A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms

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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Agriculture (Basel) Jg. 14; H. 8; S. 1256
Hauptverfasser:	Senapaty, Murali Krishna, Ray, Abhishek, Padhy, Neelamadhab
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.08.2024
Schlagworte:	accuracy rate Agricultural equipment agricultural industry Agricultural production Agriculture Algorithms Analysis climate Climate prediction computer software Crop production crop recommendation Crops Data analysis data collection Data mining Datasets Decision making Decision support systems Decision trees Farmers Fertilizers global positioning system Global positioning systems GPS Historic districts India Internet Learning algorithms Machine learning Positioning devices (machinery) precision agriculture Profits regression analysis Sensors Software development tools soil composition Soil water Water availability Websites India
ISSN:	2077-0472, 2077-0472
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	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.
AbstractList	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.
Audience	Academic
Author	Senapaty, Murali Krishna Ray, Abhishek Padhy, Neelamadhab
Author_xml	– sequence: 1 givenname: Murali Krishna orcidid: 0000-0003-4529-6549 surname: Senapaty fullname: Senapaty, Murali Krishna – sequence: 2 givenname: Abhishek surname: Ray fullname: Ray, Abhishek – sequence: 3 givenname: Neelamadhab orcidid: 0000-0001-7997-2336 surname: Padhy fullname: Padhy, Neelamadhab
BookMark	eNp9Uctq3DAUNSWFpmm-oBtDN91MKlmyHsth-khgSqFp1kKWrh0NtuRK8iJ_HzkOpYRSaSFxOI97OW-rMx88VNV7jK4IkeiTHqIzy5iXCJgigZuWvarOG8T5DlHenP31f1NdpnRC5UhMBGLnVbevP4NxyQVf3y7zHGKubx9ShqnuQ6wPMcz1TzBhmsBbnVfaXXJ-qL9rc-881EfQ0a_AYdQpud6ZjbUfhxBdvp_Su-p1r8cEl8_vRXX39cuvw_Xu-OPbzWF_3BnKUN5paQVw1nSdJR1hzAouraFCatkCogYjilvbmELQjPO208SyTiDTWESBI3JR3Wy-NuiTmqObdHxQQTv1BIQ4KB2zMyMoIkVvYE0ozlgI2csS0CHOTEcbg4vXx81rjuH3AimrySUD46g9hCUpglvCSUMEKdQPL6insERfNlUESS5Qw_E63NXGGnTJd74POWpTroXJmVJo7wq-F4hTRBlZJyCbwMSQUoT-z0YYqbV39Y_ei0q-UBmXnwopcW78r_YRXAy4mA
CitedBy_id	crossref_primary_10_3390_su17125230 crossref_primary_10_1007_s43069_025_00434_z crossref_primary_10_1016_j_compag_2025_109905 crossref_primary_10_1051_bioconf_202515104028 crossref_primary_10_1016_j_fcr_2025_109989 crossref_primary_10_3390_agriengineering7060170 crossref_primary_10_1016_j_procs_2025_02_072 crossref_primary_10_3390_app15010400 crossref_primary_10_3390_bdcc8110143 crossref_primary_10_54392_irjmt2525
Cites_doi	10.5194/isprs-archives-XLII-3-W6-477-2019 10.1371/journal.pone.0252402 10.3390/agronomy12010058 10.3390/agronomy9020087 10.4038/jmm.v10i1.45 10.1016/j.gltp.2021.08.060 10.1109/SoSE50414.2020.9130481 10.1109/WISNET.2019.8711808 10.1016/j.dajour.2022.100041 10.1016/j.kjs.2023.11.009 10.1109/ICTS52701.2021.9608436 10.1109/ICETCE48199.2020.9091741 10.3390/s18082674 10.1016/j.procs.2023.01.241 10.1016/j.jclepro.2022.133638 10.1109/ICCUBEA.2018.8697349 10.36227/techrxiv.23504496.v1 10.3389/frai.2023.1203546 10.1109/IEMENTech48150.2019.8981128 10.3390/s22166299 10.1016/j.heliyon.2023.e15245 10.1109/IDAACS.2011.6072702 10.1016/j.measen.2023.101002 10.1109/CVPRW63382.2024.00543 10.1109/TIAR.2015.7358549 10.1109/ICOEI.2018.8553720 10.30574/wjarr.2022.14.3.0581 10.1109/GeoInformatics.2011.5980847 10.1109/ICACCS51430.2021.9441736 10.1109/ICCMC51019.2021.9418351 10.3390/agronomy13041169 10.32628/CSEIT2173129 10.1109/RTEICT42901.2018.9012549 10.1007/s00521-023-09391-2 10.1080/2150704X.2014.889863 10.3390/rs14235978 10.1109/Agro-Geoinformatics.2018.8476124 10.1109/ACCESS.2023.3249205 10.1109/GHTC-SAS.2013.6629944 10.24018/ejai.2022.1.3.14 10.1016/j.compag.2023.107663 10.1109/ICICV50876.2021.9388479 10.3390/agriculture12070977
