Recent trends in the field of artificial intelligence in modern agriculture | ||
| International Journal of Family Studies, Food Science and Nutrition Health | ||
| Volume 4, Issue 1, June 2023, Pages 15-36 PDF (600.87 K) | ||
| Document Type: Original Research | ||
| DOI: 10.21608/ijfsnh.2024.293391.1007 | ||
| Author | ||
| Abdallah Bin Abdel Aziz El Qarawy* | ||
| Assistant Professor-Faculty of agriculture –King Saud University | ||
| Abstract | ||
| Enhancing Crop Productivity: Artificial intelligence is used to analyze agricultural data, such as weather and soil data, to improve crop productivity. Machine learning techniques enable farmers to make more precise decisions regarding farming and irrigation. Precision Farming: Artificial intelligence is utilized to analyze aerial imagery and remote sensing data to understand the needs of each part of the agricultural field. This enables farmers to improve resource utilization and reduce losses. Agricultural Production Predictions: Using machine learning techniques, agricultural production predictions can be enhanced based on a wide range of factors such as weather conditions and diseases. Smart Animal Care: Artificial intelligence is employed to monitor the health and behavior of animals on farms, helping improve their care and increase meat and dairy production. Risk Management and Decision Making: Artificial intelligence assists in analyzing potential risks and provides recommendations for strategic decision-making, improving farm management to face potential challenges. Smart Water Management in Agriculture: Artificial intelligence is used to analyze water consumption and improve irrigation techniques, contributing to water conservation and enhancing irrigation efficiency. Integration of Technologies: The use of environmental sensing technologies and the Internet of Things (IoT) is increasing in modern agriculture, integrating them with artificial intelligence for more accurate data and better understanding of agricultural conditions. | ||
| Keywords | ||
| Artificial Intelligence; Precision Farming; Machine Learning; Crop Productivity; Agricultural Data | ||
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