Systematic approaches to machine learning models for predicting pesticide toxicity
Pesticides play an important role in modern agriculture by protecting crops from pests and diseases. However, the negative consequences of pesticides, such as environmental contamination and adverse effects on human and ecological health, underscore the importance of accurate toxicity predictions. T...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-04-01
|
| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024047832 |
| _version_ | 1797232854683353088 |
|---|---|
| author | Ganesan Anandhi M. Iyapparaja |
| author_facet | Ganesan Anandhi M. Iyapparaja |
| author_sort | Ganesan Anandhi |
| collection | DOAJ |
| description | Pesticides play an important role in modern agriculture by protecting crops from pests and diseases. However, the negative consequences of pesticides, such as environmental contamination and adverse effects on human and ecological health, underscore the importance of accurate toxicity predictions. To address this issue, artificial intelligence models have emerged as valuable methods for predicting the toxicity of organic compounds. In this review article, we explore the application of machine learning (ML) for pesticide toxicity prediction. This review provides a detailed summary of recent developments, prediction models, and datasets used for pesticide toxicity prediction. In this analysis, we compared the results of several algorithms that predict the harmfulness of various classes of pesticides. Furthermore, this review article identified emerging trends and areas for future direction, showcasing the transformative potential of machine learning in promoting safer pesticide usage and sustainable agriculture. |
| first_indexed | 2024-04-24T16:06:54Z |
| format | Article |
| id | doaj.art-cb1f059551da4f92b77142edb8c8a25e |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-24T16:06:54Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-cb1f059551da4f92b77142edb8c8a25e2024-04-01T04:04:24ZengElsevierHeliyon2405-84402024-04-01107e28752Systematic approaches to machine learning models for predicting pesticide toxicityGanesan Anandhi0M. Iyapparaja1Department of Smart Computing, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, IndiaCorresponding author.; Department of Smart Computing, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, IndiaPesticides play an important role in modern agriculture by protecting crops from pests and diseases. However, the negative consequences of pesticides, such as environmental contamination and adverse effects on human and ecological health, underscore the importance of accurate toxicity predictions. To address this issue, artificial intelligence models have emerged as valuable methods for predicting the toxicity of organic compounds. In this review article, we explore the application of machine learning (ML) for pesticide toxicity prediction. This review provides a detailed summary of recent developments, prediction models, and datasets used for pesticide toxicity prediction. In this analysis, we compared the results of several algorithms that predict the harmfulness of various classes of pesticides. Furthermore, this review article identified emerging trends and areas for future direction, showcasing the transformative potential of machine learning in promoting safer pesticide usage and sustainable agriculture.http://www.sciencedirect.com/science/article/pii/S2405844024047832Artificial intelligenceMachine learningAlgorithmsPesticideToxicity |
| spellingShingle | Ganesan Anandhi M. Iyapparaja Systematic approaches to machine learning models for predicting pesticide toxicity Heliyon Artificial intelligence Machine learning Algorithms Pesticide Toxicity |
| title | Systematic approaches to machine learning models for predicting pesticide toxicity |
| title_full | Systematic approaches to machine learning models for predicting pesticide toxicity |
| title_fullStr | Systematic approaches to machine learning models for predicting pesticide toxicity |
| title_full_unstemmed | Systematic approaches to machine learning models for predicting pesticide toxicity |
| title_short | Systematic approaches to machine learning models for predicting pesticide toxicity |
| title_sort | systematic approaches to machine learning models for predicting pesticide toxicity |
| topic | Artificial intelligence Machine learning Algorithms Pesticide Toxicity |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024047832 |
| work_keys_str_mv | AT ganesananandhi systematicapproachestomachinelearningmodelsforpredictingpesticidetoxicity AT miyapparaja systematicapproachestomachinelearningmodelsforpredictingpesticidetoxicity |