Machine Learning for Dynamic Management Zone in Smart Farming
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914897294524416 |
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| author | Kulatunga, Chamil Dhelim, Sahraoui Kechadi, Tahar |
| author_facet | Kulatunga, Chamil Dhelim, Sahraoui Kechadi, Tahar |
| contents | Digital agriculture is growing in popularity among professionals and brings together new opportunities along with pervasive use of modern data-driven technologies. Digital agriculture approaches can be used to replace all traditional agricultural system at very reasonable costs. It is very effective in optimising large-scale management of resources, while traditional techniques cannot even tackle the problem. In this paper, we proposed a dynamic management zone delineation approach based on Machine Learning clustering algorithms using crop yield data, elevation and soil texture maps and available NDVI data. Our proposed dynamic management zone delineation approach is useful for analysing the spatial variation of yield zones. Delineation of yield regions based on historical yield data augmented with topography and soil physical properties helps farmers to economically and sustainably deploy site-specific management practices identifying persistent issues in a field. The use of frequency maps is capable of capturing dynamically changing incidental issues within a growing season. The proposed zone management approach can help farmers/agronomists to apply variable-rate N fertilisation more effectively by analysing yield potential and stability zones with satellite-based NDVI monitoring. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00789 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning for Dynamic Management Zone in Smart Farming Kulatunga, Chamil Dhelim, Sahraoui Kechadi, Tahar Computers and Society Artificial Intelligence Digital agriculture is growing in popularity among professionals and brings together new opportunities along with pervasive use of modern data-driven technologies. Digital agriculture approaches can be used to replace all traditional agricultural system at very reasonable costs. It is very effective in optimising large-scale management of resources, while traditional techniques cannot even tackle the problem. In this paper, we proposed a dynamic management zone delineation approach based on Machine Learning clustering algorithms using crop yield data, elevation and soil texture maps and available NDVI data. Our proposed dynamic management zone delineation approach is useful for analysing the spatial variation of yield zones. Delineation of yield regions based on historical yield data augmented with topography and soil physical properties helps farmers to economically and sustainably deploy site-specific management practices identifying persistent issues in a field. The use of frequency maps is capable of capturing dynamically changing incidental issues within a growing season. The proposed zone management approach can help farmers/agronomists to apply variable-rate N fertilisation more effectively by analysing yield potential and stability zones with satellite-based NDVI monitoring. |
| title | Machine Learning for Dynamic Management Zone in Smart Farming |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2408.00789 |