MURALI KRISHNA SENAPATY

RESEARCH INTERESTS

Machine Learning , IoT and Deep learning

Scopus Publications

A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
Murali Krishna Senapaty, Abhishek Ray, and Neelamadhab Padhy
MDPI AG
Today, crop suggestions and necessary guidance have become a regular need for a farmer. Farmers generally depend on their local agriculture officers regarding this, and it may be difficult to obtain the right guidance at the right time. Nowadays, crop datasets are available on different websites in the agriculture sector, and they play a crucial role in suggesting suitable crops. So, a decision support system that analyzes the crop dataset using machine learning techniques can assist farmers in making better choices regarding crop selections. The main objective of this research is to provide quick guidance to farmers with more accurate and effective crop recommendations by utilizing machine learning methods, global positioning system coordinates, and crop cloud data. Here, the recommendation can be more personalized, which enables the farmers to predict crops in their specific geographical context, taking into account factors like climate, soil composition, water availability, and local conditions. In this regard, an existing historical crop dataset that contains the state, district, year, area-wise production rate, crop name, and season was collected for 246,091 sample records from the Dataworld website, which holds data on 37 different crops from different areas of India. Also, for better analysis, a dataset was collected from the agriculture offices of the Rayagada, Koraput, and Gajapati districts in Odisha state, India. Both of these datasets were combined and stored using a Firebase cloud service. Thirteen different machine learning algorithms have been applied to the dataset to identify dependencies within the data. To facilitate this process, an Android application was developed using Android Studio (Electric Eel | 2023.1.1) Emulator (Version 32.1.14), Software Development Kit (SDK, Android SDK 33), and Tools. A model has been proposed that implements the SMOTE (Synthetic Minority Oversampling Technique) to balance the dataset, and then it allows for the implementation of 13 different classifiers, such as logistic regression, decision tree (DT), K-Nearest Neighbor (KNN), SVC (Support Vector Classifier), random forest (RF), Gradient Boost (GB), Bagged Tree, extreme gradient boosting (XGB classifier), Ada Boost Classifier, Cat Boost, HGB (Histogram-based Gradient Boosting), SGDC (Stochastic Gradient Descent), and MNB (Multinomial Naive Bayes) on the cloud dataset. It is observed that the performance of the SGDC method is 1.00 in accuracy, precision, recall, F1-score, and ROC AUC (Receiver Operating Characteristics–Area Under the Curve) and is 0.91 in sensitivity and 0.54 in specificity after applying the SMOTE. Overall, SGDC has a better performance compared to all other classifiers implemented in the predictions.


State-of-the-art of soil mineral data extraction and crop recommendation using learning tools
Murali Krishna Senapaty, Abhishek Ray, and Neelamadhab Padhy
AIP Publishing



An optimized approach towards increasing the sale rate in a Grocery Mart by using Association Rule Mining Approaches
A Sreelakshmi, Neelamadhab Padhy, and Murali Krishna Senapaty
IEEE
Sales can be affected by many factors due to heavy competition in the business world. The analysis to identify the customer’s interest in advance is an important factor in it. Association rule mining in machine learning allows for analyses of the huge dataset to identify associated item sets. A focus is given to the daily sales of grocery datasets. In this paper, we have collected a dataset from a local grocery market and taken initial steps for analysis. It has been identified that the dataset for a year will be analyzed completely to identify the customer’s interests. The customer’s interest also varies based on the season and the customer’s regular purchase habits. Based on the purchase interests observed, regular customers can be encouraged by sending messages about associated product offers. A brief study on the Apriori and FP Growth algorithms has been conducted based on a literature review to determine their performance. Based on the analysis, a model has been proposed in which our dataset can be used for identifying associated datasets based on different factors such as customer and season. The best algorithm shall be selected based on accuracy, execution time, and the number of associated pairs. Further, a hybridization of algorithms and other tools is suggested for enhancing performance.


Enhancing Soil Fertility Prediction Through Federated Learning on IoT-Generated Datasets with a Feature Selection Perspective †
Murali Krishna Senapaty, Abhishek Ray, and Neelamadhab Padhy
MDPI
: Introduction: The fertile soil has a balanced nutrient value of pH, potassium, phosphorus, nitrogen, water retention capability, and organic substances. A fertile soil allows for better plant growth, leading to better production. The soil fertility requirement varies from crop to crop. So it is essential to identify the soil’s fertile level according to the crop type. Objective: The objective of this paper is to develop a robust model that is capable of predicting soil fertility. The model is integrated with the IoT-generated data and federated learning-based feature selection techniques to improve the accuracy of the dataset. Material/Methods: Different feature selection techniques were applied to the dataset. Then we applied machine learning algorithms such as Logistic Regression, Decision Trees, Naive Bayes, and their ensemble to analyze and improve the performance. The federated learning approach is implemented for training the local models using the individual partitioned datasets. Each local model of the client shares the cryptic output weight and bias without sharing raw data. There is a centralized model at the server end that collects these weights and biases by preserving data privacy. These collected data are aggregated and applied to find a least square error (LSE). Then a gradient descent curve (GDC) is applied to identify the optimized weight and bias which shall be fed back again to improve the accuracy of prediction. Result: From our experimental observation, we analyzed the performance metrics of different ML classifiers and it revealed that the ensemble of logistic regression and decision tree has better performance compared to other models. One of our client models generates weight and bias with a precision of 87%, accuracy of 87%, recall of 87%, and F1-Score of 86%. Further, we have collected two of our client system model outcomes at a server model and applied the LSE to identify the optimal W and B. Further, in future work, we shall improve the performance of our model with a recursive approach by verifying the W, and B at the client model in a feedback process.


IoT-Enabled Soil Nutrient Analysis and Crop Recommendation Model for Precision Agriculture
Murali Krishna Senapaty, Abhishek Ray, and Neelamadhab Padhy
MDPI AG
Healthy and sufficient crop and food production are very much essential for everyone as the population is increasing globally. The production of crops affects the economy of a country to a great extent. In agriculture, observing the soil, weather, and water availability and, based on these factors, selecting an appropriate crop, finding the availability of seeds, analysing crop demand in the market, and having knowledge of crop cultivation are important. At present, many advancements have been made in recent times, starting from crop selection to crop cutting. Mainly, the roles of the Internet of Things, cloud computing, and machine learning tools help a farmer to analyse and make better decisions in each stage of cultivation. Once suitable crop seeds are chosen, the farmer shall proceed with seeding, monitoring crop growth, disease detection, finding the ripening stage of the crop, and then crop cutting. The main objective is to provide a continuous support system to a farmer so that he can obtain regular inputs about his field and crop. Additionally, he should be able to make proper decisions at each stage of farming. Artificial intelligence, machine learning, the cloud, sensors, and other automated devices shall be included in the decision support system so that it will provide the right information within a short time span. By using the support system, a farmer will be able to take decisive measures without fully depending on the local agriculture offices. We have proposed an IoT-enabled soil nutrient classification and crop recommendation (IoTSNA-CR) model to recommend crops. The model helps to minimise the use of fertilisers in soil so as to maximise productivity. The proposed model consists of phases, such as data collection using IoT sensors from cultivation lands, storing this real-time data into cloud memory services, accessing this cloud data using an Android application, and then pre-processing and periodic analysis of it using different learning techniques. A sensory system was prepared with optimised cost that contains different sensors, such as a soil temperature sensor, a soil moisture sensor, a water level indicator, a pH sensor, a GPS sensor, and a colour sensor, along with an Arduino UNO board. This sensory system allowed us to collect moisture, temperature, water level, soil NPK colour values, date, time, longitude, and latitude. The studies have revealed that the Agrinex NPK soil testing tablets should be applied to a soil sample, and then the soil colour can be sensed using an LDR colour sensor to predict the phosphorus (P), nitrogen (N), and potassium (K) values. These collected data together were stored in Firebase cloud storage media. Then, an Android application was developed to fetch and analyse the data from the Firebase cloud service from time to time by a farmer. In this study, a novel approach was identified via the hybridisation of algorithms. We have developed an algorithm using a multi-class support vector machine with a directed acyclic graph and optimised it using the fruit fly optimisation method (MSVM-DAG-FFO). The highest accuracy rate of this algorithm is 0.973, compared to 0.932 for SVM, 0.922 for SVM kernel, and 0.914 for decision tree. It has been observed that the overall performance of the proposed algorithm in terms of accuracy, recall, precision, and F-Score is high compared to other methods. The IoTSNA-CR device allows the farmer to maintain his field soil information easily in the cloud service using his own mobile with minimum knowledge. Additionally, it reduces the expenditure to balance the soil minerals and increases productivity.


