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Agri Finance Mate: Smart Agricultural Loan Calculator & Advisory Tool

Aparna Shendkar, Vikas Nandeshwar, Soham Manjaratkar, Harsh Sonar, Gauri Solanke, Soham Vyavahare, Saksham Sonawane, Bhushan Sopal

F.Y.B. Tech Students’ Applied Science & Engineering Project1 (ASEP2) Paper, SEM 2 A.Y. 2024-25 Vishwakarma Institute of Technology, Pune, INDIA.

Department of Engineering, Sciences and Humanities (DESH) Vishwakarma Institute of Technology, Pune, Maharashtra, India ***

Abstract- Many farmers struggle to understand how agricultural loans work and how much they need to repay. This paper presents, Agra Finance Mate, is an easy-to-use tool that helps farmers calculate important loan details like the amount they can borrow, the interest they will pay, and their monthly installments (EMIs). By entering basic details like the type of crop, land size, and income, farmers can quickly get useful financial information. This tool is designed to make the loan process clearer and easier, especially for farmers who may not have access to financial experts. It also supports better planning and helps build trust between farmers and banks. In this paper, we explain how we created Agri Finance Mate and how it can help improve the way farmers manage their finances.

Keywords Agri Finance Mate, Agriculture, Crop dvisory, EMI, Financial Literacy, Firebase, Government Schemes, Loan, Mobile App, Offline Mode, Subsidy, User Experience (UX), Web Application

I. INTRODUCTION

Agriculture isthe backbone ofmanycountries,especially developing countries. A significant population in many countries relies on agriculture as a source of livelihood. Though agriculture has much significance, this sector often faces numerous challenges, including inadequate accesstocredit,poorfinancialplanning,andashortageof tools required for effective budgeting and investment analysis. These issues obstruct the growth and sustainability of the farms, particularly for small landholders who operate on limited resources. To address these challenges, the use of technology in agricultural finance can be a suitable solution. The agricultural finance calculator is such a kind of innovation. It is a digital tool designed to assist farmers, agricultural businesses, and policymakers in making proper financial decisions. This calculator aims to

simplify complex financial concepts such as cost estimation, loan repayment plans, profit forecasting, and investments by providing a user-friendly interface suitableforagriculturalcontexts.

This research paper mainly explores the development and impact of the agricultural finance calculator. The paper mainly highlights its role in improving financial literacy, enhancing access to credit andloans,andassistingsuitableagriculturalpractices.By leveraging this tool, stakeholders in the agricultural sector can generate better financial revenue and ultimately contribute to food security and rural development. Altogether empowering the nation's growth.

Further, the paper is elaborated in different parts/sections,whereISectionhastheIntroductionpart, II Section has Literature Review, Section III comprised Methodology, Section IV contains research and discussion,SectionVhasResults,andSectionVIcontains conclusion.

II. LITERATURE REVIEW

Prior studies have concentrated on digital services, insurance, and microloans to help farmers. Personalized risk assessments and adaptable financial solutions are still lacking,though, which emphasizesthe necessityof a comprehensive, data driven fetch platform designed specifically for smallholder farmers.[1] By enhancing irrigation infrastructure, which uses roughly 70% of water, water-saving agriculture helps to alleviate the agricultural water issue. To build these infrastructures and encourage sustainable farming, public funding is crucial.[2] In an effort to increase access to financial services, research focuses on streamlining farmer loan applications by utilizing machine learning models like logistic regression and random forest to predict loan

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eligibility and algorithms like K-Nearest Neigh bur for tailored suggestions.[3][9][5]A study that used a variety oftechniquestodetermineloaneligibilitydiscoveredthat modelssuchasRandomForestmayachieveupto95.55% accuracy. This suggests that applying such predictive algorithmscanspeedupandimprovethereliabilityofthe loan process, which is helpful for developing tools connected to loans. [4] Using soil and meteorological data, a unified system that combines machine learning and deep learning makes suggestions for crops and fertilizerandusesimagestoidentifyillnesses.Analysis to assist farmers in increasing their output. [6] Using RAG technology, another study created a web platform that helps farmers comprehend government scheme documentation in their own tongue while also offering agricultural guidance, yield prediction, and early disease detection.[7]Amobileexpertsystemthatprovideswork scheduling, crop advising, weather notifications, and financial tracking is presented in this study for Indian farmers. It assists farmers in making well informed decisions and increasing production by giving them upto-date information about farm finances and initiatives. [8] Agri Finance Mate is an easy-to-use site that offers financial literacy, crop advise, application support, loan computation,andplanrecommendationstohelpfarmers. The system will make agricultural financing easier and provide farmers with timely, individualized financial solutions. [10] This studyusesIoTand machine learning to improve crop advice and soil testing, which helps farmers increase yields and enable early disease detection for better crop health [11] This study uses the AODE algorithm to predict crops and recommend fertilizers, which helps farmers increase yields. [12] This study uses IoT soil fertilizer monitoring in conjunction with machine learning to provide precise crop recommendations, which helps farmers maximize fertilizer use and increase yields. [13] [15] Machine learning is also successfully applied in horticulture for cropprediction,diseasedetection,andyieldoptimization, whichimprovesdecision-makinginmodernfarming.[14] While previous research has explored microloans, insurance, IoT-based soil testing, and crop recommendationsystems,thereisstillalackofaunified, personalized fintech platform tailored specifically for smallholder farmers. Most systems either focus on agriculture or finance separately, without integrating both. Additionally, many farmers struggle to understand financial and government-related information when it is availableonlyinEnglish.Therefore,thereisastrongneed for multilingual and locally relevant platforms. Our work addresses this gap by developing a data-driven system thatprovides financial tools,crop recommendations,and government scheme guidance in farmers' local language,

helping them make better decisions and improve productivity.

III. METHODOLOGY/EXPERIMENTAL

ThedevelopmentofAgriFinanceMatewascarriedoutin structured phases to ensure the platform meets the core needsofsmall-scaleandsemi-literatefarmers.Amodular design approach was adopted, targeting affordability, easeofuse,andofflinefunctionality.

A. System Overview

Agri Finance Mate is a mobile-first web application that providesanintuitiveinterfaceforcalculatingagricultural loan details, accessing subsidy schemes, and improving financial awareness among farmers. The development of Agri Finance Mate employed a modular and scalable architecture leveraging the Next.js framework, styled with Tailwind CSS, and powered by Firebase Realtime Database for backend operations. The application was designed with a mobile-first and offline-aware approach, consideringtheruralfarmingpopulationwith Limiteddigitalaccess

B. User Interface and Design

MockupsweredesignedusingFigmaandCanva,focusing on an icon-based, minimal interface. The app design avoids clutter and uses simple inputs to facilitate nontechnical users. Color-coded buttons and large text were prioritized to ensure visibility and clarity on low-end devices.

Later on, User interface components were created using Tailwind CSS utility classes, ensuring responsiveness across devices. Minimalist design principles were followed, prioritizing large text, high contrast buttons, and icon-based navigation to aid farmerswithlimiteddigitalexperience.

Figure 1: Homescreen

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C. Key Features and Functionalities

Agri Finance Mate isn’t just about loan calculations, it is anoveralladvisorytoolthathelpsfellowfarmersnotjust learn the market prices but also get educated and gain awarenessofvariousotheropportunitiesinagriculture.

1) Loan Calculation Module

A reusable component was developed in /src/components/toacceptthreeinputs:

• LoanAmount

• InterestRate(Annual)

• Loan Tenure (Years) A simple EMI formula was implementedin

TypeScript:

EMI=[P×R×(1+R)^N]/[(1 +R)^N–1]

The component outputs monthly EMI, total interest, and totalrepaymentinreal-time.

2) Scheme Advisory Logic

Users input their crop, region, and season, which are matched against a JSON-driven database of schemes hosted in Firebase. Matching logic is done via string comparisonandoptionalriskcategorization.

3) Financial Literacy Integration

A dedicated section fetches YouTube tutorial videos embedded directly into the site, explaining terms like EMI,creditscore,andsubsidyeligibility.Thesevideosare embeddedusing<Iframe>tagsforseamlessstreaming.

4) Offline Access (PWA Setup) Using next-pwa and service worker configuration, the application caches key pagessuchas:

• Calculator

• Schemelist

• Literacysection

This ensures that users in low or no-connectivity zones canstillaccessvitaltoolsoffline.

D. System Architecture and Design

TheAgriFinanceMateapplicationfollowsamodularand client-serverarchitectureoptimizedforruralaccessibility and responsive design. The architecture integrates rontendinterfaces,aFirebasebackend,andalogicengine forEMIcalculationandpersonalizedadvisory.

