Forecasting cropping patterns to increase crop yields food and horticulture using a machine approach learning
Nearly 82% of rural areas still rely on the agricultural sector, one of which is the cultivation of food crops and horticulture. Changing climatic conditions are one of the causes of farmers’ failure to predict the selection of the right time for the cultivation process. This work predicts the selec...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2024/67/bioconf_icobeaf2024_03002.pdf |
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| _version_ | 1841526773848735744 |
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| author | Sanjaya Imam Mantoro Teddy Asian Jelita Kharisma Ivana Lucia Thohir Muhammad Ikhsan |
| author_facet | Sanjaya Imam Mantoro Teddy Asian Jelita Kharisma Ivana Lucia Thohir Muhammad Ikhsan |
| author_sort | Sanjaya Imam |
| collection | DOAJ |
| description | Nearly 82% of rural areas still rely on the agricultural sector, one of which is the cultivation of food crops and horticulture. Changing climatic conditions are one of the causes of farmers’ failure to predict the selection of the right time for the cultivation process. This work predicts the selection of the right time for the cultivation of certain crops in order to optimize crop yields. Forecasting uses several machine learning methods by comparing the best results. The results showed that machine learning could produce good information at the right time and on certain types of plants to be cultivated in an area. Predictive recommendations for planting corn in 2022 are optimum planting in January to March 2022 and in August to December 2022. The optimum fertilizer application is N fertilizer dose = 100-270 kg/ha, P fertilizer dose = 63-100 kg/ha, and K fertilizer dose = 156-200 kg/ha. |
| format | Article |
| id | doaj-art-fdf90be7c6e14fcdbda20bb6bb3bc8ce |
| institution | Kabale University |
| issn | 2117-4458 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | BIO Web of Conferences |
| spelling | doaj-art-fdf90be7c6e14fcdbda20bb6bb3bc8ce2025-01-16T11:19:46ZengEDP SciencesBIO Web of Conferences2117-44582024-01-011480300210.1051/bioconf/202414803002bioconf_icobeaf2024_03002Forecasting cropping patterns to increase crop yields food and horticulture using a machine approach learningSanjaya Imam0Mantoro Teddy1Asian Jelita2Kharisma Ivana Lucia3Thohir Muhammad Ikhsan4Departement of Informatic Engineering, Nusa Putra UniversityDepartement of Informatic Engineering, Nusa Putra UniversityDepartement of Informatic Engineering, Nusa Putra UniversityDepartement of Informatic Engineering, Nusa Putra UniversityDepartement of Informatic Engineering, Nusa Putra UniversityNearly 82% of rural areas still rely on the agricultural sector, one of which is the cultivation of food crops and horticulture. Changing climatic conditions are one of the causes of farmers’ failure to predict the selection of the right time for the cultivation process. This work predicts the selection of the right time for the cultivation of certain crops in order to optimize crop yields. Forecasting uses several machine learning methods by comparing the best results. The results showed that machine learning could produce good information at the right time and on certain types of plants to be cultivated in an area. Predictive recommendations for planting corn in 2022 are optimum planting in January to March 2022 and in August to December 2022. The optimum fertilizer application is N fertilizer dose = 100-270 kg/ha, P fertilizer dose = 63-100 kg/ha, and K fertilizer dose = 156-200 kg/ha.https://www.bio-conferences.org/articles/bioconf/pdf/2024/67/bioconf_icobeaf2024_03002.pdf |
| spellingShingle | Sanjaya Imam Mantoro Teddy Asian Jelita Kharisma Ivana Lucia Thohir Muhammad Ikhsan Forecasting cropping patterns to increase crop yields food and horticulture using a machine approach learning BIO Web of Conferences |
| title | Forecasting cropping patterns to increase crop yields food and horticulture using a machine approach learning |
| title_full | Forecasting cropping patterns to increase crop yields food and horticulture using a machine approach learning |
| title_fullStr | Forecasting cropping patterns to increase crop yields food and horticulture using a machine approach learning |
| title_full_unstemmed | Forecasting cropping patterns to increase crop yields food and horticulture using a machine approach learning |
| title_short | Forecasting cropping patterns to increase crop yields food and horticulture using a machine approach learning |
| title_sort | forecasting cropping patterns to increase crop yields food and horticulture using a machine approach learning |
| url | https://www.bio-conferences.org/articles/bioconf/pdf/2024/67/bioconf_icobeaf2024_03002.pdf |
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