Mapping fruit tree dynamics using phenological metrics from optimal Sentinel-2 data and Deep Neural Network
Abstract Accurate and up-to-date crop-type maps are essential for efficient management and well-informed decision-making, allowing accurate planning and execution of agricultural operations in the horticultural sector. The assessment of crop-related traits, such as the spatiotemporal variability of...
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| Language: | English |
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CABI
2023-11-01
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| Series: | CABI Agriculture and Bioscience |
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| Online Access: | https://doi.org/10.1186/s43170-023-00193-z |
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| author | Yingisani Chabalala Elhadi Adam Mahlatse Kganyago |
| author_facet | Yingisani Chabalala Elhadi Adam Mahlatse Kganyago |
| author_sort | Yingisani Chabalala |
| collection | DOAJ |
| description | Abstract Accurate and up-to-date crop-type maps are essential for efficient management and well-informed decision-making, allowing accurate planning and execution of agricultural operations in the horticultural sector. The assessment of crop-related traits, such as the spatiotemporal variability of phenology, can improve decision-making. The study aimed to extract phenological information from Sentinel-2 data to identify and distinguish between fruit trees and co-existing land use types on subtropical farms in Levubu, South Africa. However, the heterogeneity and complexity of the study area—composed of smallholder mixed cropping systems with overlapping spectra—constituted an obstacle to the application of optical pixel-based classification using machine learning (ML) classifiers. Given the socio-economic importance of fruit tree crops, the research sought to map the phenological dynamics of these crops using deep neural network (DNN) and optical Sentinel-2 data. The models were optimized to determine the best hyperparameters to achieve the best classification results. The classification results showed the maximum overall accuracies of 86.96%, 88.64%, 86.76%, and 87.25% for the April, May, June, and July images, respectively. The results demonstrate the potential of temporal phenological optical-based data in mapping fruit tree crops under different management systems. The availability of remotely sensed data with high spatial and spectral resolutions makes it possible to use deep learning models to support decision-making in agriculture. This creates new possibilities for deep learning to revolutionize and facilitate innovation within smart horticulture. |
| format | Article |
| id | doaj-art-c0de857f7e3745db8035bd67d2bc0f6b |
| institution | Kabale University |
| issn | 2662-4044 |
| language | English |
| publishDate | 2023-11-01 |
| publisher | CABI |
| record_format | Article |
| series | CABI Agriculture and Bioscience |
| spelling | doaj-art-c0de857f7e3745db8035bd67d2bc0f6b2025-02-03T08:47:22ZengCABICABI Agriculture and Bioscience2662-40442023-11-014112010.1186/s43170-023-00193-zMapping fruit tree dynamics using phenological metrics from optimal Sentinel-2 data and Deep Neural NetworkYingisani Chabalala0Elhadi Adam1Mahlatse Kganyago2Faculty of Science, School of Geography, Archaeology and Environmental Studies, University of the WitwatersrandFaculty of Science, School of Geography, Archaeology and Environmental Studies, University of the WitwatersrandDepartment of Geography, Environmental Management, and Energy Studies, University of JohannesburgAbstract Accurate and up-to-date crop-type maps are essential for efficient management and well-informed decision-making, allowing accurate planning and execution of agricultural operations in the horticultural sector. The assessment of crop-related traits, such as the spatiotemporal variability of phenology, can improve decision-making. The study aimed to extract phenological information from Sentinel-2 data to identify and distinguish between fruit trees and co-existing land use types on subtropical farms in Levubu, South Africa. However, the heterogeneity and complexity of the study area—composed of smallholder mixed cropping systems with overlapping spectra—constituted an obstacle to the application of optical pixel-based classification using machine learning (ML) classifiers. Given the socio-economic importance of fruit tree crops, the research sought to map the phenological dynamics of these crops using deep neural network (DNN) and optical Sentinel-2 data. The models were optimized to determine the best hyperparameters to achieve the best classification results. The classification results showed the maximum overall accuracies of 86.96%, 88.64%, 86.76%, and 87.25% for the April, May, June, and July images, respectively. The results demonstrate the potential of temporal phenological optical-based data in mapping fruit tree crops under different management systems. The availability of remotely sensed data with high spatial and spectral resolutions makes it possible to use deep learning models to support decision-making in agriculture. This creates new possibilities for deep learning to revolutionize and facilitate innovation within smart horticulture.https://doi.org/10.1186/s43170-023-00193-zClassificationDeep neural networkPhenologyDynamicsSentinel-2 |
| spellingShingle | Yingisani Chabalala Elhadi Adam Mahlatse Kganyago Mapping fruit tree dynamics using phenological metrics from optimal Sentinel-2 data and Deep Neural Network CABI Agriculture and Bioscience Classification Deep neural network Phenology Dynamics Sentinel-2 |
| title | Mapping fruit tree dynamics using phenological metrics from optimal Sentinel-2 data and Deep Neural Network |
| title_full | Mapping fruit tree dynamics using phenological metrics from optimal Sentinel-2 data and Deep Neural Network |
| title_fullStr | Mapping fruit tree dynamics using phenological metrics from optimal Sentinel-2 data and Deep Neural Network |
| title_full_unstemmed | Mapping fruit tree dynamics using phenological metrics from optimal Sentinel-2 data and Deep Neural Network |
| title_short | Mapping fruit tree dynamics using phenological metrics from optimal Sentinel-2 data and Deep Neural Network |
| title_sort | mapping fruit tree dynamics using phenological metrics from optimal sentinel 2 data and deep neural network |
| topic | Classification Deep neural network Phenology Dynamics Sentinel-2 |
| url | https://doi.org/10.1186/s43170-023-00193-z |
| work_keys_str_mv | AT yingisanichabalala mappingfruittreedynamicsusingphenologicalmetricsfromoptimalsentinel2dataanddeepneuralnetwork AT elhadiadam mappingfruittreedynamicsusingphenologicalmetricsfromoptimalsentinel2dataanddeepneuralnetwork AT mahlatsekganyago mappingfruittreedynamicsusingphenologicalmetricsfromoptimalsentinel2dataanddeepneuralnetwork |