FertiCal-P: An Android-based Decision Support System (DSS) Determines the NPK Fertilizer Recommendation by Assessing pH and Macronutrient of the Soil

Introduction 

Modernization and urbanization have improved human comforts, but they have also reduced the available resources for food production due to shrinking available fertile land and massive deforestation.¹ In the last fifty years, the world’s population has increased 2 to 2.5 fold, yet food grain production has not kept pace with this demand.²

Farmers have long engaged in the practices of ploughing, fertilization and irrigation, (which are referred to as “Khed, Khatar ane Pani” in Gujarati) in order to cultivate food grain across the Indian continents and also throughout the world.³

It has been proved that at each and every stage of crop life cycle,^4,5 these farming methods are impacted by the presence of uncertainties⁶ When looking for the solution, we found that the agriculture industry and its allied practices faced a significant challenges due to the inherent uncertainties associated with it.^7,8 Advances in computational power, based on engineering solutions, have greatly reduced the consequences of such uncertainty in agriculture.^9,10 The effects of such uncertainty in agriculture have been significantly mitigated as a result of developments in computational tools and techniques, also known as Precision agriculture (PA).¹¹ Precision agriculture is the farming practice that monitors, measures, and responds to crop variability^12,13 and it uses the 5 R’s with the aim of applying the right inputs, at the right amount, to the right place, at the right time, and in the right manner.¹⁴ The objective of precision agriculture research is to develop a decision support system^15,16 (DSS) that enables comprehensive farm management, with the aim of optimizing input yields and preserving the environment^17-19 Precision Agriculture offers an ideal solution by employing specialized equipment, software, and services^20,21 The development and application areas of an expert DSS for agriculture encompass a wide range of farming activities, such as irrigation scheduling, farm management, disease identification and forecasting, and nutrition and fertilizer advisory²² 

Soil Fertility

Precision agriculture focuses on evaluating soil variabilities such as nature and different Properties,²³ and implementing various tools and techniques²⁴ listed below to achieve this. Physio-chemical properties of soil (pH, EC, etc.) Availability of macro- and micronutrients and other ions in the soil moisture and water holding capacity of the soil and organic matters and microbial populations

Nutrition and fertilizer advisory services, assessed for soil fertility and optimized it for crop production, are crucial components of sustainable agriculture.^25-27 The one of the goal of precision agriculture is to intensively offer suitable fertilizer types and doses, resulting in efficient sources of nutrients for achieving profitable yields.

The optimal growth and yield of food crops necessitate the presence of seventeen essential nutrients in the soil, which should be available to plants and these are characterized into two categories such as Macro and Micronutrients. Macro nutrients are further divided into two; the primary macronutrients (N), (P), and (K). secondary nutrients, including (S), (Ca), and (Mg), are to be applied in huge quantity as Kg Ha^-1, while micronutrients (Cu), (Mn), (Co), (B) (Zn),(Mb) and (Fe) should be provided as minor quantity ppm concentration. These nutrients must be present in the soil at levels that are sufficiently bioavailable for plant uptake, as well as plant tissue. The soil physio-chemical properties, such as pH, organic matter, cation exchange capacity (CEC), the presence of anions that interact with specific cations, and soil moisture content, influence the uptake of nutrients from the soil²⁸

Soil fertility testing is the prime objective of nutrition and fertilizer advisory which can be obtained by online or offline, Online remote sensing, wireless sensor networks and IoT’s and also spectroscopy and colorimetry are used in soil fertility tests, but the software and diagnostic tools used are not perfect. Offline mode will be accurate the process takes long time to do a soil fertility test, and we have to do field tests, soil sampling,²⁹ a wet chemistry laboratory approach with the equipments, sensor meters, colorimetry, and spectroscopy.

Chemical Fertilizer

Chemical fertilizers are inorganic forms that are applied to soil or directly to plants to provide nutrients for optimum plant development and growth.^30-31 Three categories are there

One is straight fertilizers which include only one primary plant nutrient, (N or P or K) such as and potassium chloride

Complex fertilizers in which two basic nutrients are chemically combined. such as diammonium phosphate (DAP) and ammonium phosphate. and finally mixed fertilizers two or three major plant nutrients.

Chemical fertilizers are often accessible in granular form with water-soluble properties; however, a few chemical fertilizers are also in liquid form.

Nitrogen Fertilizer

Nitrogen (N) is a vital nutrient for plants at all stages of their life cycle encompassing sowing, seedling development, sprouting, and vegetative growth. It is often asserted that all plants rely on it for survival. Nitrogen is crucial for chlorophyll synthesis, protein formation, and the generation of nucleic acids and amino acids, during the plant’s life cycle. Plants need mineral nitrogen and are absorbed from the soil in the form of nitrates (NO₃^–), nitrites (NO₂^–), and ammonium (NH₄+) ion.³² A very small amount of organic nitrogen can be present in humus, living organisms, or as intermediate products of organic matter decomposition. Microorganisms transform organic nitrogen into (NH₄+) and subsequently into nitrates (NO₃^–).is used directly in plants, and is not lost from the soil as easily as NO₃^– ions. Plants require nitrogen for uptake of nitrate from ammonium nitrate fertilizer (fast rate application) and uptake of ammonium (NH₄+), from urea fertilizer (slow rate application).

Phosphorus Fertilizer

Phosphorus³³ is involved in many plant processes, such as energy transfer reactions, development of reproductive structures, crop maturity, and root growth protein synthesis. Despite the low concentration of phosphate in soil solutions, plants take up a substantial amount of P due to desorption and dissolution, followed by diffusion to the plant root. Single superphosphate (SSP) is: the first commercial fertilizer for treating soil phosphorus deficiency followed by DAP fertilizer, comprising 18% N and 46% P of phosphorus. It also contains 2.5% sulpher.

Potassium Fertilizer

Potassium (K₂O) is another macronutrient that plants need in large amounts for growth, and its requirement can be as high as that of nitrogen.  Potassium nutrients required to plants for photo synthesis, ATP production, translocation of sugar, starch Production, nitrogen fixation in legumes, protein synthesis.³⁴ Plant uptake only soluble k (1 to 10mL ) and exchangeable K. Numerous factors influence potassium absorption by plants, including soil moisture, soil aeration or oxygen concentration, soil temperature, and tillage practices. The requisite quantity of potassium fertilizer is contingent upon the soil type and the crop cultivated. Muriate of potash (KCI) and sulphate of potash (K₂SO₄) are commonly used potassium fertilizers.

Soil pH

The pH of a prime soil property has a significant impact on the types and amounts of critical nutrients that are accessible to plant roots.³⁵ Soil pH reaction is the key to plant nutrient availability in the soil which regulates the plant’s nutrient fixation, and their release, and availability. In very acidic soil, Al and Mn may become more accessible and more poisonous to plants, whereas Ca, P and Mg are less accessible. In very alkaline soil, phosphorus and most micronutrients exhibit reduced availability.

Figure 1: Availability of soil nutrients versus soil pH36Click here to view Figure

Each plant should adhere to a specific pH value range. Due to the fact that pH has an effect on the availability of nutrients within the soil,³⁷ and plants have varying nutrient requirements, refer fig-1. When the pH value of the soil is more than 5.5, highly critical nutrient for plants, is readily available in the soil. Because of this, nitrogen has the potential to be transformed into a gas that has a pH value that is higher than 7.2, the availability of the phosphorus occurs when the pH value is between 6 and 7. Hence, planting of suitable crops in specific pH is necessary to avoid the deficiency as well as disease occurrence  It is advisable to maintain the pH of the soil between 4.5 and 8.8, which will make exchangeable K⁺ available for plant uptake. The optimal pH value range for soil is approximately between 6 and 7.2, according to experts.^38,39

Figure 2: How Soil pH affect the availability of Plant Nutrients38,39Click here to view Figure

This study work uses soil pH as a secondary variable to ascertain the requirements for N, P, and K fertilizers by employing regression and rule-based methodologies.^40-42

Materials and Methodology

The FertiCal-P App

This paper implements a comprehensive framework to calculate the soil’s need forPK fertilizer for optimal crop yields and demonstrates precise farming practices.^43-45  The FertiCal-P App (Fertilizer calculator in context to soil pH) is an Android-based chemical fertilizer recommendation calculator that estimates the need for NPK fertilizers based on soil test data like N, P₂O₅, K₂O, and pH.  Small farmers rarely use commercial software to estimate the need for fertilization for various reasons; the FertiCal-P App is available for free installation on playstation. Farmers can use it to determine the requirements for NPK fertilizer by comparing the price breakdown presented on the app’s last screen, making it the most adaptable solution.  As a result, it meets the objective by offering improved decision support to the end users.

Table 1: Fuzzy Rule for Clustering

Available Fertilizer in soil Kg Ha-1	Case-I	Case-II	Case-III	Case-IV	Case-V
Nitrogen	0 to 100	101 to 200	201 to 300	301 to 400	401 to 500
Phosphorus	0 to 15.00	15.01 to 30	30.01 to 45	45.01 to 60	60.01 to 75
Potassium	0  to 75	76 to 150	151 to 225	226 to 300	301 to 375
Attributes	Very low	Low	Moderate	High	Very High

Methodology

The FertiCal-P App has implemented procedure as indicated in table-1 to calculate the need for NPK fertilizer recommendations based on soil test values for nitrogen (N), phosphorus (P₂O₅), and potassium (K₂O), in context to soil pH. The following are the detailed procedure to work out the fertilizer dose using this app.

Step-1: Evaluate the Total need of nutrients of the soil.

Find the appropriate case constant by comparing soil nutrients test values and ascertain total need of nutrients from chemical fertilizers (T_NR) for N, P₂O₅ and K₂O.

T_NR = D_NR + S_NR

Where D_NR = Deficient of Macronutrients in the soil

T_NR = Total Macro-nutrients to be required for optimal Yield

S_NR  =  Macro-nutrients available in the soil (Test data)

Step-2: Find the deficient of nutrients of the soil

D_NR = A_{case constant} + ( B_{case coeffiecient}  – S_NR )

Where A_{case constant} = Case wise constant of the respective nutrients

B_{case coeffiecient} = Case wise co-efficient of the respective nutrients

Step-3: Estimate Macro Nutrients to be required in context to soil pH.

F_NR = D_{NR  x}  S_pH

Where F_NR = Final Macronutrients (N,P₂O₅ and K₂O) to be required

S_pH = pH of the soil

Soil pH affects linearly the requirements of soil nitrogen (N) and soil potassium (K₂O), but it also changes both sides from the peak of about pH of 6.32 for phosphorus (P₂O_5).

Step-4: Calculate Chemical fertilizer Recommendation (N, P₂O₅ and K₂O) for estimated  F_NR.

Urea (46:0:0) recommendation for N = F_NR x 2.17 Kg Ha^-1

SSP (016:0)* recommendation for P₂O₅ = F_NR x 6.25 Kg Ha^-1

MoP (0:0: 60 ) recommendation for K₂O = F_NR x 1.7 Kg Ha^-1

Table 2: Fertilizer recommendation

Reco-I	Urea	SSP	MoP
Reco-II	DAP	Urea	MoP
Rec-III	NPK(18:18:18)	Urea	MoP

DAP (18: 46:0) recommendation for P₂o_% = F_NR x 2.2 KgHa^-1 and for N = F_NR  x  5.55 Kg Ha^-1

and NPK (18:18:18) recommendation for N, P₂O₅ and K₂O = F_NR x 5.55 KgHa^-1

Description of FertiCal-P App

The interface of The FertiCal-P App is built in the form of tabs. Calculations are performed automatically after the user has entered the required soil test data. The Fertical-P App launched on the Android phone and displayed the main menu, as depicted in fig-3. Pressing the START button causes the app to display a screen similar to figure 4, which impart the necessary crop details, including crop selection, irrigation method, soil type, and soil test data status.

Figure 3: Main ManuClick here to view Figure

Figure 4: Essential DetailsClick here to view Figure

The Fertical-P App comes up with two modes for estimating the amount of fertilizer needed. If the soil test report is available, the next screen switches to the page where the user can enter the values of N, P₂O₅, K₂O, and pH from soil test data, as shown in Fig.5 otherwise the app shows the fertilizer recommendation in KgHa^-1 as per the standard agronomic ratios amended by the government for N, P₂O₅, K₂O chemical fertilizer (Fig. 8).

The Fertical-P App provides two options for estimating the amount of fertilizer needed. If the soil test report is available, the next screen switches to the page where the user can enter the values of N, P₂O₅, K₂O, and pH from soil test data (Fig. 5).

Figure 5: To enter soil Test DataClick here to view Figure

Figure 6: NPK Recommendations.Click here to view Figure

When pressing the Fertilizer Calculation button, the app will calculate three fertilization recommendations Fig.6. As illustrated in Fig.7, the pricing breakdown button, which allows the farmer to choose the most cost-effective fertilizer recommendation.

Process Breakdown

Figure 7: Price breakup screenClick here to view Figure

Figure 8: Standard agronomic recommendationClick here to view Table

Data Acquisition

Before acquiring the soil test data, the user can configure the app by providing the necessary crop details, including crop selection, irrigation method, soil type, and soil test data status. The system collects soil test data that includes essential parameters such as N, P₂O₅, (K₂O), and pH levels. These data are stored in a centralized application for further processing. Fig-9.

Figure 9: Operational Flow of SoftwareClick here to view Figure

Data Clustering

We analyze the collected soil data using case-wise clustering. This step involves grouping similar data points using clustering algorithms to identify patterns and trends in the soil’s nutrient composition.

NPK Requirement calculation

After clustering, the system performs an analysis to determine the optimal NPK fertilizer requirements. This step factors in the pH levels to fine-tune the NPK ratio for each cluster, ensuring balanced soil nutrient management.

Fertilizer Recommendation Generation

The application then computes and provides three fertilizer recommendations based on the analysis results. These recommendations are tailored to optimize crop yield and maintain soil health based on the specific characteristics of the soil cluster. This app compares the price breakdown on the last screen and provides recommendations based on the need for NPK fertilizer.

Technical features

Clustering Algorithm utilizes Fuzzy clustering an association with machine learning technique for case wise data clustering. Fuzzy clustering offers overlapping flexibility, robustness, and interpretability.

Data Processing The integration of soil parameters with pH level considerations ensures precision in the recommendation output.

Recommendation Engine The application deploys a recommendation engine to output three potential fertilizer solutions that align with the soil’s nutrient profile.

After the coding phase, the testing phase is performed by connecting the database to the developed modules with Open Database Connectivity (ODBC). Debugging is performed to correct the syntax and semantic errors in the developed program. Finally, a ‘setup’ program was prepared for easy loading and execution of the software.

Result

If Soil Test is YES

The FertiCal-P App schedules the recommendation of NPK fertilizers in two stages, the first at sowing and the second after 30 days of sowing, ensuring optimal fertilizer efficiency, reduced fertilizer usage, fewer expenses and ultimately adverse impact on environment. The app is developed using rule based and regression techniques and it calculate three different fertilizers recommendations with costing.

Recommendation-I tell us the requirements of urea, SSP and MoP according to soil test data.

Recommendation-II for DAP urea and MoP.

Recommendation-III for triple NPK (18:18:18), urea and MoP of chemical fertilizer kg per hectare.

At last, farmers select the price breakdown button to ascertain their fertilizer needs by analyzing the cost analysis displayed on the app’s final screen, rendering it the most versatile option.

If Soil Test is NO

If a soil test is not available, the app shows the fertilizer recommendation in Kg Ha-1 as per the standard agronomic ratios amended by the government for N:P:K fertilizer. Consequently, it fulfills its purpose by providing cost-effective decision support to the stake holder.

Discussion

The FertiCal-P App works well within the ranges and values specified in the table-1 for soil test data. If the soil test data beyond the mentioned band then app could not accept the inputs for N, P₂O₅, K₂O and pH. Before applying this strategy throughout the Indian subcontinent, we will test it first at the district level in Gujarat. Once the farmer complies with the app’s recommendations for chemical fertilizer, the end user will undoubtedly reap financial rewards.

Conclusions

The present study the Fertical-P app application accomplishes the primary objective of precision agriculture, to obtain greater fertilizer efficiency with minimum inputs for sustainable agriculture.  The Fertical-P App provides decision support for technology-based engagement in agricultural decision-making. Further test are needed at the district level in Gujarat before implementing it across the entire Indian sub-continent. With this app, the farmer, can make financially and agronomically beneficial decisions by referring to the statistics provided in the app’s final screen.

Future Scope

The Fertical-P App has the potential to expand its functionality by incorporating fuzzy rules for cotton and rice crops into the crop selection menu. Offering a selection of multiple soil characteristics, such as black and sandy, and providing the option to update irrigation types by adding rain fed, can further enhance the Fertical-P app’s functionality. Thus, the aforementioned app provides coverage for nearly three major crops in the state and enhances its operational popularity in the farmer community.

Acknowledgement

I am very thankful to Mr. Bhavya Shah-Sr. Application Developer, Oracle, India Pvt. Ltd., Gift City, Gandhinagar, India, who has helped me in developing an Android-based The FertiCal-P App fertilizer recommendation calculator.

Funding Sources

The author(s) also declares that there is no financial support for the research, authorship and/or publication of the article.

Conflict of Interest

The authors do not have any conflict of interest.

Data Availability Statement

The manuscript incorporates all datasets produced or examined throughout this research study. 

Ethics Statement

This research did not involve human participants, animal subjects, or any material that requires ethical approval.

Authors Contribution

Mukesh kumar Sharma: Author engaged in conceptualization, methodology, and the composition of the original draft.

Manoj Khediya: Precision Agriculture concept, Writing and & Editing the draft.

Chetan Bhatt: Offer comprehensive oversight and aid in the resolution of intricate instances as needed.

Reference

1. Bodo T., Gimah B., Seomoni K. Deforestation: Human Causes, Consequences and Possible Solutions, Journal of Geographical Research, April 2021; 4 (2); 22-30
   CrossRef
2. Anonymous article in The Guardian, 2022 “How hsas the populations grown since 1950?
3. Patel S., Sharma A., Singh G.S., Traditional agricultural practices in India: an approach for environmental sustainability and food security, Energy, Ecology and Environment, 2020; 5 (4):253–271
   CrossRef
4. Das R.,Dask B.P, “Principles of Plant Nutrients, Chapter-6, Current research in soil fertility, 2018; Volume-1,Book
5. Salvaraj A., Gutam J.,Verma S.,Verma G. Jain S. “Life Cycle sustainability assessment of croips in india”:, Current Research Environment sustainability, 2021; 2 ; https://doi.org/10.1016/j.crsust. 2021.100074
   CrossRef
6. Das S.,”Uncertainty and Chaos in Indian Farming: A Comprehensive Analysis of Agricultural Practices, Climate Variability, and Policy Interventions”, Agriculture & Food, 2023;5(8);384-389.
7. Eastwood C.R. Renwick A.”Innovation Uncertainty Impacts the Adoption of Smarter Farming Approaches, Frontiers in sustainable in food system, 2020;4(24);1-14.
8. Pannell D. J. Uncertainty and adoption of sustainable farming System, chapter-4, Risk Management and Environment: Agriculture in Perspective, Babcock Bruce 2003; DOI:10.1007/978-94-017-2915-4_5 PP 76-81.
   CrossRef
9. Pandey C.,Sethy P. K., Vishvakarma J. Behera S.K., Tande V.”Smart agriculture: Technological advancements on agriculture- A systematical review:, Chapter-4, 2022; DOI: 10.1016/B978-0-323-85214-2.00002-1.
   CrossRef
10. Robertson G., Swinton S. “Reconciling agricultural productivity and environmental integrity: a grand challenge for agriculture” The Ecological society of America, Front Ecol Environ 2005; 3(1); 38–46.
    CrossRef
    
11. Banu S, “Precision Agriculture: Tomorrow’s Technology for Today’s Farmer”, Food Process Technol, 2015; 6-8. DOI:  http://dx.doi.org/10.4172/2157-7110.1000468
    CrossRef
12. R.Khosla ”Precision agriculture: Challenges and Opportunities in a flat world”. 19^th world congress of soil science solutions for a Changing World, Brisbane, Australia, 2010.
13. Precision Agriculture Web article, WIKIPEDIA, 2023,
14. Nafchi A., Askarzadeh Y.,Azad B.. “What is Precision Agriculture and what are the 5R’s?”,South Dakota state University extension, 2022, Cross Reference.
15. Sharma S. Precision Agriculture: Reviewing the Advancements, Technologies, and Applications in Precision Agriculture for Improved Crop Productivity and Resource Management. Reviews In Food and Agriculture. 2023;4(2);41-45. doi:10.26480/rfna.02.2023.41.45.
    CrossRef
16. JanakiramanV.S, Sarukesi K. “Decision Support System”, 11^th Edition 2009; PHI Learning Pvt. Ltd, New Delhi-Book
17. Tillman D.“Global environmental impacts of agricultural expansion: The need for sustainable and efficient practices” Proceedings of the National Academy of Sciences of the United States of America, 1999; 96 (11); 5995-6000.
    CrossRef
    
18. Ning Wang, Naiqian Zhang , Maohua Wang , Ning Wang ”Precision Agriculture” Computers and Electronics in Agriculture,2002; 36(2), 13-132.
    CrossRef
    
19. Pal S., Sathi I.C., Arora A. “Decision support System for Nutrient Management in Crops “Indian Society of Agricultural Statistics,(ISAS),2009; 63(1); 91-96.
20. Er M.C.  “Decision Support Systems: A Summary, Problems, and Future Trend” Elsevier Science Publishers,1998; 355-363.
21. Ravisankar H.R., Chandrasekhararao C., Sivaraju K. “Decision support system forsoil and water analysis and fertiliser recommendation for flue-cured Virginia (FCV) tobacco” African Journal of Agricultural Research,2013; 8(44); 5550-5554.
22. Kekane S.S., Chavan R.P, Shinde D.N, Patil C.L, Sagar S.S., A review on physico-chemical properties of soil, International Journal of Chemical Studies 2015; 3(4)29-32.
23. Weil R., Nyle C. Brady, “The Nature and Properties of Soils” 15^th edition, 2017; Book.
24. Kuzhivilayil S., Shekhar R.S., George J.“Soil Test & Plant tissue Analysis as Diagnostic Tools for Fertilizer Recommendations for Cassava in an Ultisol, Communications in Soilk Science and Plant Analysis, 2016; 46 (13) ;1607-1627,
    CrossRef
    
25. Vishwajit K.P,.Bhat A.S, Ashalatha K.V., Sahu  P.K. “Decision support system for fertilizer recommendation–A case study” .Indian Journal of Agronomy, 2014; 59 (2): 344-349.
26. Chakraborty S.”Soil Science and Technology”, NPTEL Lecture series, IIT, Kharagpur
27. .Robert P.C. “Precision agriculture: a challenge for crop nutrition management”, Plant & Soil 247,2002;247; 143–149.
    CrossRef
    
28. Nafiu A.K., Abiodun M, Okpara I.M., Chude V.O., Soil fertility evaluation: a potential tool for predicting fertilizer requirement for crops in Nigeria, African Journal of Agricultural Research,  2012; 7(47); 6204-6214.
    CrossRef
    
29. Karande S., Ganit S., Prajapati D. “Analysis of soil samples for its physical and chemical parameters from Mehsana and Patan District” International research journal of Science & Engineering, 2020; A9.86-94.
30. Rajendra Prasad, “Sustainable Agriculture and Fertilizer use”. Article
31. Hera C. “The role of inorganic fertilizers and their management Practices” Fertilizer Research , 1996; 43;:63-81.
    CrossRef
32. Wlodarczyk T. “Nitrogen Transformations And Their Circumstances In Soils” Acta  Agrophysica, 2002, 63,123-158.
33. Bechtaoui  N., Rabiu M, Raklami A., Oufdou K., Jemo M. “Phosphate-Dependent Regulation of Growth and Stresses Management in Plants, Frontiers in Plant Science, published, 2021; 12,doi: 10.3389/fpls.2021.679916
    CrossRef
    
34. Reddy, K.C.K., Maruthi Sankar G.R., and Velayutham M.. “Potassium availability in soils of Different Pedogenic Characteristics. In Soil Testing”, Plant Analysis & Fertilizer Evaluation for Potassium, 1987; 4; 167-181
35. Dora N, Review Article, “The Role of Soil pH in Plant Nutrition and Soil remediation, Applied and Environmental Soil Science, 2019;2019, doi.org/10.1155/2019/5794869
    CrossRef
36. Rawichandran D. Kasim S. , Zuan A., Effendi M., Sharing R.  “ Utilization of Yeasts in Promoting Plant Growth in Acidic Soil–A Review” AGRIVITA Journal of Agricultural Science. 2024. 46(1): 196-210.
    CrossRef
    
37. Tandzi N., Mutengwa L., Ngonken C., & Green V ”Breeding maize for torerance to acidicsoils:A review” Agromony,8(6),84, http:/doi/org/10_339/agromony80060084,2018-Cross Reference
38. Ferrarezi R., Lin Xiongjie, Gonzalez A., Neir C, Zambon F., Tabay, H., Wang Xianda, Huang J., Fan G. “Substrate pH Influences the Nutrient Absorption and Rhizosphere Microbiome of Huanglongbing-Affected Grapefruit Plants” Frantier in Plant Science, 2022; 13, doi: 10.3389/fpls.2022.856937-Cross reference
    CrossRef
    
39. Roques S., Kendal S., Smit K.A., Berry P.M. Review of the non-NPKS nutrient requirements of UK cereals and oilseed rape, Agriculture & Horticulture Development Board, 2013.Review Report
40. Aravinth S., Senthikumar M,, Senthikumar J “Regression analysis based decision support system with relationship extraction”, Model Assisted Statistics and Application,  2022;17(1); 3-7.
    CrossRef
    
41. Sellam V. and Poovammal E. Prediction of Crop Yield using Regression Analysis Indian Journal of Science and Technology, 2016; 9(38), 10.17485/ijst/2016/v9i38/91714,
42. Ujjainia S., Gautam P, Veenadhari S., Crop yield prediction using regression Model, International Journal of Innovative Technology and Exploring Engineering (IJITEE) , 2020; 9 (10). 269-273.
    CrossRef
    
43. Chemical Fertilizer, price and chemical composition, www.aau.com
44. Types of chemical fertilizers used in indian agriculture, www.nicheagriculture.com
45. Nutrient management and fertilizers, www.agritech.tnau.in 

Abbreviations

Al : Aluminium

ATP: Adenosine Tri-Phosphate

DAP:  Di-Ammonium Phosphate

DSS: Decision Support System

EC: Electrical Conductivity

FertiCal-P: Fertilizer Calculator in context to Soil pH.

IoT: Internet of Thing

K₂O: Potassium Oxide

KCL:  Potassium Chloride

Kg Ha^-1: Kilogram per Hectare

K₂SO₄ : Potassium Sulfate

ml: Mili litre

MOP: Muriatic of Potash

N: Nitrogen ions

NH₄⁺: Ammonium Ions

NPK: Nitrogen-Phosphorus and Potassium nutrients

NO₃^–: Nitrate ions 

NO₂^-: Nitrites ions-

P₂O_5-: Phosphorus Pentoxide

PA: Precision Agriculture

pH: Logarithmic scale which measure acidic or alkaline properties of matter

S-Sulpher

SSP: Super Single Phosphate

Views: 53