Leveraging Machine Learning for Precision Agriculture: A Crop Yield Prediction and Recommendation System Article Swipe
Suman Kumari
,
Amrita Kumari
,
Ranjana Ray
,
Shubham Kumar
,
Sayan Sen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.56025/ijaresm.2025.1304252240
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.56025/ijaresm.2025.1304252240
Machine learning (ML) is transforming agriculture by enhancing crop production, reducing waste, and optimizing resources through data-driven decision-making. This study explores ML applications, analysing challenges and opportunities in integrating ML models with farm data and real-time IoT sensors. Evaluating 15 ML algorithms, the research proposes a novel feature combination scheme-enhanced algorithm, improving predictive accuracy. Experimental results show that modifying labels significantly impacts data analysis. Bayes Net achieved the highest accuracy (99.59%), followed by Naive Bayes Classifier (99.46%). These findings highlight ML’s potential in aiding farmers with crop growth, disease detection, soil health, and irrigation management. ML also boosts agricultural production while reducing costs. Integrating ML with IoT data enables precision agriculture, strengthening infrastructure and sustainability. ML-driven solutions enhance food security by improving crop yield predictions and mitigating climate and resource risks. Challenges include data quality, model interpretability, and integration with existing practices. Collaboration among researchers, agronomists, and developers is crucial for refining ML models. This study underscores ML’s role in shaping sustainable farming and calls for further research on algorithm refinement, data collection, and farmer-friendly applications. By optimizing agricultural processes, ML fosters smart farming and digital transformation. Its integration with traditional methods can create resilient, efficient, and sustainable agricultural systems, benefiting global food production and economic stability.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.56025/ijaresm.2025.1304252240
- https://doi.org/10.56025/ijaresm.2025.1304252240
- OA Status
- bronze
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410095802
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410095802Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.56025/ijaresm.2025.1304252240Digital Object Identifier
- Title
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Leveraging Machine Learning for Precision Agriculture: A Crop Yield Prediction and Recommendation SystemWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-01Full publication date if available
- Authors
-
Suman Kumari, Amrita Kumari, Ranjana Ray, Shubham Kumar, Sayan SenList of authors in order
- Landing page
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https://doi.org/10.56025/ijaresm.2025.1304252240Publisher landing page
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https://doi.org/10.56025/ijaresm.2025.1304252240Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://doi.org/10.56025/ijaresm.2025.1304252240Direct OA link when available
- Concepts
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Yield (engineering), Machine learning, Agriculture, Precision agriculture, Computer science, Artificial intelligence, Agricultural engineering, Crop, Agronomy, Engineering, Biology, Ecology, Materials science, MetallurgyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.10591017 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |