التفاصيل البيبلوغرافية
| العنوان: |
Machine Learning Approaches for Forecasting the Best Microbial Strains to Alleviate Drought Impact in Agriculture |
| المؤلفون: |
Tymoteusz Miller, Grzegorz Mikiciuk, Anna Kisiel, Małgorzata Mikiciuk, Dominika Paliwoda, Lidia Sas-Paszt, Danuta Cembrowska-Lech, Adrianna Krzemińska, Agnieszka Kozioł, Adam Brysiewicz |
| المصدر: |
Agriculture; Volume 13; Issue 8; Pages: 1622 |
| بيانات النشر: |
Multidisciplinary Digital Publishing Institute |
| سنة النشر: |
2023 |
| المجموعة: |
MDPI Open Access Publishing |
| مصطلحات موضوعية: |
machine learning, predictive analytics, soil microbiome, climate resilience, crop yield enhancement, SVM, ANN, data-driven agriculture, sustainable farming practices, crop stress management, agricultural biotechnology, artificial intelligence |
| جغرافية الموضوع: |
agris |
| الوصف: |
Drought conditions pose significant challenges to sustainable agriculture and food security. Identifying microbial strains that can mitigate drought effects is crucial to enhance crop resilience and productivity. This study presents a comprehensive comparison of several machine learning models, including Random Forest, Decision Tree, XGBoost, Support Vector Machine (SVM), and Artificial Neural Network (ANN), to predict optimal microbial strains for this purpose. Models were assessed on multiple metrics, such as accuracy, standard deviation of results, gains, total computation time, and training time per 1000 rows of data. Notably, the Gradient Boosted Trees model outperformed others in accuracy but required extensive computational resources. This underscores the balance between accuracy and computational efficiency in machine learning applications. Leveraging machine learning for selecting microbial strains signifies a leap beyond traditional methods, offering improved efficiency and efficacy. These insights hold profound implications for agriculture, especially concerning drought mitigation, thus furthering the cause of sustainable agriculture and ensuring food security. |
| نوع الوثيقة: |
text |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
Digital Agriculture; https://dx.doi.org/10.3390/agriculture13081622 |
| DOI: |
10.3390/agriculture13081622 |
| الاتاحة: |
https://doi.org/10.3390/agriculture13081622 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: |
edsbas.3646FAD6 |
| قاعدة البيانات: |
BASE |