A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote Sensing
Deep learning has emerged as a potential tool for crop yield prediction, allowing the model to automatically extract features and learn from the datasets. Meanwhile, smart farming technology enables the farmers to achieve maximum crop yield by extracting essential parameters of crop growth. This sys...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/9/1990 |
| _version_ | 1797503111129989120 |
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| author | Priyanga Muruganantham Santoso Wibowo Srimannarayana Grandhi Nahidul Hoque Samrat Nahina Islam |
| author_facet | Priyanga Muruganantham Santoso Wibowo Srimannarayana Grandhi Nahidul Hoque Samrat Nahina Islam |
| author_sort | Priyanga Muruganantham |
| collection | DOAJ |
| description | Deep learning has emerged as a potential tool for crop yield prediction, allowing the model to automatically extract features and learn from the datasets. Meanwhile, smart farming technology enables the farmers to achieve maximum crop yield by extracting essential parameters of crop growth. This systematic literature review highlights the existing research gaps in a particular area of deep learning methodologies and guides us in analyzing the impact of vegetation indices and environmental factors on crop yield. To achieve the aims of this study, prior studies from 2012 to 2022 from various databases are collected and analyzed. The study focuses on the advantages of using deep learning in crop yield prediction, the suitable remote sensing technology based on the data acquisition requirements, and the various features that influence crop yield prediction. This study finds that Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) are the most widely used deep learning approaches for crop yield prediction. The commonly used remote sensing technology is satellite remote sensing technology—in particular, the use of the Moderate-Resolution Imaging Spectroradiometer (MODIS). Findings show that vegetation indices are the most used feature for crop yield prediction. However, it is also observed that the most used features in the literature do not always work for all the approaches. The main challenges of using deep learning approaches and remote sensing for crop yield prediction are how to improve the working model for better accuracy, the practical implication of the model for providing accurate information about crop yield to agriculturalists, growers, and policymakers, and the issue with the black box property. |
| first_indexed | 2024-03-10T03:45:51Z |
| format | Article |
| id | doaj.art-58d9d05400674ed3ac000de2aeb23ea6 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:45:51Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-58d9d05400674ed3ac000de2aeb23ea62023-11-23T09:08:58ZengMDPI AGRemote Sensing2072-42922022-04-01149199010.3390/rs14091990A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote SensingPriyanga Muruganantham0Santoso Wibowo1Srimannarayana Grandhi2Nahidul Hoque Samrat3Nahina Islam4School of Engineering and Technology, Central Queensland University, Melbourne 3000, AustraliaSchool of Engineering and Technology, Central Queensland University, Melbourne 3000, AustraliaSchool of Engineering and Technology, Central Queensland University, Melbourne 3000, AustraliaSchool of Engineering and Technology, Central Queensland University, Melbourne 3000, AustraliaSchool of Engineering and Technology, Central Queensland University, Melbourne 3000, AustraliaDeep learning has emerged as a potential tool for crop yield prediction, allowing the model to automatically extract features and learn from the datasets. Meanwhile, smart farming technology enables the farmers to achieve maximum crop yield by extracting essential parameters of crop growth. This systematic literature review highlights the existing research gaps in a particular area of deep learning methodologies and guides us in analyzing the impact of vegetation indices and environmental factors on crop yield. To achieve the aims of this study, prior studies from 2012 to 2022 from various databases are collected and analyzed. The study focuses on the advantages of using deep learning in crop yield prediction, the suitable remote sensing technology based on the data acquisition requirements, and the various features that influence crop yield prediction. This study finds that Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) are the most widely used deep learning approaches for crop yield prediction. The commonly used remote sensing technology is satellite remote sensing technology—in particular, the use of the Moderate-Resolution Imaging Spectroradiometer (MODIS). Findings show that vegetation indices are the most used feature for crop yield prediction. However, it is also observed that the most used features in the literature do not always work for all the approaches. The main challenges of using deep learning approaches and remote sensing for crop yield prediction are how to improve the working model for better accuracy, the practical implication of the model for providing accurate information about crop yield to agriculturalists, growers, and policymakers, and the issue with the black box property.https://www.mdpi.com/2072-4292/14/9/1990deep learning approachescrop yield predictionremote sensing techniquessystematic literature review |
| spellingShingle | Priyanga Muruganantham Santoso Wibowo Srimannarayana Grandhi Nahidul Hoque Samrat Nahina Islam A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote Sensing Remote Sensing deep learning approaches crop yield prediction remote sensing techniques systematic literature review |
| title | A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote Sensing |
| title_full | A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote Sensing |
| title_fullStr | A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote Sensing |
| title_full_unstemmed | A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote Sensing |
| title_short | A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote Sensing |
| title_sort | systematic literature review on crop yield prediction with deep learning and remote sensing |
| topic | deep learning approaches crop yield prediction remote sensing techniques systematic literature review |
| url | https://www.mdpi.com/2072-4292/14/9/1990 |
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