Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability
The agriculture sector is confronted with numerous challenges in the quest for accurate crop yield estimation, which is essential for efficient resource management and mitigating food scarcity in a rapidly growing global population. This research paper delves into the application of advanced Artific...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024168673 |
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| author | Md. Abu Jabed Masrah Azrifah Azmi Murad |
| author_facet | Md. Abu Jabed Masrah Azrifah Azmi Murad |
| author_sort | Md. Abu Jabed |
| collection | DOAJ |
| description | The agriculture sector is confronted with numerous challenges in the quest for accurate crop yield estimation, which is essential for efficient resource management and mitigating food scarcity in a rapidly growing global population. This research paper delves into the application of advanced Artificial Intelligence (AI) techniques to enhance crop yield estimation in the context of diverse agricultural challenges. Through a systematic literature review and analysis of relevant studies, this paper explores the role of AI methods, such as Machine Learning (ML) and Deep Learning (DL), in addressing the complexities posed by geographical variations, crop diversity, and cultivation areas. The review identifies a wealth of AI-powered solutions employed in crop yield prediction, emphasizing the importance of precise environmental and agricultural data. Key factors contributing to accurate estimation include temperature, rainfall, soil type, humidity, and various vegetation indices, such as NDVI, EVI, LAI, and NDWI. The research paper also examines the algorithms frequently utilized in the machine learning domain, including Random Forest (RF), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). In the realm of deep learning, Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM), and Deep Neural Networks (DNN) emerge as promising candidates. The findings of this study shed light on the transformative potential of advanced AI techniques in improving crop yield estimation accuracy, ultimately enhancing agricultural planning and resource management. By addressing the challenges posed by geographical diversity, crop heterogeneity, and changing environmental conditions, AI-driven models offer new avenues for sustainable agriculture in an ever-evolving world. This research paper provides valuable insights and directions for future studies, highlighting the critical role of AI in ensuring food security and sustainability in agriculture. |
| format | Article |
| id | doaj-art-3f4c7a0274bf4bfaa3ea97666c6bcf58 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-3f4c7a0274bf4bfaa3ea97666c6bcf582024-12-19T10:56:01ZengElsevierHeliyon2405-84402024-12-011024e40836Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainabilityMd. Abu Jabed0Masrah Azrifah Azmi Murad1Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Selangor, Malaysia; Department of Computer Science and Engineering, University of Creative Technology Chittagong, Chattogram, BangladeshFaculty of Computer Science and Information Technology, Universiti Putra Malaysia, Selangor, Malaysia; Corresponding author.The agriculture sector is confronted with numerous challenges in the quest for accurate crop yield estimation, which is essential for efficient resource management and mitigating food scarcity in a rapidly growing global population. This research paper delves into the application of advanced Artificial Intelligence (AI) techniques to enhance crop yield estimation in the context of diverse agricultural challenges. Through a systematic literature review and analysis of relevant studies, this paper explores the role of AI methods, such as Machine Learning (ML) and Deep Learning (DL), in addressing the complexities posed by geographical variations, crop diversity, and cultivation areas. The review identifies a wealth of AI-powered solutions employed in crop yield prediction, emphasizing the importance of precise environmental and agricultural data. Key factors contributing to accurate estimation include temperature, rainfall, soil type, humidity, and various vegetation indices, such as NDVI, EVI, LAI, and NDWI. The research paper also examines the algorithms frequently utilized in the machine learning domain, including Random Forest (RF), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). In the realm of deep learning, Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM), and Deep Neural Networks (DNN) emerge as promising candidates. The findings of this study shed light on the transformative potential of advanced AI techniques in improving crop yield estimation accuracy, ultimately enhancing agricultural planning and resource management. By addressing the challenges posed by geographical diversity, crop heterogeneity, and changing environmental conditions, AI-driven models offer new avenues for sustainable agriculture in an ever-evolving world. This research paper provides valuable insights and directions for future studies, highlighting the critical role of AI in ensuring food security and sustainability in agriculture.http://www.sciencedirect.com/science/article/pii/S2405844024168673Systematic literature reviewCrop yield productionDecision support systemMachine learningDeep learning |
| spellingShingle | Md. Abu Jabed Masrah Azrifah Azmi Murad Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability Heliyon Systematic literature review Crop yield production Decision support system Machine learning Deep learning |
| title | Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability |
| title_full | Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability |
| title_fullStr | Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability |
| title_full_unstemmed | Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability |
| title_short | Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability |
| title_sort | crop yield prediction in agriculture a comprehensive review of machine learning and deep learning approaches with insights for future research and sustainability |
| topic | Systematic literature review Crop yield production Decision support system Machine learning Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024168673 |
| work_keys_str_mv | AT mdabujabed cropyieldpredictioninagricultureacomprehensivereviewofmachinelearninganddeeplearningapproacheswithinsightsforfutureresearchandsustainability AT masrahazrifahazmimurad cropyieldpredictioninagricultureacomprehensivereviewofmachinelearninganddeeplearningapproacheswithinsightsforfutureresearchandsustainability |