Cloud-based data analytics: Applications, security issues, and challenges




Enrichment of Antenna Gain of a Biconvex Patch with a Novel Superstrate
Ribhu Abhusan Panda, Murali Krishna Senapaty, Premansu Sekhar Rath, and Debasis Mishra
IEEE
This paper illustrates the mathematical derivation for the biconvex shaped patch which can be used for the 5G application. The gain enhancement has been done by using the superstrate with metal blocks. A 40mm × 40 mm substrate has been taken in which FR4 Epoxy dielectric is used for the substrate with a height 1.6 mm. Air gap between the superstrate and the substrate is also 1.6 mm. The conformal rounded patch has been altered in such a way that it will be shaped as a biconvex lens. The parameters like S-Parameter, SWR, antenna gain etc have been found out and a comparison has been done taking the proposed antenna with and without superstrate. The effect of metal blocks that are used on the superstrate is considered for gain enrichment of the projected model. The return loss (<−10 dB) has been found out to be −31.18 dB at 27.8 GHz with a bandwidth of 5.1 GHz.


IoT based Smart Parking System: A Proposed Algorithm and Model
Sibo Prasad Patro, Padmaja Patel, Murali Krishna Senapaty, Neelamadhab Padhy, and Rahul Deo Sah
IEEE
Today, due to the growth of IoT(Internet of Things) the concept of smart cities has gained considerable popularity. To maximize the productivity and reliability of urban infrastructure consistent efforts are being made in the field of IoT. Many problems such as traffic congestion and road safety are being solved by the use of IoT. Today peoples face a common problem in the parking area to find a free parking slot in cities. In this study, we are designing a Smart Parking System, which will enable the user to find Parking slots in a given parking area. It also avoids unnecessary traveling through filled parking lots. In this paper, the author presents a smart parking system with the help of IoT over Wi-Fi. This intelligent parking system consists of an IoT module that helps to track the availability of each single vacant parking space. The author used an Arduino Uno, which can be embedded over the Wi-Fi module to establish a connection to the internet. This technology helps to transfer the data live. In this smart parking system, with the help of digital IR sensors, the system gets the status regarding the parking slot status, whether it is occupied or vacant. This sensor sends the collected data to the microcontroller. Latter the data are processed, and the status of parking slots is updated in the central database. The IR sensors need to be deployed in the appropriate locations so that the system can cover all the parking slots. Each parking slot is identified with a unique id to identify them on the network

RECENT SCHOLAR PUBLICATIONS

Enhancing Soil Fertility Prediction Through Federated Learning on IoT-Generated Datasets with a Feature Selection Perspective
MK Senapaty, A Ray, N Padhy
Engineering Proceedings 82 (1), 39 2024



A decision support system for crop recommendation using machine learning classification algorithms
MK Senapaty, A Ray, N Padhy
Agriculture 14 (8), 1256 2024



State-of-the-art of soil mineral data extraction and crop recommendation using learning tools
MK Senapaty, A Ray, N Padhy
AIP Conference Proceedings 2919 (1) 2024



An optimized approach towards increasing the sale rate in a Grocery Mart by using Association Rule Mining Approaches
A Sreelakshmi, N Padhy, MK Senapaty
2024 International Conference on Emerging Systems and Intelligent Computing 2024



IoT-enabled soil nutrient analysis and crop recommendation model for precision agriculture
MK Senapaty, A Ray, N Padhy
Computers 12 (3), 61 2023



IoT-enabled soil nutrient analysis and crop recommendation model for precision agriculture. Computers 12 (3): 61
MK Senapaty, A Ray, N Padhy
2023



Cloud-Based Data Analytics: Applications, Security Issues, and Challenges
MK Senapaty, G Mishra, A Ray
The Role of IoT and Blockchain, 373-389 2022



IoT based smart parking system: a proposed algorithm and model
SP Patro, P Patel, MK Senapaty, N Padhy, RD Sah
2020 International Conference on Computer Science, Engineering and 2020



Enrichment of antenna gain of a biconvex patch with a novel superstrate
RA Panda, MK Senapaty, PS Rath, D Mishra
2020 International Conference on Computer Science, Engineering and 2020



IoT-Enabled Soil Nutrient Analysis and Crop Recommendation Model for Precision Agriculture. Computers 2023, 12, 61
MK Senapaty, A Ray, N Padhy
Int. J. Anal. Exp. Modal Anal 12, 1112-1117 2020



