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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2117-4458 |