The system is broadly divided into three layers:

A. Presentation Layer (Frontend) Thislayer consists oftheuserinterfacedevelopedusingNext.jsandTailwind CSS. It includes input forms, result displays, navigation elements, and video embedding components. This layer handlesuserinteractionandrenders:

• Loancalculator

• Schemesuggestions

Figure 2:FunctionSelect

Figure 3: Loancalculator

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• Financialliteracysection

B. Application Logic Layer Businesslogiciswrittenin TypeScriptcomponents,whichprocess:

• EMIcalculationsusingmathematicalformulas

• Inputvalidation

• Advisory logic based on user’s crop, region, and season

C. Data Layer (Firebase Backend) This layer uses FirebaseRealtimeDatabasetostore:

• Userpreferences(ifneeded)

• Schemedata(manuallyaddedJSONordynamic)

• OfflinecachinghandledviaPWAserviceworkers Firebase Authentication can be added optionally to manageindividualuserdatasecurely.

D. Offline Capability

Offline access is enabled using PWA configuration and local storage caching to ensure uninterrupted access to calculatorsandguides,eveninpoorconnectivityareas.

Figure 4: Execution Diagram

E. Pseudo Code START

// //1.LOANCALCULATORMODULE //

DISPLAY"EnterLoanAmount:" INPUTloanAmount

DISPLAY"EnterAnnualInterestRate(%):" INPUTinterestRate

DISPLAY"EnterTenure(Years):" INPUTtenureYears

CALLcalculateEMI(loanAmount,interestRate, TenureYears)

FUNCTIONcalculatesEMI(P,R_percent,N_years): R=R_percent/(12*100) //Monthlyrate N=N_years*12 //Months EMI=(P*R*(1+ R)^N)/((1+R)^N-1) totalPayment=EMI*N total Interest=totalPayment-P DISPLAYEMI,totalPayment,totalInterest RETURN ENDFUNCTION

// //2.CROP&REGIONBASEDINPUTS //

DISPLAY"EnterCropType:" INPUTcropType

DISPLAY"EnterRegion:" INPUTregion

DISPLAY"EnterSeason:" INPUTseason

// //3.SCHEMESUGGESTIONSMODULE //

SchemesList = FETCH_FROM_FIREBASE("schemeData") FilteredSchemes=[]

FOReachschemeIN schemes List: IF scheme. Crop== cropTypeORscheme.region==region:

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ADDschemeTOfilteredSchemes ENDIF ENDFOR

DISPLAY"MatchingGovernmentSchemes:" DISPLAYfilteredSchemes

// //4.CROPADVICEMODULE //

CropAdviceList = FETCH_FROM_FIREBASE("cropAdvice")

FOR each advice IN crop AdviceList: IF advice.crop == cropTypeANDadvice.season==season: DISPLAY"Advicefor",cropType,":",advice.tips ENDSIF ENDFOR

// //5.LOANAPPLICATIONGUIDE //

DISPLAY"Doyouwanthelpapplyingforaloan? (Y/N)”INPUTguideChoice

IFguideChoice=="Y":

DISPLAY"Step1:Checkeligibility" DISPLAY"Step2:Gatherrequireddocuments"

DISPLAY"Step3:Applyonofficialportalorvisitoffice" DISPLAY"Step4:Trackapplicationstatus" ENDIF

// //6.FINANCIALLITERACYSECTION //

DISPLAY"Wouldyouliketowatchfinancial Videos? (Y/N)" INPUTlearnChoice

IFlearnChoice=="Y": videoList=[ "WhatisEMI?", "Howinterestworks", "Understandingcreditscore", "Loansafetytips" ] FOReachvideoINvideoList: EMBED_YOUTUBE(video) ENDFOR ENDIF

// //7.OFFLINEMODESETUP(PWA) //

CACHEpages:[LoanCalculator,LiteracyVideos, Schemes,Advice] DISPLAY"Offlinemodeenabledforfutureuse"

END

IV. RESULTS AND DISCUSSIONS

The Agri Finance Mate platform was successfully developed and tested as a functional prototype. The application allows users to calculate agricultural loan repayment details, access government schemes, view crop-specific financial advice, and learn financial concepts through embedded videos. All modules performedasexpectedduringinternaltesting.

The following modules were evaluated:

ModuleResult

Accurate EMI, interest, and raiment o output with realtime Calculator Calculations

Module Result Scheme Suggestions

Loan

Application Guide

Financial Literacy

Successfully filters schemes by crop and region using dummy Firebasedata

Displays clear, user-friendly steps withnolagorerrors

Embedded YouTube videos play correctly and are readable on mobile

Displays stored crop-specific tips Crop Advisory Based on user selection Calculator and static content Offline Mode remain accessible in no-internet conditions (via PWA)

B. Performance Observations

• Responsiveness:UIperformedwellonbothdesktop andmobilebrowsers.

• Usability: All content is structured for semiliterate usersusingminimaltextandiconbasednavigation.

• Accuracy: EMI logic matched real-world loan calculatorswithverifiedresults

• Offline support: Local caching was successful using service workers; scheme data and calculator remainedavailableoffline.

C. Limitations Identified

• Schemedataisstaticandmanuallyentered;itdoes notyetfetchliveupdates.

• FinancialliteracyvideosarelimitedtoYouTubeand require active connection for full stream (unless preloaded).

• Multi-language and voice assistant features are not yetimplementedbutareplanned.

• Responsiveness is a little slower Difficulty in integratinglivemarkettrendsandpricestoprovide real-time accurate information. Live weather updatestobe Perfected

V. Future Scope

1. Expansion into Micro-Insurance: Predictive modeling can be extended to crop insurance risk management,identifyingpotential climate risks, and suggestingcoverageplansdynamically.PrecisionFarming through Data Fusion: Combine satellite imagery, drones, and soil/meteorological IoT data to offer hyper personalized advisories, detect pest outbreaks, and optimizeresourceusage(likewaterandfertilizers).

2. Integration with e-Marketplaces: Connectingsmartadvisorysystemstodigitalagri market platforms for better price realization and credit-based selling.

3. Holistic Agri- Fin tech Platforms: Development of integrated platforms combining loan eligibility prediction, credit scoring, crop recommendation, and insurance guidance tailored to farmer profiles using AI/ML and real time data from Iot sensors.

VI. CONCLUSION

The Agri Finance Mate project seeks to give address to those financial literacy issues that can be meaningful for farmers located in rural areas. Many farmers do not understandinterestrates,howtorepayloans,alongwith the true cost of borrowing. They do battle with all of the complexities of both loans and subsidies. This often results from debt cycles, loan rejection, or schemes' underutilizationAgriFinanceMateoffersupasolutionby assisting you financially in such simple terms. It has

Figure 5: loancalculator

Figure 6:Guide Figure 7:

simple calculators that are still detailed, builds awareness, and grants scheme access. It also gives farmers advice about key money ideas like credit and EMI. The project seeks to help farmers with understanding various loan schemes and repayment plans,clearlyvisualizeEMI,totalinterest,andrepayment timelines, advise them based on crop type, region, and season, and give them awareness of and access to relevant agricultural subsidies and schemes through links. It also has an endeavor to try to increase financial literacybywayofeducationalcontent.

Agri Finance Mate's key features include Financial Literacy Videos along with an Offline Mode for areas with poor connectivity plus Crop Based Advice, a Loan ApplicationGuide,Scheme&SubsidySuggestions,alsoan EMI & Interest Calculator Future plans for Agri Finance Mate involve integrating with government schemes as well as subsidies, therefore it will implement AI-based loan prediction plus risk scoring to predict repayment capacity,suggestidealloanamountsplusterms,thenflag high-riskborrowingpatterns.Italsoseeksdatacollection for policymaking thatlets governmentstrack loanusage patterns,letsbankscreateflexibleloanproducts,andlets NGOs target financial education campaigns. We plan for crop-specificfinancialmatterslater.Furthermore,wewill have to add voice-based interfaces to regional languages because they are accessible, and we will have to fully deploytheprogrambecausewereachallfarmers.

VII. ACKNOWLEDGMENT

We express deepest gratitude to our project guide and faculty members at VIT Pune for their constant support and guidance throughout this journey. Their valuable insights helped us shape our ideas and tackle challenges effectively. A big thank you to our teammates for their dedication, teamwork, and countless hours of effort in bringing this project. We also appreciate the incredible work done by researchers in AI and the field of Agriculture, which provided us with a strong foundation tobuildupon.Lastly,aheartfeltthankstoourfriendsand family for their encouragement and belief in us it kept us going every step of the way! for their encouragement andbeliefinus itkeptusgoingeverystepoftheway!