A study on Query Processing and Optimization to reduce the usage of system resources in mobile environment
ASL Murali Krishna Senapaty
International Journal of Research and Analytical Reviews 1 (special issue 2019



A stack based cloud computing architecture using clustering
DPSR Murali Krishna Senapaty
ICFTEMST(International Conference on Future Trends in Engineering 2019



Cloud-Based Data Analytics: Applications, Security Issues and Challenges
DAR Murali Krishna Senapaty, Gitanjali Mishra
http://www.appleacademicpress.com/the-role-of-iot-and-blockchain-techniques 2019



A study on Query Processing and Optimization to reduce the usage of system resources in mobile environment
ASL Murali Krishna Senapaty
International Journal of Research and Analytical Reviews, page no 115 2019



A Comparative Study on Query Optimization and Performance Analysis using Different Data Values
AS Murali Krishna Senapaty, Sudhakar Panigrahy
International Journal of Advance Research, Ideas and Innovations in 2017



Performance Testing and Monitoring SQL Queries for Rebuild or Reorganize Operations
MMKS Mr. Sudhakar Panigrahy1 , Mr. Pragnyaban Mishra2
International Journal of Advanced Research in Computer and Communication 2016



Implementing Radix Sort With Linked Buckets Using Lsd Msd And Their Comparitive Analysis And Discussion On Applications. sl
MK Senapaty, P Patel, R Panigrahic
International Journal Of Engineering And Computer Science, 15492-15497 2016



Implementing Radix Sort With Linked Buckets Using Lsd & Msd And Their Comparitive Analysis And Discussion On Applications
RP Murali Krishna Senapaty, Padmaja Patel
International Journal Of Engineering And Computer Science 4 (12), 15397-15402 2015



Real time system for software Engineering: An overview
MKS Rajeeb Lochan Panigrahi
GLOBAL JOURNAL FOR RESEARCH ANALYSIS 3 (1), 3 2014



Performance Testing and Monitoring SQL Queries for Rebuild or Reorganize Operations
MS Panigrahy, MP Mishra, MMK Senapaty

MOST CITED SCHOLAR PUBLICATIONS

IoT-enabled soil nutrient analysis and crop recommendation model for precision agriculture
MK Senapaty, A Ray, N Padhy
Computers 12 (3), 61 2023
Citations: 87



IoT based smart parking system: a proposed algorithm and model
SP Patro, P Patel, MK Senapaty, N Padhy, RD Sah
2020 International Conference on Computer Science, Engineering and 2020
Citations: 19



A decision support system for crop recommendation using machine learning classification algorithms
MK Senapaty, A Ray, N Padhy
Agriculture 14 (8), 1256 2024
Citations: 17



IoT-enabled soil nutrient analysis and crop recommendation model for precision agriculture. Computers 12 (3): 61
MK Senapaty, A Ray, N Padhy
2023
Citations: 8



IoT-Enabled Soil Nutrient Analysis and Crop Recommendation Model for Precision Agriculture. Computers 2023, 12, 61
MK Senapaty, A Ray, N Padhy
Int. J. Anal. Exp. Modal Anal 12, 1112-1117 2020
Citations: 4



An optimized approach towards increasing the sale rate in a Grocery Mart by using Association Rule Mining Approaches
A Sreelakshmi, N Padhy, MK Senapaty
2024 International Conference on Emerging Systems and Intelligent Computing 2024
Citations: 2



Enrichment of antenna gain of a biconvex patch with a novel superstrate
RA Panda, MK Senapaty, PS Rath, D Mishra
2020 International Conference on Computer Science, Engineering and 2020
Citations: 2



State-of-the-art of soil mineral data extraction and crop recommendation using learning tools
MK Senapaty, A Ray, N Padhy
AIP Conference Proceedings 2919 (1) 2024
Citations: 1



Cloud-Based Data Analytics: Applications, Security Issues, and Challenges
MK Senapaty, G Mishra, A Ray
The Role of IoT and Blockchain, 373-389 2022
Citations: 1



Implementing Radix Sort With Linked Buckets Using Lsd Msd And Their Comparitive Analysis And Discussion On Applications. sl
MK Senapaty, P Patel, R Panigrahic
International Journal Of Engineering And Computer Science, 15492-15497 2016
Citations: 1