Sensor, IoT-based post-harvest shelf life determination of tomato (Lycopersicon esculentum) through machine learning predictive analysis for intelligent transport
Aim: The current research explores the potential of machine learning predictive models in optimizing the storage conditions of tomatoes. This is achieved through Internet of Things (IoT) technology, sensors, cameras, and microprocessors integrated into refrigerators along the supply chain. Methodolo...
Uložené v:
| Vydané v: | Journal of environmental biology Ročník 45; číslo 4; s. 455 - 464 |
|---|---|
| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Lucknow
Triveni Enterprises
01.07.2024
|
| Predmet: | |
| ISSN: | 0254-8704, 2394-0379 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Aim: The current research explores the potential of machine learning predictive models in optimizing the storage conditions of tomatoes. This is achieved through Internet of Things (IoT) technology, sensors, cameras, and microprocessors integrated into refrigerators along the supply chain. Methodology: Controlling temperature and humidity inside the refrigerated container was accomplished by implementing the Arduino microcontroller and supplementary hardware components, including the ESP32 module relay, an advancement over the ESP8266 microcontroller. The Arduino Integrated Development Environment (IDE) was used as software platform for this experimentation. Various parameters, including humidity, oxygen, carbon-di-oxide, and shelf life, were recorded at different temperatures and on different days. Subsequently, the collected data was analyzed employing machine-learning models to determine the most effective prediction model for these variables. Results: From the results it has been revealed that apolynomial of degree 4 is the best-fit regressor model for the data on humidity. Polynomials of degrees 2, 2, and 3 are the best models for the target variables oxygen, carbon-di-oxide, and shelf life. Interpretation: During analysis, This result suggests that different polynomial degrees are optimal for modeling different variables in the dataset. Polynomials of degrees 2, 2, and 3 are the best ML models for the target variables oxygen, carbon-di-oxide, and shelf life, respectively,to enhance the effectiveness of our predictive models. Key words: Io T sensors, ML models, Quantile loss, Supply chain, Tomato |
|---|---|
| AbstractList | Aim: The current research explores the potential of machine learning predictive models in optimizing the storage conditions of tomatoes. This is achieved through Internet of Things (loT) technology, sensors, cameras, and microprocessors integrated into refrigerators along the supply chain. Methodology: Controlling temperature and humidity inside the refrigerated containerwas accomplished by implementing the Arduino microcontroller and supplementary hardware components, including the ESP32 module relay, an advancement over the ESP8266 microcontroller. The Arduino Integrated Development Environment (IDE) was used as software platform for this experimentation. Various parameters, including humidity, oxygen, carbon-di-oxide, and shelf life, were recorded at different temperatures and on different days. Subsequently, the collected data was analyzed employing machine-learning models to determine the most effective prediction model for these variables. From the results it has been revealed that apolynomial of degree 4 is the best-fit regressor model for the data on humidity. Polynomials of degrees 2,2, and 3 are the best models forthe target variables oxygen, carbon-di-oxide, and shelf life. Interpretation: During analysis, This result suggests that different polynomial degrees are optimal for modeling different variables in the dataset. Polynomials of degrees 2,2, and 3 are the best ML models forthe target variables oxygen, carbon-di-oxide, and shelf life, respectively.to enhance the effectiveness of ourpredictive models. Aim: The current research explores the potential of machine learning predictive models in optimizing the storage conditions of tomatoes. This is achieved through Internet of Things (IoT) technology, sensors, cameras, and microprocessors integrated into refrigerators along the supply chain. Methodology: Controlling temperature and humidity inside the refrigerated container was accomplished by implementing the Arduino microcontroller and supplementary hardware components, including the ESP32 module relay, an advancement over the ESP8266 microcontroller. The Arduino Integrated Development Environment (IDE) was used as software platform for this experimentation. Various parameters, including humidity, oxygen, carbon-di-oxide, and shelf life, were recorded at different temperatures and on different days. Subsequently, the collected data was analyzed employing machine-learning models to determine the most effective prediction model for these variables. Results: From the results it has been revealed that apolynomial of degree 4 is the best-fit regressor model for the data on humidity. Polynomials of degrees 2, 2, and 3 are the best models for the target variables oxygen, carbon-di-oxide, and shelf life. Interpretation: During analysis, This result suggests that different polynomial degrees are optimal for modeling different variables in the dataset. Polynomials of degrees 2, 2, and 3 are the best ML models for the target variables oxygen, carbon-di-oxide, and shelf life, respectively,to enhance the effectiveness of our predictive models. Key words: Io T sensors, ML models, Quantile loss, Supply chain, Tomato |
| Author | Shankaraswamy, J. Radhika, T.S.L. |
| Author_xml | – sequence: 1 givenname: J. orcidid: 0000-0002-5623-6384 surname: Shankaraswamy fullname: Shankaraswamy, J. – sequence: 2 givenname: T.S.L. surname: Radhika fullname: Radhika, T.S.L. |
| BookMark | eNp9kc9qGzEQxkVxoY6TF-hJ0EsD3VpeSfvnGEyaBNwUkvQstNqRV0YrbSWtwa_TJ62S9JRD5jIwfL9hvvnO0MJ5Bwh93pDvZclosz5At2Z8zdY_H-4LTmn7AS1L2rKC0LpdoCUpOSuamrBP6CLGA8lF27Lm7RL9fQQXffiG7_xT0ckIPZ58TMUgwxFiwnEAq7E1GnAPCcJonEzGO-w1Tn6UyeOvu5PyE4RoVJ5DVLMFl-bxEqch-Hk_4FGqwTjAFmRwxu3xFKA3KpkjYOmkPUUTsfYBG5fAWrPPPE5Bujj5kM7RRy1thIv_fYV-_7h-2t4Wu183d9urXaGylVR0fVM2G6IaqjtKKt1y1kvdag4KJKeSqqokXcsrTZUmfd1TwiraadWpqmOyoiv05XXvFPyfOZsXBz-HfF4UlDSM1aSpN1nVvKpU8DEG0EKZ9PKSfLCxYkPESygihyIYF0zkUMRzKBkt36BTMKMMp_egf65umMI |
| CitedBy_id | crossref_primary_10_1177_18761364251372647 |
| ContentType | Journal Article |
| Copyright | Copyright Triveni Enterprises Jul 2024 |
| Copyright_xml | – notice: Copyright Triveni Enterprises Jul 2024 |
| CorporateAuthor | Department of Fruit Science, College of Horticulture, Mojerla, Sri Konda Laxman,Wanaparthy-509 382, India Department of Mathematics, BITS Pilani, Hyderabad Campus, Hyderabad-500 078, India |
| CorporateAuthor_xml | – name: Department of Fruit Science, College of Horticulture, Mojerla, Sri Konda Laxman,Wanaparthy-509 382, India – name: Department of Mathematics, BITS Pilani, Hyderabad Campus, Hyderabad-500 078, India |
| DBID | AAYXX CITATION 04Q 04W 3V. 7ST 7U7 7X7 7XB 88E 8FE 8FG 8FH 8FI 8FJ 8FK ABJCF ABUWG AEUYN AFKRA ATCPS AZQEC BBNVY BENPR BGLVJ BHPHI C1K CCPQU DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. L6V LK8 M0S M1P M7N M7P M7S PATMY PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PTHSS PYCSY SOI |
| DOI | 10.22438/jeb/45/4/MRN-5339 |
| DatabaseName | CrossRef India Database India Database: Science & Technology ProQuest Central (Corporate) Environment Abstracts Toxicology Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest MSED ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Agricultural & Environmental Science Collection ProQuest Central Essentials - QC Biological Science Collection ProQuest Central ProQuest Technology Collection Natural Science Collection Environmental Sciences and Pollution Management ProQuest One ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Engineering Collection ProQuest Biological Science Collection ProQuest Health & Medical Collection Medical Database Algology Mycology and Protozoology Abstracts (Microbiology C) Biological Science Database Engineering Database Environmental Science Database ProQuest Central Premium ProQuest One Academic (New) ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection Environmental Science Collection Environment Abstracts |
