Breeding of Solanaceous Crops Using AI: Machine Learning and Deep Learning Approaches—A Critical Review
This review discusses the potential of artificial intelligence (AI), particularly machine learning (ML) and its subset, deep learning (DL), in advancing the genetic improvement of Solanaceous crops. AI has emerged as a powerful solution to overcome the limitations of traditional breeding techniques,...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/15/3/757 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849340738341437440 |
|---|---|
| author | Maria Gerakari Anastasios Katsileros Konstantina Kleftogianni Eleni Tani Penelope J. Bebeli Vasileios Papasotiropoulos |
| author_facet | Maria Gerakari Anastasios Katsileros Konstantina Kleftogianni Eleni Tani Penelope J. Bebeli Vasileios Papasotiropoulos |
| author_sort | Maria Gerakari |
| collection | DOAJ |
| description | This review discusses the potential of artificial intelligence (AI), particularly machine learning (ML) and its subset, deep learning (DL), in advancing the genetic improvement of Solanaceous crops. AI has emerged as a powerful solution to overcome the limitations of traditional breeding techniques, which often involve time-consuming, resource-intensive processes with limited predictive accuracy. Through advanced algorithms and predictive models, ML and DL facilitate the identification and optimization of key traits, including higher yield, improved quality, pest resistance, and tolerance to extreme climatic conditions. By integrating big data analytics and omics, these methods enhance genomic selection (GS), support gene-editing technologies like CRISPR-Cas9, and accelerate crop breeding, thus enabling the development of resilient and adaptable crops. This review highlights the role of ML and DL in improving Solanaceae crops, such as tomato, potato, eggplant, and pepper, with the aim of developing novel varieties with superior agronomic and quality traits. Additionally, this study examines the advantages and limitations of AI-driven breeding compared to traditional methods in Solanaceae, emphasizing its contribution to agricultural resilience, food security, and environmental sustainability. |
| format | Article |
| id | doaj-art-21aea5334ea043be91e6326fbaaac6f0 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-21aea5334ea043be91e6326fbaaac6f02025-08-20T03:43:50ZengMDPI AGAgronomy2073-43952025-03-0115375710.3390/agronomy15030757Breeding of Solanaceous Crops Using AI: Machine Learning and Deep Learning Approaches—A Critical ReviewMaria Gerakari0Anastasios Katsileros1Konstantina Kleftogianni2Eleni Tani3Penelope J. Bebeli4Vasileios Papasotiropoulos5Laboratory of Plant Breeding & Biometry, Department of Crop Science, Agricultural University of Athens, Iera Odos 75, 11855 Athens, GreeceLaboratory of Plant Breeding & Biometry, Department of Crop Science, Agricultural University of Athens, Iera Odos 75, 11855 Athens, GreeceLaboratory of Plant Breeding & Biometry, Department of Crop Science, Agricultural University of Athens, Iera Odos 75, 11855 Athens, GreeceLaboratory of Plant Breeding & Biometry, Department of Crop Science, Agricultural University of Athens, Iera Odos 75, 11855 Athens, GreeceLaboratory of Plant Breeding & Biometry, Department of Crop Science, Agricultural University of Athens, Iera Odos 75, 11855 Athens, GreeceLaboratory of Plant Breeding & Biometry, Department of Crop Science, Agricultural University of Athens, Iera Odos 75, 11855 Athens, GreeceThis review discusses the potential of artificial intelligence (AI), particularly machine learning (ML) and its subset, deep learning (DL), in advancing the genetic improvement of Solanaceous crops. AI has emerged as a powerful solution to overcome the limitations of traditional breeding techniques, which often involve time-consuming, resource-intensive processes with limited predictive accuracy. Through advanced algorithms and predictive models, ML and DL facilitate the identification and optimization of key traits, including higher yield, improved quality, pest resistance, and tolerance to extreme climatic conditions. By integrating big data analytics and omics, these methods enhance genomic selection (GS), support gene-editing technologies like CRISPR-Cas9, and accelerate crop breeding, thus enabling the development of resilient and adaptable crops. This review highlights the role of ML and DL in improving Solanaceae crops, such as tomato, potato, eggplant, and pepper, with the aim of developing novel varieties with superior agronomic and quality traits. Additionally, this study examines the advantages and limitations of AI-driven breeding compared to traditional methods in Solanaceae, emphasizing its contribution to agricultural resilience, food security, and environmental sustainability.https://www.mdpi.com/2073-4395/15/3/757artificial intelligencemachine learningbig datadeep learningplant breedingSolanaceae |
| spellingShingle | Maria Gerakari Anastasios Katsileros Konstantina Kleftogianni Eleni Tani Penelope J. Bebeli Vasileios Papasotiropoulos Breeding of Solanaceous Crops Using AI: Machine Learning and Deep Learning Approaches—A Critical Review Agronomy artificial intelligence machine learning big data deep learning plant breeding Solanaceae |
| title | Breeding of Solanaceous Crops Using AI: Machine Learning and Deep Learning Approaches—A Critical Review |
| title_full | Breeding of Solanaceous Crops Using AI: Machine Learning and Deep Learning Approaches—A Critical Review |
| title_fullStr | Breeding of Solanaceous Crops Using AI: Machine Learning and Deep Learning Approaches—A Critical Review |
| title_full_unstemmed | Breeding of Solanaceous Crops Using AI: Machine Learning and Deep Learning Approaches—A Critical Review |
| title_short | Breeding of Solanaceous Crops Using AI: Machine Learning and Deep Learning Approaches—A Critical Review |
| title_sort | breeding of solanaceous crops using ai machine learning and deep learning approaches a critical review |
| topic | artificial intelligence machine learning big data deep learning plant breeding Solanaceae |
| url | https://www.mdpi.com/2073-4395/15/3/757 |
| work_keys_str_mv | AT mariagerakari breedingofsolanaceouscropsusingaimachinelearninganddeeplearningapproachesacriticalreview AT anastasioskatsileros breedingofsolanaceouscropsusingaimachinelearninganddeeplearningapproachesacriticalreview AT konstantinakleftogianni breedingofsolanaceouscropsusingaimachinelearninganddeeplearningapproachesacriticalreview AT elenitani breedingofsolanaceouscropsusingaimachinelearninganddeeplearningapproachesacriticalreview AT penelopejbebeli breedingofsolanaceouscropsusingaimachinelearninganddeeplearningapproachesacriticalreview AT vasileiospapasotiropoulos breedingofsolanaceouscropsusingaimachinelearninganddeeplearningapproachesacriticalreview |