ContentType	Journal Article
Copyright	COPYRIGHT 2024 MDPI AG 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: COPYRIGHT 2024 MDPI AG – notice: 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	AAYXX CITATION 3V. 7SS 7ST 7T7 7X2 8FD 8FE 8FH 8FK ABUWG AEUYN AFKRA ATCPS AZQEC BENPR BHPHI C1K CCPQU DWQXO FR3 HCIFZ M0K P64 PHGZM PHGZT PIMPY PKEHL PQEST PQQKQ PQUKI PRINS SOI 7S9 L.6 DOA
DOI	10.3390/agriculture14081256
DatabaseName	CrossRef ProQuest Central (Corporate) Entomology Abstracts (Full archive) Environment Abstracts Industrial and Applied Microbiology Abstracts (Microbiology A) Agricultural Science Collection Technology Research Database ProQuest SciTech Collection ProQuest Natural Science Collection ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Agricultural & Environmental Science Collection ProQuest Central Essentials ProQuest Central - New (Subscription) Natural Science Collection Environmental Sciences and Pollution Management ProQuest One Community College ProQuest Central Engineering Research Database SciTech Premium Collection Agricultural Science Database Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Environment Abstracts AGRICOLA AGRICOLA - Academic DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef Agricultural Science Database Publicly Available Content Database Technology Research Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Natural Science Collection ProQuest Central China Environmental Sciences and Pollution Management ProQuest Central ProQuest One Sustainability Natural Science Collection ProQuest Central Korea Agricultural & Environmental Science Collection Industrial and Applied Microbiology Abstracts (Microbiology A) ProQuest Central (New) ProQuest One Academic Eastern Edition Agricultural Science Collection ProQuest SciTech Collection Biotechnology and BioEngineering Abstracts Entomology Abstracts ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic Environment Abstracts ProQuest One Academic (New) ProQuest Central (Alumni) AGRICOLA AGRICOLA - Academic
DatabaseTitleList	Agricultural Science Database AGRICOLA CrossRef
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Agriculture
EISSN	2077-0472
ExternalDocumentID	oai_doaj_org_article_398fced879e041889f94c1b076cb42c1 A807404631 10_3390_agriculture14081256
GeographicLocations	India
GeographicLocations_xml	– name: India
GroupedDBID	2XV 5VS 7X2 8FE 8FH AAFWJ AAHBH AAYXX ADBBV AEUYN AFFHD AFKRA AFPKN ALMA_UNASSIGNED_HOLDINGS ATCPS BCNDV BENPR BHPHI CCPQU CITATION GROUPED_DOAJ HCIFZ IAG IAO ITC KQ8 M0K MODMG M~E OK1 OZF PHGZM PHGZT PIMPY PROAC 3V. 7SS 7ST 7T7 8FD 8FK ABUWG AZQEC C1K DWQXO FR3 P64 PKEHL PQEST PQQKQ PQUKI PRINS SOI 7S9 L.6
ID	FETCH-LOGICAL-c460t-a9d8e762bbd3b366d879dc489a95e04c10415d2c2bba6775ba3d6b80c2d04e703
IEDL.DBID	BENPR
ISICitedReferencesCount	10
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001305845000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2077-0472
IngestDate	Fri Oct 03 12:50:42 EDT 2025 Sun Nov 09 11:27:12 EST 2025 Mon Jun 30 13:21:32 EDT 2025 Tue Nov 04 18:27:54 EST 2025 Sat Nov 29 07:08:48 EST 2025 Tue Nov 18 21:58:16 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	8
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c460t-a9d8e762bbd3b366d879dc489a95e04c10415d2c2bba6775ba3d6b80c2d04e703
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0003-4529-6549 0000-0001-7997-2336
OpenAccessLink	https://www.proquest.com/docview/3097802710?pq-origsite=%requestingapplication%
PQID	3097802710
PQPubID	2032441