| DatabaseTitle | CrossRef ProQuest Central Student ProQuest Central Essentials SciTech Premium Collection ProQuest Central China Environmental Sciences and Pollution Management ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Engineering Collection ProQuest Indian Journals Engineering Database ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest Hospital Collection (Alumni) Environmental Science Collection ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Environmental Science Database ProQuest One Academic ProQuest One Academic (New) Technology Collection ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central ProQuest Health & Medical Research Collection ProQuest Engineering Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Algology Mycology and Protozoology Abstracts (Microbiology C) Agricultural & Environmental Science Collection Toxicology Abstracts ProQuest SciTech Collection ProQuest Medical Library Materials Science & Engineering Collection Indian Journals: Science & Technology Environment Abstracts ProQuest Central (Alumni) |
| DatabaseTitleList | ProQuest Central Student CrossRef |
| Database_xml | – sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Ecology |
| EISSN | 2394-0379 |
| EndPage | 464 |
| ExternalDocumentID | 10_22438_jeb_45_4_MRN_5339 |
| GeographicLocations | India |
| GeographicLocations_xml | – name: India |
| GroupedDBID | 04Q 04W 5GY 7X7 7XC 88E 8FE 8FG 8FH 8FI 8FJ AAYXX ABJCF ABUWG ACIWK ACPRK ADBBV AENEX AEUYN AFFHD AFKRA AFRAH AHMBA ALMA_UNASSIGNED_HOLDINGS ATCPS BANNL BBNVY BENPR BGLVJ BHPHI BPHCQ BVXVI CCPQU CITATION EDH FYUFA HCIFZ HMCUK L6V LK8 M1P M7P M7S OK1 PATMY PHGZM PHGZT PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PTHSS PYCSY RNS UKHRP 3V. 7ST 7U7 7XB 8FK AZQEC C1K DWQXO GNUQQ K9. M7N PKEHL PQEST PQUKI PRINS SOI |
| ID | FETCH-LOGICAL-c275t-bd82810c83fb306f954daf9f5ecea53a3c620b956f3cf0d7d30463bfcbc6b4a63 |
| IEDL.DBID | 7X7 |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001251837300011&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0254-8704 |
| IngestDate | Sat Nov 29 14:18:36 EST 2025 Sat Nov 29 02:44:47 EST 2025 Tue Nov 18 21:03:45 EST 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 4 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c275t-bd82810c83fb306f954daf9f5ecea53a3c620b956f3cf0d7d30463bfcbc6b4a63 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-5623-6384 |
| PQID | 3084470871 |
| PQPubID | 636374 |
| PageCount | 10 |
| ParticipantIDs | proquest_journals_3084470871 crossref_citationtrail_10_22438_jeb_45_4_MRN_5339 crossref_primary_10_22438_jeb_45_4_MRN_5339 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-07-01 |
| PublicationDateYYYYMMDD | 2024-07-01 |
| PublicationDate_xml | – month: 07 year: 2024 text: 2024-07-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Lucknow |
| PublicationPlace_xml | – name: Lucknow |
| PublicationTitle | Journal of environmental biology |
| PublicationYear | 2024 |
| Publisher | Triveni Enterprises |
| Publisher_xml | – name: Triveni Enterprises |
| SSID | ssj0000392759 |
| Score | 2.3443985 |
| Snippet | Aim: The current research explores the potential of machine learning predictive models in optimizing the storage conditions of tomatoes. This is achieved... |
| SourceID | proquest crossref |
| SourceType | Aggregation Database Enrichment Source Index Database |
| StartPage | 455 |
| SubjectTerms | Artificial intelligence Carbon Commodities Data collection Effectiveness Food Food quality Fruits Harvest Horticulture Humidity Internet of Things Learning algorithms Lycopersicon esculentum Machine learning Microcontrollers Microprocessors Optimization Oxygen Physiology Polynomials Prediction models Sensors Shelf life Software Software development tools Storage conditions Supply chains Temperature Tomatoes Vegetables |
| Title | Sensor, IoT-based post-harvest shelf life determination of tomato (Lycopersicon esculentum) through machine learning predictive analysis for intelligent transport |
| URI | https://www.proquest.com/docview/3084470871 |
| Volume | 45 |
| WOSCitedRecordID | wos001251837300011&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: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 2394-0379 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000392759 issn: 0254-8704 databaseCode: M7P dateStart: 20110501 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2394-0379 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000392759 issn: 0254-8704 databaseCode: M7S dateStart: 20110501 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: Environmental Science Database customDbUrl: eissn: 2394-0379 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000392759 issn: 0254-8704 databaseCode: PATMY dateStart: 20110501 isFulltext: true titleUrlDefault: http://search.proquest.com/environmentalscience providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 2394-0379 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000392759 issn: 0254-8704 databaseCode: 7X7 dateStart: 20110501 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: India Database customDbUrl: eissn: 2394-0379 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000392759 issn: 0254-8704 databaseCode: 04Q dateStart: 20110501 isFulltext: true titleUrlDefault: https://search.proquest.com/indianjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2394-0379 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000392759 issn: 0254-8704 databaseCode: BENPR dateStart: 20110501 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3LbtQwFLVoCxIbylMUysgLFiCw4sZ2nKyqgqYCqYyGtqDZRX7SQdPJkASk_g5f2muPM6ibbthkkThSlGPfh-_1OQi9ZoI6wb0kOmeS8EoXRBvIeSqV59ZKo12s4H8_kZNJOZtV07Th1qW2ysEmRkNtGxP2yDNGS84lhfj-cPWLBNWoUF1NEhpbaCfIZod5Lmdys8dCwfnLqJcWznzDwqd8fW4G_BYrs59OZ1xkPPtyOiEQ9lQ3fdNN0xz9zfHu_37pQ_QgRZr4aD01HqE7bvkY3RtHluqrJ-jvGSSwTfsef27OSfBlFq-aricXqg3MG7i7cAuPF3PvsB1aZgKIuPG4byDObfCbk6twpqUFoOG-Cy2twYVdvsVJ_QdfxlZNh5M2xQ-8akNhKJhYrBIdCoawGc83zKA97ge-9afo2_H4_OMnkgQbiIH_3BNtIX87oKZkXkMq4ivBrfKVF844JZhipsiphozMM-OplTbylWlvtCk0VwV7hraXzdI9R9iURWmNclbxkuuCKSuEdFIzLZ0rXLGHDgaoapPYzIOoxqKGrCbCWwO8NRc1rwHeOsC7h95t3lmtuTxuHb0_wFundd3V_7B9cfvjl-h-DuHPurF3H2337W_3Ct01f_p5147QFuVfR3GyjtDOh_FkejoKXafTeD27Biuc-iQ |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3LbtQwFLVKAcGGN2qhgBcggaiVNLbjZIEQglYddTqqYEDdGT_bQdPJkATQ_A4fwDdy7UkGddNdF2zzWjgn95wb33suQs8pTx1nXhCdUUFYqXOiDeQ8pcoya4XRLu7gfxmK0ag4Pi6P1tCfvhcmlFX2MTEGaluZ8I88oWnBmEhB37-dfydhalTYXe1HaCxhceAWvyBla94MPsD7fZFle7vj9_ukmypATCZ4S7SFJGMnNQX1GvSyLzmzypeeO-MUp4qaPEs1pA2eGp9aYaOplvZGm1wzlVN47hV0FWRElsZSwaPVP50UxIaI89lCjzkEmpQt-3SAJ2mRfHM6YTxhyeHHEQGZVZ7nwvNUEPlt7_b_tjJ30K1OSeN3S-jfRWtudg9d340u3Iv76PcnSNCrehsPqjEJXG3xvGpacqrq4CyCm1M39Xg68Q7bviQogBRXHrcV6PgKvxwuQs9ODUCG4y6U7AaKPnuFu-lG-CyWojrczd44wfM6bHwFCsGqs3vBkBbgycr5tMVt7yf_AH2-lAV6iNZn1cxtIGyKvLBGOatYwXROleVcOKGpFs7lLt9EOz00pOnc2sPQkKmErC3CSQKcJOOSSYCTDHDaRK9X98yXXiUXXr3Vw0l2cauR_7D06OLTz9CN_fHhUA4Ho4PH6GYGUm9ZxLyF1tv6h3uCrpmf7aSpn8ZPBKOvl428vxACVUM |
| 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=Sensor%2C+IoT-based+post-harvest+shelf+life+determination+of+tomato+%28Lycopersicon+esculentum%29+through+machine+learning+predictive+analysis+for+intelligent+transport&rft.jtitle=Journal+of+environmental+biology&rft.au=Shankaraswamy%2C+J&rft.au=Radhika%2C+T+S&rft.date=2024-07-01&rft.pub=Triveni+Enterprises&rft.issn=0254-8704&rft.eissn=2394-0379&rft.volume=45&rft.issue=4&rft.spage=455&rft.epage=464&rft_id=info:doi/10.22438%2Fjeb%2F45%2F4%2FMRN-5339 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0254-8704&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0254-8704&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0254-8704&client=summon |