Prospects of artificial intelligence for the sustainability of sugarcane production in the modern era of climate change: An overview of related global findings
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| Název: | Prospects of artificial intelligence for the sustainability of sugarcane production in the modern era of climate change: An overview of related global findings |
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| Autoři: | Rajan Bhatt, Akbar Hossain, Debjyoti Majumder, Mandapelli Sharath Chandra, Rajiv Ghimire, Muhammad Faisal Shahzad, Krishan K. Verma, Amarinder Singh Riar, Vishnu D. Rajput, Mauro Wagner Oliveira, Adel Nisi, Riyadh S. Almalki, Viliam Bárek, Marian Brestic, Sagar Maitra |
| Zdroj: | Journal of Agriculture and Food Research, Vol 18, Iss , Pp 101519- (2024) |
| Informace o vydavateli: | Elsevier, 2024. |
| Rok vydání: | 2024 |
| Sbírka: | LCC:Agriculture (General) LCC:Nutrition. Foods and food supply |
| Témata: | Artificial intelligence, Machine learning, Greenhouse emissions, Water efficiency, Soil organic stocks, Sugarcane agriculture, Agriculture (General), S1-972, Nutrition. Foods and food supply, TX341-641 |
| Popis: | By analysing biochemical composition, assessing soil quality, projecting yields, predicting productivity, identifying illnesses, and predicting productivity, artificial intelligence (AI) has greatly improved sugarcane cultivation. This study discusses the latest research on artificial intelligence (AI) in sugarcane farming, with particular attention given to soil biochemistry, disease detection, climate-smart technology for greenhouse gas emissions, yield and water productivity prediction, and cane juice biochemistry. Artificial intelligence (AI) tools, such as machine learning algorithms, can optimize irrigation, increase yields, save water, and properly estimate sugarcane production. Because of the effectiveness, affordability, and efficiency of AI, it is essential in sugarcane agriculture for accurate yield forecasting as well as for streamlining resource allocation and crop management. AI facilitates prompt interventions by assisting in early disease identification and production prediction. AI can also forecast soil organic carbon (SOC) levels, which can help guide sustainability and soil health initiatives. The comprehensive global review identifies research gaps in the literature and suggests potential avenues and directions for future research. |
| Druh dokumentu: | article |
| Popis souboru: | electronic resource |
| Jazyk: | English |
| ISSN: | 2666-1543 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2666154324005568; https://doaj.org/toc/2666-1543 |
| DOI: | 10.1016/j.jafr.2024.101519 |
| Přístupová URL adresa: | https://doaj.org/article/f66c66c28fd2446c93ea20a913e8b5c7 |
| Přístupové číslo: | edsdoj.f66c66c28fd2446c93ea20a913e8b5c7 |
| Databáze: | Directory of Open Access Journals |
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Nájsť tento článok vo Web of Science | Abstrakt: | By analysing biochemical composition, assessing soil quality, projecting yields, predicting productivity, identifying illnesses, and predicting productivity, artificial intelligence (AI) has greatly improved sugarcane cultivation. This study discusses the latest research on artificial intelligence (AI) in sugarcane farming, with particular attention given to soil biochemistry, disease detection, climate-smart technology for greenhouse gas emissions, yield and water productivity prediction, and cane juice biochemistry. Artificial intelligence (AI) tools, such as machine learning algorithms, can optimize irrigation, increase yields, save water, and properly estimate sugarcane production. Because of the effectiveness, affordability, and efficiency of AI, it is essential in sugarcane agriculture for accurate yield forecasting as well as for streamlining resource allocation and crop management. AI facilitates prompt interventions by assisting in early disease identification and production prediction. AI can also forecast soil organic carbon (SOC) levels, which can help guide sustainability and soil health initiatives. The comprehensive global review identifies research gaps in the literature and suggests potential avenues and directions for future research. |
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| ISSN: | 26661543 |
| DOI: | 10.1016/j.jafr.2024.101519 |