ParticipantIDs	doaj_primary_oai_doaj_org_article_398fced879e041889f94c1b076cb42c1 proquest_miscellaneous_3153732383 proquest_journals_3097802710 gale_infotracacademiconefile_A807404631 crossref_primary_10_3390_agriculture14081256 crossref_citationtrail_10_3390_agriculture14081256
PublicationCentury	2000
PublicationDate	2024-08-01
PublicationDateYYYYMMDD	2024-08-01
PublicationDate_xml	– month: 08 year: 2024 text: 2024-08-01 day: 01
PublicationDecade	2020
PublicationPlace	Basel
PublicationPlace_xml	– name: Basel
PublicationTitle	Agriculture (Basel)
PublicationYear	2024
Publisher	MDPI AG
Publisher_xml	– name: MDPI AG
References	Shams (ref_63) 2024; 36 Pandey (ref_28) 2019; 42 Rajak (ref_22) 2017; 4 Olofintuyi (ref_59) 2023; 9 Bhatnagar (ref_37) 2022; 71 Kawakura (ref_49) 2022; 1 ref_14 Sharma (ref_31) 2023; 33 ref_13 ref_12 ref_11 ref_10 ref_52 Paudel (ref_55) 2023; 206 Kedlaya (ref_36) 2021; 2 ref_19 Apat (ref_53) 2023; 82 Rajakumaran (ref_57) 2024; 31 ref_17 Dubey (ref_34) 2023; 8 Ranaweera (ref_15) 2023; 10 Reyana (ref_38) 2023; 11 ref_61 ref_23 Ashoka (ref_46) 2022; 36 ref_66 ref_21 Sabrina (ref_54) 2022; 72 ref_65 Bondre (ref_16) 2019; 4 ref_20 ref_64 Raju (ref_58) 2024; 51 ref_62 Coulibaly (ref_51) 2022; 371 ref_27 Panigrahi (ref_24) 2023; 218 Kawakura (ref_47) 2022; 4 Reddy (ref_30) 2023; 14 Dhanavel (ref_29) 2023; 44 Gosai (ref_32) 2021; 7 Batchuluun (ref_56) 2022; 34 Bandaiaha (ref_60) 2022; 41 Garg (ref_25) 2023; 10 Eddaoudi (ref_39) 2023; 14 Bandara (ref_33) 2020; 975 Sonobe (ref_18) 2014; 5 ref_45 Ryo (ref_50) 2022; 6 ref_44 Islam (ref_40) 2023; 14 ref_42 ref_1 ref_3 Sundari (ref_35) 2022; 14 ref_2 Durai (ref_43) 2022; 3 ref_48 ref_9 ref_8 ref_5 ref_4 Shankar (ref_26) 2022; 9 ref_7 Bhuyan (ref_41) 2023; 15 ref_6
References_xml	– volume: 10 start-page: 498 year: 2023 ident: ref_25 article-title: An effective crop recommendation method using machine learning techniques publication-title: Int. J. Adv. Technol. Eng. Explor. – volume: 42 start-page: 477 year: 2019 ident: ref_28 article-title: Improved In-Season Crop Classification Performance Using Ensemble Learning Technique: A Case Study of Lekoda Insurance Unit, Ujjain, Madhya Pradesh publication-title: Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. doi: 10.5194/isprs-archives-XLII-3-W6-477-2019 – ident: ref_64 doi: 10.1371/journal.pone.0252402 – volume: 14 start-page: 1062 year: 2023 ident: ref_30 article-title: Optimizing Crop Forecasts: Leveraging Feature Selection and Ensemble Methods publication-title: Turk. J. Comput. Math. Educ. (TURCOMAT) – volume: 82 start-page: 558 year: 2023 ident: ref_53 article-title: An Artificial Intelligence-based Crop Recommendation System using Machine Learning publication-title: J. Sci. Ind. Res. (JSIR) – ident: ref_20 doi: 10.3390/agronomy12010058 – ident: ref_61 doi: 10.3390/agronomy9020087 – volume: 10 start-page: 19 year: 2023 ident: ref_15 article-title: Crop Price Prediction Using Machine Learning Approaches: Reference to the Sri Lankan Vegetable Market publication-title: J. Manag. Matters doi: 10.4038/jmm.v10i1.45 – volume: 2 start-page: 294 year: 2021 ident: ref_36 article-title: An efficient algorithm for predicting crop using historical data and pattern matching technique publication-title: Glob. Transit. Proc. doi: 10.1016/j.gltp.2021.08.060 – ident: ref_7 doi: 10.1109/SoSE50414.2020.9130481 – ident: ref_8 doi: 10.1109/WISNET.2019.8711808 – volume: 15 start-page: 417 year: 2023 ident: ref_41 article-title: Machine Learning-based Crop Recommendation System in Biswanath District of Assam publication-title: Biol. Forum Int. J. – volume: 3 start-page: 100041 year: 2022 ident: ref_43 article-title: Smart farming using machine learning and deep learning techniques publication-title: Decis. Anal. J. doi: 10.1016/j.dajour.2022.100041 – volume: 51 start-page: 100160 year: 2024 ident: ref_58 article-title: CropCast: Harvesting the future with interfused machine learning and advanced stacking ensemble for precise crop prediction publication-title: Kuwait J. Sci. doi: 10.1016/j.kjs.2023.11.009 – ident: ref_62 doi: 10.1109/ICTS52701.2021.9608436 – ident: ref_45 doi: 10.1109/ICETCE48199.2020.9091741 – volume: 4 start-page: 11 year: 2022 ident: ref_47 article-title: Analyses of diverse agricultural worker data with explainable artificial intelligence: Xai based on shap, lime, and lightgbm publication-title: Eur. J. Agric. Food Sci. – ident: ref_1 doi: 10.3390/s18082674 – volume: 218 start-page: 2684 year: 2023 ident: ref_24 article-title: A machine learning-based comparative approach to predict the crop yield using supervised learning with regression models publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2023.01.241 – volume: 371 start-page: 133638 year: 2022 ident: ref_51 article-title: Explainable deep convolutional neural networks for insect pest recognition publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2022.133638 – volume: 8 start-page: 2059 year: 2023 ident: ref_34 article-title: Crop Recommendation System for Madhya Pradesh Districts using Machine Learning publication-title: Int. J. Innov. Sci. Res. Technol. – volume: 9 start-page: 127 year: 2022 ident: ref_26 article-title: Crops Prediction Based on Environmental Factors Using Machine Learning Algorithm publication-title: Cent. Dev. Econ. Stud. – ident: ref_2 doi: 10.1109/ICCUBEA.2018.8697349 – ident: ref_66 – volume: 6 start-page: 257 year: 2022 ident: ref_50 article-title: Explainable artificial intelligence and interpretable machine learning for agricultural data analysis publication-title: Artif. Intell. Agric. – ident: ref_42 doi: 10.36227/techrxiv.23504496.v1 – ident: ref_48 doi: 10.3389/frai.2023.1203546 – ident: ref_12 doi: 10.1109/IEMENTech48150.2019.8981128 – ident: ref_17 doi: 10.3390/s22166299 – volume: 36 start-page: 169 year: 2022 ident: ref_46 article-title: IMLAPC: Interfused Machine Learning Approach for Prediction of Crops publication-title: Rev. D’intell. Artif. – volume: 9 start-page: E15245 year: 2023 ident: ref_59 article-title: An ensemble deep learning approach for predicting cocoa yield publication-title: Heliyon doi: 10.1016/j.heliyon.2023.e15245 – ident: ref_11 doi: 10.1109/IDAACS.2011.6072702 – volume: 31 start-page: 101002 year: 2024 ident: ref_57 article-title: Crop yield prediction using multi-attribute weighted tree-based Support Vector Classifier publication-title: Meas. Sens. doi: 10.1016/j.measen.2023.101002 – ident: ref_65 doi: 10.1109/CVPRW63382.2024.00543 – volume: 71 start-page: 626 year: 2022 ident: ref_37 article-title: Agriculture Crop Recommendation System using Machine-Learning publication-title: Math. Stat. Eng. Appl. – ident: ref_5 doi: 10.1109/TIAR.2015.7358549 – volume: 41 start-page: 476 year: 2022 ident: ref_60 article-title: Classification of Fertiliser Type Based on Soil Minerals Using Voting Classification Over Decision Tree publication-title: Adv. Parallel Comput. Algorithms Tools Paradig. – ident: ref_10 doi: 10.1109/ICOEI.2018.8553720 – volume: 44 start-page: 1126 year: 2023 ident: ref_29 article-title: A Study on Variable Selections and Prediction for Crop Recommender System with Soil Nutrients Using Stochastic Model and Machine Learning Approaches publication-title: Tuijin Jishu/J. Propuls. Technol. – volume: 14 start-page: 452 year: 2022 ident: ref_35 article-title: Crop recommendation and yield prediction using machine learning algorithms publication-title: World J. Adv. Res. Rev. doi: 10.30574/wjarr.2022.14.3.0581 – volume: 33 start-page: 700 year: 2023 ident: ref_31 article-title: Yield Prediction and Recommendation