Prospects of artificial intelligence for the sustainability of sugarcane production in the modern era of climate change: An overview of related global findings
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.
Authors
- Sagar Maitra (ORCID: https://orcid.org/0000-0001-8210-1531)
- Riyadh S. Almalki (ORCID: https://orcid.org/0009-0001-1803-1812)
- Rajiv Ghimire (ORCID: https://orcid.org/0000-0003-1962-1803)
- Adel Nisi
- Marián Brestič (ORCID: https://orcid.org/0000-0003-3470-6100)
- Viliam Bárek (ORCID: https://orcid.org/0000-0001-8131-7517)
- M. Sharath Chandra
- Akbar Hossain (ORCID: https://orcid.org/0000-0003-0264-2712)
- Mauro Wagner de Oliveira (ORCID: https://orcid.org/0000-0002-3837-429X)
- Muhammad Faisal Shahzad (ORCID: https://orcid.org/0000-0003-3855-6859)
- Krishan K. Verma (ORCID: https://orcid.org/0000-0002-5501-7905)
- Rajan Bhatt (ORCID: https://orcid.org/0000-0001-9977-9955)
- Debjyoti Majumder (ORCID: https://orcid.org/0000-0002-9387-9100)
- Vishnu D. Rajput (ORCID: https://orcid.org/0000-0002-6802-4805)
- Amarinder Singh (ORCID: https://orcid.org/0000-0002-6887-4105)
Institutions
- Slovak University of Agriculture in Nitra (SK)
- Southern Federal University (RU)
- Guru Nanak Dev University (IN)
- Abdul Wali Khan University Mardan (PK)
- Shahid Chamran University of Ahvaz (IR)
- Umm al-Qura University (SA)
- University of Michigan (US)
- Bangladesh Wheat and Maize Research Institute (BD)
- Professor Jayashankar Telangana Agricultural University (IN)
- Krishi Vigyan Kendra, Ghatkhed Amravati (IN)
- North Bengal Agricultural University (IN)
- Centurion University of Technology and Management (IN)
- Shandong Agricultural University (CN)
- Universidade Federal de Alagoas (BR)
Publication Details
- Journal
- Journal of Agriculture and Food Research
- Published
- 2024-11-15
- DOI
- https://doi.org/10.1016/j.jafr.2024.101519
- Citations
- 6
- Primary Topic
- Sugarcane Cultivation and Processing
- Type
- article
- Field-Weighted Citation Impact
- 3.60