VIII. REFERENCES

[1] I. S.Joyet al., "Revolutionizing Agricultural Finance: Simplifying Farmer Access to Financial Tools with an Innovative Fintech Platform," 2024 2nd World Conference on Communication & Computing(WCONF),RAIPUR,India,2024,pp.

[2] Z. Li and Y. Chen, "Water-Saving Agriculture Based on Financial Support," 2010 International Conference on Management and Service Science, Wuhan, China, 2010, pp.1-4,

[3] A. Imtiaz, S. Nachiket, K. V. Nishanth, J. Angadi and T. C. Pramod, "Agricultural Loan Recommender System - A Machine Learning Approach," 2021 International Conference on Innovative Trends in Information Technology(ICITIIT), Kottayam,India,2021,pp.1-5,

[4] U.E.Orji,C.H.Ugwuishiwu,J.C.N.NguemaleuandP.N. Ugwuanyi, "Machine Learning Models for Predicting Bank Loan Eligibility," 2022 IEEE Nigeria 4th InternationalConferenceonDisruptiveTechnologiesfor Sustainable Development (NIGERCON), Lagos, Nigeria, 2022,pp.1-5.

[5] A.S,V.J,M.J.M.M.Jenitha,M.L.M. GladenceandM.T. G. R. Angel, "Predicting Loan Risk in Banking Sectors using Machine Learning," 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL),Bhimdatta,Nepal,2025,pp.1619-1624

[6] S. Bhansali, P. Shah, J. Shah, P. Vyas and P. Thakre, "Healthy Harvest: Crop Prediction And Disease Detection System," 2022 IEEE 7th International conference for Convergence in Technology (I2CT), Mumbai,India,2022,pp.1-5

[7] S. C, M. Vaseekaran, M. C. Prabhu, M. S. Reddy, R. K. Mishra and M. R. Reddy, "AGRI-SMART HUB: A Multilingual Integrated Platform for Comprehensive FarmingSolutionsandSupport,"20255thInternational ConferenceonTrendsinMaterialScienceand Inventive Materials (ICTMIM), Kanyakumari, India, 2025, pp. 1806-1811

[8] S. Shikalgar, M. Kolhe, N. Bhalerao, S. Pansare and S. Laddha, "A cross platform mobile expert system for agriculture task scheduling," 2016 International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India, 2016, pp. 835-840

[9] V. Singh, A. Yadav, R. Awasthi and G. N. Partheeban, "PredictionofModernizedLoanApprovalSystemBased on Machine Learning Approach," 2021 International Conference on Intelligent Technologies (CONIT), Hubli, India,2021,pp.1-4,

[10] I. S. Joy et al., "Revolutionizing Agricultural Finance: Simplifying Farmer Access to Financial Tools with an Innovative Fintech Platform," 2024 2nd World ConferenceonCommunication& Computing(WCONF),RAIPUR,India, 2024,pp.1-8,

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[11] P.SandV.K,"OptimizingCropYield-AnIoTand MLbased Soil Testing and Crop Recommendation System," 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT),Bengaluru,India,2025,pp.789-795,

[12] M.S.Ali,B.Rohit,R.Roshith,V.BiradarandM.A.Jabbar, "Crop Prediction & Fertilizer Recommendation using AODE Algorithm," 2024 IEEE 9th International Conference for Convergence in Technology (I2CT), Pune,India,2024,pp.1-5,

[13] M.D.Hossain,M.A.Kashem andS.Mustary,"IoTBased Smart Soil FertilizerMonitoring And ML Based Crop Recommendation System," 2023 International ConferenceonElectrical,ComputerandCommunication Engineering (ECCE), Chittagong, Bangladesh, 2023, pp. 1-6

[14] A. Kirti, A. Das and R. Priyanka, "Advancements in Horticulture Development Using Machine Learning Techniques," 2024 2nd International Conference on Networking and Communications (ICNWC), Chennai, India, 2024,pp.1-7,

[15] M.D.Hossain,M.A.Kashem andS.Mustary,"IoTBased Smart Soil Fertilizer Monitoring And ML Based Crop Recommendation System," 2023 International ConferenceonElectrical,ComputerandCommunication Engineering(ECCE),Chittagong,Bangladesh,2023,pp. 1-6,doi: