series was collected from the period 1957 to 2019. Of this, 80% of the data (from 1957 to 2007) is treated as training data, and the rest (20% from 2008 to 2019) is treated as testing data. Ideal models were selected based on lower AIC and...

Sugarcane is the primary agricultural industry that sustains and promotes economic growth in India. In 2018, the majority of India's sugarcane production, specifically 79.9%, was allocated for the manufacturing of white sugar. A smaller portion, 11.29%, was used to produce jaggery, while 8.80% was utilized as seed and feed components. A total of 84...

Agriculture is the backbone of Indian Economy. Proper forecast of food crops and cash crops are necessary for the government in policy making decisions. The present paper aims to forecast Wheat and Sugarcane yield using Random Forest Regression. For the development of Random Forest models, Yield has been taken as dependent variable and variables li...

Online shopping can be done from our convenient places like home, office, etc., and the product will be delivered to the respective places. There are many factors influencing online shopping. The purpose of this study is to develop a statistical model that is used to determine the factors that influence online shopping. In this study, using factor...

This study goes into the essential challenge of estimating potato output in order to ensure sustainable agricultural practices while also providing vital insights into global market patterns. The potato production data series compares the accuracy of two popular forecasting models, ARIMA (AutoRegressive Integrated Moving Average) and ETS (Error-Tre...

Background-field experiment entitled "Effect of sowing dates and seed priming on agrometeorological indices and soil moisture regimes of Chickpea in NCPZ of Odisha" was conducted at RRTTS, Keonjhar, Odisha, during the Rabi season for two consecutive years i.e., 2021-22 and 2022-23. Method: The field experiment was laid out in factorial RBD design w...

This study utilizes time series analysis and machine learning techniques to model and forecast rainfall patterns across different seasons in India. The statistical models, i.e., autoregressive integrated moving average (ARIMA) and state space model and machine learning models, i.e., Support Vector Machine, Artificial Neural Network and Random Fores...

As the second largest potato producer globally, reliable forecasts of output for India and major growing states are crucial. This study developed autoregressive integrated moving average (ARIMA) models alongside state space and gradient boosting machine learning techniques for annual potato production spanning 1967-2020. Model adequacy was evaluate...

India is the second largest producer of potato in the world. The present study aims at examining the growth, sustainability, market and export situations of Indian potatoes during the period 1966-1967 to 2019-2020 using time series data of area, production , productivity, market prices, export amount and values in major growing states, markets and...

This study analyzed and forecasted potato production in eight major South Asian countries from 1961 to 2028 using advanced time series and machine learning approaches. Annual potato production data was modelled with autoregressive integrated moving average (ARIMA), state space, and extreme gradient boosting (XGBoost) models. The models were trained...

The paper is devoted to study the dynamics of the infestation of potatoes owing to the occurrence of late blight disease over two successive years (2014 and 2015) in the Terai region of West Bengal. Nonlinear models have been fitted on the potato late blight data (i.e. percent disease index data). The goodness-of-fit tests on different models have...

This study examines the association between fiscal sustainability indicators and Egypt’s economic growth from 1980 to 2018. Fiscal sustainability refers to a government’s ability to generate sufficient revenue to cover its costs and debt obligations in the long run without excessive borrowing or money creation. Egypt’s economic growth has slowed, r...

The endeavor to implement the 2030 Agenda of national and international stakeholders became increasingly impetuous, considering the wide range of uncertainties and risks. The new humans-centered development model built on the prominence of environmental and social values seeks to reinforce communities’ resilience and mitigate environmental risks, l...

The varying pattern of agriculture data in different phases over a long period could be thought of capturing effectively by the spline regression technique. The present study focuses on using spline regression in estimating the growth rate of production of green gram for Odisha. This technique requires the segmentation of the entire study period in...

Agriculture is the backbone of Indian economy and farmers play significant role in sustaining the sector. However, farmers face several challenges such as poor market access and limited access to market information which adversely affect their marketing performance. Therefore, this study aimed to examine the marketing performance and factors influe...

A 34-year rainfall data from 1976 to 2009 of ten sub-basins of the Vaigai River in Tamil Nadu were collected and analysed statistically using various probability distribution functions. The best-fit probability distributions for the annual, monthly and seasonal rainfall for the study area were found using two goodness-of-fit tests. The Box-Jenkins...

Machine learning mechanism is establishing itself as a promising area for modelling and forecasting complex time series over conventional statistical models. In this article, focus has been made on presenting a machine learning algorithm with special attention to deep learning model in form of a potential alternative to statistical models such as A...

A 34-year rainfall data from 1976 to 2009 of ten sub-basins of the Vaigai River in Tamil Nadu were collected and analysed statistically using various probability distribution functions. The best-fit probability distributions for the annual, monthly and seasonal rainfall for the study area were found using two goodness-of-fit tests. The Box-Jenkins...

Global energy consumption has increased significantly in recent decades due to changes in the industrial and economic sectors. Accurate demand estimates are critical for decision-makers to save operation and maintenance costs, improve energy reliability, and make informed decisions for future development. This study evaluates a newly proposed soft...

Background: Arecanut is popularly known as supari and is grown in many parts of the country. India maintained its first place in production among all the countries. In total world's area and production, India contributes about 49 per cent and 59 per cent respectively. The area has expanded to various states such as Tamil Nadu, West Bengal, Maharash...

The study deals with the use of data mining techniques to build a classification model to predict students' academic performance. The research indicates that the use of machine learning models and data mining methods can reveal hidden patterns and relationships in big data, making them indispensable tools in the field of education analysis. Special...

Forecasting is valuable to countries because it enables them to make informed business decisions and develop data-driven strategies. Fruit production offers promising economic opportunities to reduce rural poverty and unemployment in developing countries and is a crucial component of farm diversification strategies. After vegetables, fruits are the...

"This present study was carried out to examine and analyse the factors determining the labour absorption in agriculture in different agro-climatic regions of Rajasthan with state as a whole. Both primary and secondary data were used for this study. 200 respondents from 10 villages were collected for primary data during year of 2018-2019 and seconda...

This study seeks to determine how economic policy uncertainty (EPU) influences investment decisions and the market value of the Pakistan Stock Exchange. This study examines investment and operational data from 249 energy and petroleum companies between 2015 and 2020 and macroeconomic variables such as EPU. This study investigates the moderating eff...

Forecasts are valuable to countries to make informed business decisions and develop data-driven strategies. The production of pulses is an integral part of agricultural diversification initiatives because it offers promising economic opportunities to reduce rural poverty and unemployment in developing countries. Pulses are the cheapest source of pr...

This study seeks to determine how economic policy uncertainty (EPU) influences investment decisions and the market value of the Pakistan Stock Exchange. The study examines investment and operational data from 249 energy and petroleum companies between 2015 and 2020, in addition to macroeconomic variables such as EPU. This study investigates the mod...

The wheat crop dominates Indian agriculture, making it vital for policymakers and food security planners to anticipate wheat production. In order to forecast wheat production statistics for India and five of its major wheat-producing states from 1950–51 to 2019–20, the research empirically compares the two most popular forecasting techniques Holt's...

India is a major producer of pulses around the world, which constitute an essential component of vegetarians' protein-rich diets in India. The present study attempts to apply the autoregressive integrated moving average (ARIMA) and Holt linear trend model approach to investigate lentil production trends in Bihar, Madhya Pradesh, Uttar Pradesh, West...

Designs of the experiment have significant application in different sectors like clinical, agriculture, laboratory, etc. Various experimental designs have been used to compare treatments and select the best treatment. The right decision with higher precisionism is not possible without considering the rule of experimental design. The present investi...

To verify, regulate and monitor processes, it is critical to use visual as well as analytical tools to identify outliers. The root cause analysis of a possible outlier should be done using analytical approaches to establish, if a suspect point is indeed an outlier and whether it should be deleted from the data set. While outliers can be spotted vis...

The objective of this study is to measure compound growth and predict future area, production and yield of maize in Punjab province and entire Pakistan using time series data collected from various government reports for the period of 1981-2019. Area, production and yield under maize crop showed positive compound growth in Punjab province and Pakis...

The present paper attempts to study the effect of wheat production in the Kurukshetra area of Haryana, India, as affected by changes in meteorological conditions.The study examined 35 years of time series data on wheat yield as well as weekly data on five weather variables for the crop season from 1985-86 to 2019-20. Using weather indices and time...

Countries can use forecasts to establish data-driven strategies and make educated commercial decisions. In order to minimize rural poverty and unemployment in developing nations, the development of cash crops is a crucial component of agricultural diversification projects. A comparison of the ARIMA, ETS, and NNAR models for forecasting area, produc...

Sampling techniques play an important role in determining the efficiency of control charts. The study was designed to develop a new Shewhart-type x control chart to monitor processes using a newly developed cost-effective method of ranked set sampling namely Stratified Balanced Quartile Ranked Set Sampling (SBGQRSS). SBGQRSS is a recent sampling de...

This study investigated the relationship between financial development and inflation in Egypt from 1980-2018. The study employed various econometric techniques, such as Johansen's test for co-integration, error correction model, Granger's causality test, Toda-Yamamoto causality test, and dynamic ordinary least squares model. The findings indicated...

Environmental quality and climate change have become hot topics among academics in all scientific fields in recent decades due to their impact on human health and economic development. Hence, this paper investigates the key factors of carbon dioxide emissions in India from 1970–2020 through the Bayer-Hanck test and Augmented ARDL framework on an au...

A study examined the changes in wheat production in Uttar Pradesh state of India and the factors affecting it from 1950 to 2018. The whole period was divided into three parts, Period-I (1950 to 1975), Period-II (1976 to 2000), and Period-III (2001-2018), to analyze instability and sources of growth. Period-I witnessed the highest instability in are...

