Machine and Deep Learning: Artificial Intelligence Application in Biotic and Abiotic Stress Management in Plants
Biotic and abiotic stresses significantly affect plant fitness, resulting in a serious loss in food production. Biotic and abiotic stresses predominantly affect metabolite biosynthesis, gene and protein expression, and genome variations. However, light doses of stress result in the production of pos...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IMR Press
2024-01-01
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| Series: | Frontiers in Bioscience-Landmark |
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| Online Access: | https://www.imrpress.com/journal/FBL/29/1/10.31083/j.fbl2901020 |
| _version_ | 1827368878421835776 |
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| author | Caiming Gou Sara Zafar Fatima Zuhair Hasnain Nazia Aslam Naeem Iqbal Sammar Abbas Hui Li Jia Li Bo Chen Arthur J. Ragauskas Manzar Abbas |
| author_facet | Caiming Gou Sara Zafar Fatima Zuhair Hasnain Nazia Aslam Naeem Iqbal Sammar Abbas Hui Li Jia Li Bo Chen Arthur J. Ragauskas Manzar Abbas |
| author_sort | Caiming Gou |
| collection | DOAJ |
| description | Biotic and abiotic stresses significantly affect plant fitness, resulting in a serious loss in food production. Biotic and abiotic stresses predominantly affect metabolite biosynthesis, gene and protein expression, and genome variations. However, light doses of stress result in the production of positive attributes in crops, like tolerance to stress and biosynthesis of metabolites, called hormesis. Advancement in artificial intelligence (AI) has enabled the development of high-throughput gadgets such as high-resolution imagery sensors and robotic aerial vehicles, i.e., satellites and unmanned aerial vehicles (UAV), to overcome biotic and abiotic stresses. These High throughput (HTP) gadgets produce accurate but big amounts of data. Significant datasets such as transportable array for remotely sensed agriculture and phenotyping reference platform (TERRA-REF) have been developed to forecast abiotic stresses and early detection of biotic stresses. For accurately measuring the model plant stress, tools like Deep Learning (DL) and Machine Learning (ML) have enabled early detection of desirable traits in a large population of breeding material and mitigate plant stresses. In this review, advanced applications of ML and DL in plant biotic and abiotic stress management have been summarized. |
| first_indexed | 2024-03-08T09:37:11Z |
| format | Article |
| id | doaj.art-c8060e17a970459bbcaafa8944bbb1ed |
| institution | Directory Open Access Journal |
| issn | 2768-6701 |
| language | English |
| last_indexed | 2024-03-08T09:37:11Z |
| publishDate | 2024-01-01 |
| publisher | IMR Press |
| record_format | Article |
| series | Frontiers in Bioscience-Landmark |
| spelling | doaj.art-c8060e17a970459bbcaafa8944bbb1ed2024-01-30T07:44:34ZengIMR PressFrontiers in Bioscience-Landmark2768-67012024-01-012912010.31083/j.fbl2901020S2768-6701(23)01105-XMachine and Deep Learning: Artificial Intelligence Application in Biotic and Abiotic Stress Management in PlantsCaiming Gou0Sara Zafar1Fatima2Zuhair Hasnain3Nazia Aslam4Naeem Iqbal5Sammar Abbas6Hui Li7Jia Li8Bo Chen9Arthur J. Ragauskas10Manzar Abbas11School of Agriculture, Forestry and Food Engineering, Yibin University, 644000 Yibin, Sichuan, ChinaBotany Department, Government College University, 38000 Faisalabad, Punjab, PakistanDepartment of Mathematics, University of Karachi, 75270 Karachi, Sindh, PakistanPMAS Arid Agriculture University, Rawalpindi, 44000 Rawalpindi, Punjab, PakistanBotany Department, Government College University, 38000 Faisalabad, Punjab, PakistanBotany Department, Government College University, 38000 Faisalabad, Punjab, PakistanCollege of Biological Sciences and Biotechnology, Beijing Forestry University, 100091 Beijing, ChinaCollege of Forestry, Inner Mongolia Agricultural University, 010019 Hohhot, ChinaSchool of Agriculture, Forestry and Food Engineering, Yibin University, 644000 Yibin, Sichuan, ChinaSchool of Agriculture, Forestry and Food Engineering, Yibin University, 644000 Yibin, Sichuan, ChinaDepartment of Forestry, Wildlife, and Fisheries, Center for Renewable Carbon, University of Tennessee Institute of Agriculture, Knoxville, TN 37996, USASchool of Agriculture, Forestry and Food Engineering, Yibin University, 644000 Yibin, Sichuan, ChinaBiotic and abiotic stresses significantly affect plant fitness, resulting in a serious loss in food production. Biotic and abiotic stresses predominantly affect metabolite biosynthesis, gene and protein expression, and genome variations. However, light doses of stress result in the production of positive attributes in crops, like tolerance to stress and biosynthesis of metabolites, called hormesis. Advancement in artificial intelligence (AI) has enabled the development of high-throughput gadgets such as high-resolution imagery sensors and robotic aerial vehicles, i.e., satellites and unmanned aerial vehicles (UAV), to overcome biotic and abiotic stresses. These High throughput (HTP) gadgets produce accurate but big amounts of data. Significant datasets such as transportable array for remotely sensed agriculture and phenotyping reference platform (TERRA-REF) have been developed to forecast abiotic stresses and early detection of biotic stresses. For accurately measuring the model plant stress, tools like Deep Learning (DL) and Machine Learning (ML) have enabled early detection of desirable traits in a large population of breeding material and mitigate plant stresses. In this review, advanced applications of ML and DL in plant biotic and abiotic stress management have been summarized.https://www.imrpress.com/journal/FBL/29/1/10.31083/j.fbl2901020biotic and abiotic stressessatelliteunmanned aerial vehiclesmart-phonesartificial intelligencemachine learningdeep learningplant phenotyping |
| spellingShingle | Caiming Gou Sara Zafar Fatima Zuhair Hasnain Nazia Aslam Naeem Iqbal Sammar Abbas Hui Li Jia Li Bo Chen Arthur J. Ragauskas Manzar Abbas Machine and Deep Learning: Artificial Intelligence Application in Biotic and Abiotic Stress Management in Plants Frontiers in Bioscience-Landmark biotic and abiotic stresses satellite unmanned aerial vehicle smart-phones artificial intelligence machine learning deep learning plant phenotyping |
| title | Machine and Deep Learning: Artificial Intelligence Application in Biotic and Abiotic Stress Management in Plants |
| title_full | Machine and Deep Learning: Artificial Intelligence Application in Biotic and Abiotic Stress Management in Plants |
| title_fullStr | Machine and Deep Learning: Artificial Intelligence Application in Biotic and Abiotic Stress Management in Plants |
| title_full_unstemmed | Machine and Deep Learning: Artificial Intelligence Application in Biotic and Abiotic Stress Management in Plants |
| title_short | Machine and Deep Learning: Artificial Intelligence Application in Biotic and Abiotic Stress Management in Plants |
| title_sort | machine and deep learning artificial intelligence application in biotic and abiotic stress management in plants |
| topic | biotic and abiotic stresses satellite unmanned aerial vehicle smart-phones artificial intelligence machine learning deep learning plant phenotyping |
| url | https://www.imrpress.com/journal/FBL/29/1/10.31083/j.fbl2901020 |
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