of Crops in the Northeastern Region Using Machine Learning Regression Models publication-title: Yuz. Yıl Univ. J. Agric. Sci. – ident: ref_14 doi: 10.1109/GeoInformatics.2011.5980847 – ident: ref_3 doi: 10.1109/ICACCS51430.2021.9441736 – ident: ref_44 doi: 10.1109/ICCMC51019.2021.9418351 – ident: ref_21 doi: 10.3390/agronomy13041169 – volume: 7 start-page: 558 year: 2021 ident: ref_32 article-title: Crop recommendation system using machine learning publication-title: Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. doi: 10.32628/CSEIT2173129 – ident: ref_13 doi: 10.1109/RTEICT42901.2018.9012549 – volume: 975 start-page: 8887 year: 2020 ident: ref_33 article-title: Crop recommendation system publication-title: Int. J. Comput. Appl. – volume: 36 start-page: 5695 year: 2024 ident: ref_63 article-title: Enhancing crop recommendation systems with explainable artificial intelligence: A study on agricultural decision-making publication-title: Neural Comput. Appl. doi: 10.1007/s00521-023-09391-2 – ident: ref_6 – volume: 34 start-page: 10474 year: 2022 ident: ref_56 article-title: Deep learning-based plant classification and crop disease classification by thermal camera publication-title: J. King Saud Univ.-Comput. Inf. Sci. – volume: 5 start-page: 157 year: 2014 ident: ref_18 article-title: Random forest classification of crop type using multi-temporal TerraSAR-X dual-polarimetric data publication-title: Remote Sens. Lett. doi: 10.1080/2150704X.2014.889863 – ident: ref_52 doi: 10.3390/rs14235978 – volume: 4 start-page: 371 year: 2019 ident: ref_16 article-title: Prediction of crop yield and fertilizer recommendation using machine learning algorithms publication-title: Int. J. Eng. Appl. Sci. Technol. – ident: ref_9 doi: 10.1109/Agro-Geoinformatics.2018.8476124 – volume: 11 start-page: 20795 year: 2023 ident: ref_38 article-title: Accelerating Crop Yield: Multisensor Data Fusion and Machine Learning for Agriculture Text Classification publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3249205 – ident: ref_4 doi: 10.1109/GHTC-SAS.2013.6629944 – ident: ref_19 – volume: 14 start-page: 100880 year: 2023 ident: ref_40 article-title: Machine learning enabled IoT system for soil nutrients monitoring and crop recommendation publication-title: J. Agric. Food Res. – volume: 1 start-page: 27 year: 2022 ident: ref_49 article-title: Adaptations of Explainable Artificial Intelligence (XAI) to Agricultural Data Models with ELI5, PDPbox, and Skater using Diverse Agricultural Worker Data publication-title: Eur. J. Artif. Intell. Mach. Learn. doi: 10.24018/ejai.2022.1.3.14 – volume: 206 start-page: 107663 year: 2023 ident: ref_55 article-title: Interpretability of deep learning models for crop yield forecasting publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2023.107663 – volume: 4 start-page: 950 year: 2017 ident: ref_22 article-title: Crop recommendation system to maximize crop yield using machine learning technique publication-title: Int. Res. J. Eng. Technol. – ident: ref_23 doi: 10.1109/ICICV50876.2021.9388479 – ident: ref_27 doi: 10.3390/agriculture12070977 – volume: 14 start-page: 199 year: 2023 ident: ref_39 article-title: A Predictive Approach to Improving Agricultural Productivity in Morocco through Crop Recommendations publication-title: Int. J. Adv. Comput. Sci. Appl. – volume: 72 start-page: 3777 year: 2022 ident: ref_54 article-title: An interpretable artificial intelligence based smart agriculture system publication-title: Comput. Mater. Contin.
SSID	ssj0000913806
Score	2.4567482
Snippet	Today, crop suggestions and necessary guidance have become a regular need for a farmer. Farmers generally depend on their local agriculture officers regarding...