The study was undertaken with the objective to develop strategies for certification of wood and woodcraft products for Saharanpur District of Uttar Pradesh, India. The primary data was collected through a preliminary survey from the various stakeholder including manufacturers and exporters, commission agents /brokers traders, and artisans. The data...

To ascertain the amount of nitrogen fixed and mineralization rate by green manure dhaincha were studied at Research Farm of Navsari Agricultural University, Navsari. The experiment consists of six dhaincha (Sesbania bispinosa) genotypes, NSB-6, NSB-7, NSB-9, NSB-13, CSD-123, and CSD-137, subjected to field incorporation and in vitro decomposition....

There is a link between economic progress and Financial Development. In order to analyze the potential for influencing Economic Growth, this study will look at the underlying elements that drive the development of Syria’s Financial Sector. The research team is also speculating on how much Economic Growth these effects will bring. A Dynamic Linear M...

Due to the importance of wheat crop and Punjab being the leading wheat producer, this paper considers hierarchical time series data on wheat production in Punjab. The Punjab state wheat production data is organized in a hierarchy based on geographical regions. Topdown, bottom-up, middle-out, and optimal-combination approaches were used along with s...

For Hisar, Bhiwani, and Sirsa districts, as well as Fatehabad district, the western zone of Haryana has developed zonal wheat yield models based on weather data dating from 1980-81 to 2013-14. Multiple Linear Regression and Principal Component Analysis were employed to achieve this goal. The models’ validity was confirmed for the post-sample years...

This study primarily focused on predicting power use for agricultural purposes because it is a highly useful issue in our daily lives.Therefore, it is crucial to examine the nature of overall power consumption as well as its use for agricultural purposes. We attempted to develop a time series forecasting model to anticipate power usage.The ARIMA an...

The high Himalayas in northern India are an essential source of climate generation and maintenance over the entire northern belt of the Indian subcontinent. It also affects extreme weather phenomena such as western disturbances in the region during winter. The work presented here describes the trends in 117-year precipitation changes and their impa...

Machine learning (ML) has proved to be a prominent study field while solving complex real-world problems. The whole globe has suffered and continues suffering from Coronavirus disease 2019 (COVID-19), and its projections need to be forecasted. In this article, we propose and derive an autoregressive modeling framework based on ML and statistical me...

Machine learning (ML) has proved to be a prominent study field while solving complex real-world problems. The whole globe has suffered and continues suffering from Coronavirus disease 2019 (COVID-19), and its projections need to be forecasted. In this article, we propose and derive an autoregressive modeling framework based on ML and statistical me...

The challenges of fighting poverty and enhancing food security in South Asia have made maize a strategic crop in this region. In this study, maize production in South Asia, encompassing Afghanistan, Bangladesh, Bhutan, China, India, Nepal, Pakistan, and Sri Lanka, was analysed and projected from 1961 to 2027 using state-space and ARIMA models. The...

Pulses are edible dry seeds of the leguminous family, an essential nutritious element to ensure protein for the vegetarian population of India. There seems to be a gap in their domestic production in one of the leading Indian states, Madhya Pradesh. This work aims to study and forecast the future indicators of production, productivity and cultivati...

The main objective of this study is to find out why sugar companies’ revaluation of their fixed assets has no direct financial impact. The purpose of this financial statement analysis of the sugar sector is to help potential investors make better decisions. It can also be used to address information asymmetries and alert investors. Fixed assets for...

This study will indubitably benefit the farmers, policymakers and stakeholders by delivering accurate forecast information of Madhya Pradesh and India. Forecasting is the primary method to predict wheat production in order to identify the situation and determine the value of production for the following year while minimizing production risk. This s...

Pulses are edible dry seeds of the leguminous family, an essential nutritious element to ensure protein for the vegetarian population of India. There seems to be a gap in their domestic production in one of the leading Indian states, Madhya Pradesh. This work aims to study and forecast the future indicators of production, productivity and cultivati...

Training manual available at https://rpubs.com/kumaragri/programming_with_R

Corona Virus is the biggest global health disease and it is an epidemic according to the World Health Organization (WHO). The purpose of this paper is to identify the best fitted model among BATS, TBATS, Holt's linear trend and ARIMA based on the minimum value of AIC and MAE and forecasting data about corona virus from SAARC and China countries. Cu...

Weather factors such as temperature and humidity are indispensable for good agriculture.The best-suitable products can be selected according to the optimal of these weather factors. In this study, data on maximum temperature, minimum temperature, morning relative humidity and evening relative humidity was analyzed from 31 st January, 1921 to 31 st...

This research aimed to propose an index that measures the degree of economic uncertainty in Syria. As it is a difficult situation to predict, the economic future's uncertainty may be a latent variable that drives random fluctuations in the variables of the economy. The index was estimated within Bayesian inference based on the Stochastic Volatility...