A review of machine learning approaches for predicting lettuce yield in hydroponic systems
Accurate and timely yield prediction of hydroponically grown lettuce is essential for financial planning, strategic decision-making, and enhancing farmers' profitability. In controlled hydroponic environments, this prediction remains challenging, mainly due to complex factors influencing growth...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
|
| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525001583 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850186457324453888 |
|---|---|
| author | Sabrina Sharmin Md. Tazel Hossan Mohammad Shorif Uddin |
| author_facet | Sabrina Sharmin Md. Tazel Hossan Mohammad Shorif Uddin |
| author_sort | Sabrina Sharmin |
| collection | DOAJ |
| description | Accurate and timely yield prediction of hydroponically grown lettuce is essential for financial planning, strategic decision-making, and enhancing farmers' profitability. In controlled hydroponic environments, this prediction remains challenging, mainly due to complex factors influencing growth. Machine Learning (ML) offers advanced methods to address these challenges. This review analyzes ML techniques for forecasting lettuce yield in hydroponic systems, starting with an overview of global trends in lettuce production. It then explores core ML methodologies, key model characteristics, and application-specific features that contribute to yield prediction. A comparative analysis of existing ML models also highlights their strengths and limitations. Current challenges, such as data integration and prediction accuracy, are discussed alongside potential improvements through remote sensing, monitoring, and feature optimization. This paper concludes by proposing a framework aimed at efficient yield prediction in hydroponics, offering insights for future research and applications in agricultural technology. |
| format | Article |
| id | doaj-art-01d458e69df34b7fa70027e34acf61fc |
| institution | OA Journals |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-01d458e69df34b7fa70027e34acf61fc2025-08-20T02:16:21ZengElsevierSmart Agricultural Technology2772-37552025-08-011110092510.1016/j.atech.2025.100925A review of machine learning approaches for predicting lettuce yield in hydroponic systemsSabrina Sharmin0Md. Tazel Hossan1Mohammad Shorif Uddin2Department of Computer Science & Engineering, Jahangirnagar University, Dhaka, Bangladesh; Corresponding author.Department of Computer Science & Engineering, Jahangirnagar University, Dhaka, BangladeshDepartment of Computer Science & Engineering, Jahangirnagar University, Dhaka, Bangladesh; Green University of Bangladesh, Rupganj, Narayanganj, BangladeshAccurate and timely yield prediction of hydroponically grown lettuce is essential for financial planning, strategic decision-making, and enhancing farmers' profitability. In controlled hydroponic environments, this prediction remains challenging, mainly due to complex factors influencing growth. Machine Learning (ML) offers advanced methods to address these challenges. This review analyzes ML techniques for forecasting lettuce yield in hydroponic systems, starting with an overview of global trends in lettuce production. It then explores core ML methodologies, key model characteristics, and application-specific features that contribute to yield prediction. A comparative analysis of existing ML models also highlights their strengths and limitations. Current challenges, such as data integration and prediction accuracy, are discussed alongside potential improvements through remote sensing, monitoring, and feature optimization. This paper concludes by proposing a framework aimed at efficient yield prediction in hydroponics, offering insights for future research and applications in agricultural technology.http://www.sciencedirect.com/science/article/pii/S2772375525001583Hydroponic systemsLettuce yield predictionMachine learningDeep learning |
| spellingShingle | Sabrina Sharmin Md. Tazel Hossan Mohammad Shorif Uddin A review of machine learning approaches for predicting lettuce yield in hydroponic systems Smart Agricultural Technology Hydroponic systems Lettuce yield prediction Machine learning Deep learning |
| title | A review of machine learning approaches for predicting lettuce yield in hydroponic systems |
| title_full | A review of machine learning approaches for predicting lettuce yield in hydroponic systems |
| title_fullStr | A review of machine learning approaches for predicting lettuce yield in hydroponic systems |
| title_full_unstemmed | A review of machine learning approaches for predicting lettuce yield in hydroponic systems |
| title_short | A review of machine learning approaches for predicting lettuce yield in hydroponic systems |
| title_sort | review of machine learning approaches for predicting lettuce yield in hydroponic systems |
| topic | Hydroponic systems Lettuce yield prediction Machine learning Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525001583 |
| work_keys_str_mv | AT sabrinasharmin areviewofmachinelearningapproachesforpredictinglettuceyieldinhydroponicsystems AT mdtazelhossan areviewofmachinelearningapproachesforpredictinglettuceyieldinhydroponicsystems AT mohammadshorifuddin areviewofmachinelearningapproachesforpredictinglettuceyieldinhydroponicsystems AT sabrinasharmin reviewofmachinelearningapproachesforpredictinglettuceyieldinhydroponicsystems AT mdtazelhossan reviewofmachinelearningapproachesforpredictinglettuceyieldinhydroponicsystems AT mohammadshorifuddin reviewofmachinelearningapproachesforpredictinglettuceyieldinhydroponicsystems |