SourceID	doaj proquest gale crossref
SourceType	Open Website Aggregation Database Enrichment Source Index Database
StartPage	1256
SubjectTerms	accuracy rate Agricultural equipment agricultural industry Agricultural production Agriculture Algorithms Analysis climate Climate prediction computer software Crop production crop recommendation Crops Data analysis data collection Data mining Datasets Decision making Decision support systems Decision trees Farmers Fertilizers global positioning system Global positioning systems GPS Historic districts India Internet Learning algorithms Machine learning Positioning devices (machinery) precision agriculture Profits regression analysis Sensors Software development tools soil composition Soil water Water availability Websites
SummonAdditionalLinks	– databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV07T8MwELYQYoAB8RTlJSMhsRDhxKljj-ElBkAMILFZfrUg0Ralhd_PnWNKBx4La3KJnHv5PuX8HSGHQkENUEIgMesBoOSMZzI3IROl7YL_BF_ZSJl_Xd3eysdHdTcz6gt7wlp64FZxJ1zJngteViqwMpdS9VTpcgvw29mycBH4sErNgKmYg1XOJRMtzRAHXH9i-k0iswiAKWBbw5HVM1tRZOz_KS_HzeZyhSynKpHW7epWyVwYrpGl-uvl68TW9DzNx6E4mhPKaNrSj1OoQ-lZM3qliC0Hg5DmJtHYHkBvYvtkoIlZtU_jXEzsGGql6pf-qHmePA3GG-Th8uL-7CpLAxMyVwo2yYzyMkB2s9Zzy4VArXlXSmVUF7TncjyP7wsHAkZUVdca7oWVzBWelQFif5PMD0fDsEVoj4NIYQzmA6jpjHHMShOsAKOLXmU6pPjUnXaJTRyHWrxoQBWocP2NwjvkePrQa0um8bv4KRplKopM2PEC-IdO_qH_8o8OOUKTaoxXWKAz6dgBfCYyX-ka2YCQNg0kdz-trlMgjzXHcy4A3XPWIQfT2xCC-F_FDMPoDWRg16g41D58-z9WvEMWC6ic2i7DXTI_ad7CHllw75PncbMf_fwDJiUEwg priority: 102 providerName: Directory of Open Access Journals
Title	A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
URI	https://www.proquest.com/docview/3097802710 https://www.proquest.com/docview/3153732383 https://doaj.org/article/398fced879e041889f94c1b076cb42c1
Volume	14
WOSCitedRecordID	wos001305845000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2077-0472 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913806 issn: 2077-0472 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2077-0472 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913806 issn: 2077-0472 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Agricultural Science Database customDbUrl: eissn: 2077-0472 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913806 issn: 2077-0472 databaseCode: M0K dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/agriculturejournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2077-0472 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913806 issn: 2077-0472 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2077-0472 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913806 issn: 2077-0472 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELZKywEOtOUhlj5kpEpciOrEWcc-VelLRbCrFQKpnCy_dkHqbpbslt_PjOPdcqC99JJDMonseGY8Y4-_j5AjoSAGKMGQmPWQoOSMZzI3IROl7YP-BF_ZCJn_pRoO5fW1GqUFt0Uqq1z5xOiofeNwjfyY44EDyKFydjL_nSFrFO6uJgqNJ2QLkcpAz7dOL4ajr-tVFkS9lEx0cEMc8vtjM2kTqEWA3AKmN6Su_mdKisj99_nnOOlcbj-2uTvkRQo3ad3pxy7ZCLOX5Hl917pXxNb0PBHtUOT4hHicdjjmFAJaetY2c4pJ6nQaEgETjXUGdBDrMANNEK0TGgk2sfSok6pvJtCi5c_p4jX5fnnx7ewqS8wLmSsFW2ZGeRnATVrrueVCeFkp70qpjOoHVrocD_b7woGAEVXVt4Z7YSVzhWdlACfyhmzOmll4S-iYg0hhDDoWCA6NccxKE6wA7RHjyvRIsfr52iVYcmTHuNGQnuCI6f-MWI98XL8071A5HhY_xVFdiyKkdrzRtBOdLFRzJccuYE-hh7mUaqygo5ZVwtmycHmPfECd0Gj40EBn0vkF6CZCaOkaYYUQfw0k91c6oZNHWOg7heiR9-vHYMu4QWNmobkFGZh-Kg5BFH_38Cf2yLMCgquuEHGfbC7b23BAnro_y1-L9jAZwWFcX4DrgH2Ge6NPg9GPvwWrF8I
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VFAk48EYECiwSiAtW115nvT4gZFqqRk2iHIpUTu6-EpCaOCQpiD_Fb2TGXqccoLceuCYTK7P-dh727PcBvJI51gApbiRuHDYoMReRirWPZGp6iB_vMlNT5g-y0UidnOTjLfjVnoWhsco2JtaB2lWWnpHvCjpwgD1UzN8vvkWkGkVvV1sJjQYWR_7nD2zZVu_6-3h_XyfJwcfjvcMoqApENpV8HencKY8hwBgnjJDSqSx3NlW5znuepzamQ-susWigZZb1jBZOGsVt4njqcYPgda_Bdkpg78D2uD8cf9481SGWTcVlQ28kRM539XQZSDQ89jKYTkkq-48UWCsF_Csf1Enu4M7_tjx34XYop1nR4P8ebPn5fbhVXKzGAzAF2w9CQow0TLHfYA1PO8OCne0tqwWjJnw280FgitVzFGxYz5l6Fihop6wWEKXRqsaqOJviCqy_zFYP4dOVOPkIOvNq7h8Dmwg0SbSmwInFr9aWG6W9kbg75CTTXUjam13aQLtO6h9nJbZfhJDyLwjpwtvNjxYN68jl5h8IRRtTogyvP6iW0zJEoFLkamI9eYoexkrlkxwdNTyT1qSJjbvwhjBYUmDDP2h1OJ-BbhJFWFkQbRLxy6HlTovBMkS8VXkBwC683HyNsYpeQOm5r87RBtNrJrBIFE8uv8QLuHF4PByUg_7o6CncTLCQbIYud6CzXp77Z3Ddfl9_XS2fhw3I4PSqQf0bqm5xfA
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nb9NAEB2VFCE48I0IFFgkEBesrL3Oen1AyDRERG2jHEAqJ3d3vQ5ITRycFMRf49cxY69TDtBbD1yTiZVdv_myZ98DeCFTrAFidCRuCmxQQi4CFWoXyNgMET-uSExDmX-YTKfq-Did7cCv7iwMjVV2MbEJ1EVl6Rn5QNCBA-yhQj4o_VjEbDR-u_oWkIIUvWnt5DRaiBy4nz-wfVu_mYzwXr-MovH7j_sfAq8wENhY8k2g00I5DAfGFMIIKQuVpIWNVarToeOxDekAexFZNNAySYZGi0IaxW1U8Nihs-B1r8AuluRx1IPd2eRo9nn7hIcYNxWXLdWRECkf6HntCTUc9jWYWkk2-4902KgG_Cs3NAlvfOt_3qrbcNOX2Sxr_eIO7LjlXbiRne_MPTAZG3mBIUbaptiHsJa_nWEhz_brasWoOV8snBeeYs18BTtq5k8d89S0c9YIi9LIVWuVnc5xBzZfFuv78OlSFvkAestq6R4CKwWaRFpTQMWiWGvLjdLOSPQaWSa6D1F343Pr6dhJFeQ0x7aM0JL_BS19eL390aplI7nY_B0hamtKVOLNB1U9z31kykWqSutopbjCUKm0THGhhifSmjiyYR9eER5zCnj4B6325zZwmUQdlmdEp0S8c2i51-Ex95FwnZ-DsQ_Pt19jDKMXU3rpqjO0wbSbCCwexaOLL_EMriGS88PJ9OAxXI-wvmxnMfegt6nP3BO4ar9vvq7rp94XGZxcNqZ_A3Msejw
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Decision+Support+System+for+Crop+Recommendation+Using+Machine+Learning+Classification+Algorithms&rft.jtitle=Agriculture+%28Basel%29&rft.au=Senapaty%2C+Murali+Krishna&rft.au=Ray%2C+Abhishek&rft.au=Padhy%2C+Neelamadhab&rft.date=2024-08-01&rft.pub=MDPI+AG&rft.eissn=2077-0472&rft.volume=14&rft.issue=8&rft.spage=1256&rft_id=info:doi/10.3390%2Fagriculture14081256&rft.externalDBID=HAS_PDF_LINK
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2077-0472&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2077-0472&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2077-0472&client=summon