This study explores the integration of artificial intelligence (AI) in American agriculture to enhance climate resilience, focusing on three critical areas: Precision farming, crop yield prediction, resource optimization and control of pests. The study adopts machine learning techniques, IoT real-time monitoring, and satellite data to compare the performance of AI interventions in addressing climate change risks. This paper establishes that AI leads to an increase in crop yields by 28.6%, a decrease in water use by 40% and a decrease in pesticide use by 30%, proving the capacity of AI in productivity and sustainability. These findings are statistically significant, as revealed by correlational coefficients showing the extent of associations between AI applications and agriculture. This study’s contributions to knowledge and practice for policymakers, agribusinesses, and researchers are valuable despite the barriers, including high implementation costs and poor rural infrastructure. If these barriers are well addressed, then AI offers a perfect solution to developing a robust agriculture sector that can cope with climate volatility.
Inhaltsverzeichnis (Table of Contents)
- Abstract
- Introduction
- Proposed Methodology
- Evaluation
- Statistical Analysis
- Conclusion
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This study investigates the application of artificial intelligence (AI) in American agriculture to enhance climate resilience. The primary objective is to assess the effectiveness of AI interventions in mitigating climate change risks and improving agricultural sustainability. The research employs machine learning techniques, IoT monitoring, and satellite data to analyze AI's impact on crop yields, water usage, and pesticide application.
- The impact of AI on climate resilience in American agriculture.
- The application of AI technologies (machine learning, IoT, satellite data) in precision farming.
- The optimization of resource use (water, pesticides) through AI-driven interventions.
- The challenges and limitations of AI adoption in agriculture (cost, infrastructure, technical knowledge).
- The potential of AI to enhance productivity and sustainability in American agriculture.
Zusammenfassung der Kapitel (Chapter Summaries)
Abstract: This study explores the integration of artificial intelligence (AI) in American agriculture to enhance climate resilience, focusing on precision farming, crop yield prediction, resource optimization, and pest control. Using machine learning, IoT, and satellite data, the research demonstrates that AI significantly increases crop yields (28.6%), decreases water use (40%), and reduces pesticide use (30%). While acknowledging barriers like high implementation costs and poor rural infrastructure, the study highlights AI's potential for creating a robust and climate-resilient agricultural sector.
Introduction: This chapter establishes the context for the study by highlighting the vulnerability of American agriculture to climate change impacts such as increased temperatures, altered precipitation patterns, and extreme weather events. It emphasizes the need for climate-resilient agricultural practices and introduces AI as a transformative technology capable of improving climate outcomes and enhancing agricultural sustainability. The chapter defines agricultural resilience and lays the groundwork for exploring AI's potential role in achieving it.
Proposed Methodology: [Note: Since the provided text lacks details on the methodology, a synthetic summary is needed. This section would detail the specific AI techniques employed (e.g., types of machine learning models, data sources, data preprocessing steps), the experimental design (e.g., control groups, treatment groups, data collection methods), and the analytical approach (e.g., statistical methods used to analyze the data and assess the impact of AI interventions).]
Evaluation: [Note: A synthetic summary is required here as well. This section would describe the evaluation metrics used to assess the effectiveness of AI interventions, the results obtained from the analysis of the collected data, and a comparison of the performance of AI-based solutions against traditional farming methods. This would include a discussion of the key findings related to crop yields, resource use, and pest control.]
Statistical Analysis: [Note: This section requires a synthetic summary. It would detail the statistical methods used to analyze the collected data and assess the significance of the findings. This would include a discussion of the statistical tests employed, the results obtained, and the interpretation of the statistical findings in the context of the research objectives.]
Schlüsselwörter (Keywords)
Artificial Intelligence, Climate Resilience, Precision Agriculture, Resource Optimization, Sustainable Farming Practices, Machine Learning, IoT, Satellite Data, Crop Yield Prediction, Pest Control, Water Management.
Frequently asked questions about the Language Preview Text
What is the main focus of the study described in the Language Preview?
The study investigates the application of artificial intelligence (AI) in American agriculture to enhance climate resilience. It assesses the effectiveness of AI interventions in mitigating climate change risks and improving agricultural sustainability.
What are the key objectives of the study?
The primary objective is to assess the effectiveness of AI interventions in mitigating climate change risks and improving agricultural sustainability. The research employs machine learning techniques, IoT monitoring, and satellite data to analyze AI's impact on crop yields, water usage, and pesticide application.
What are the main themes explored in the study?
The key themes include: the impact of AI on climate resilience in American agriculture; the application of AI technologies in precision farming; the optimization of resource use through AI-driven interventions; the challenges and limitations of AI adoption in agriculture; and the potential of AI to enhance productivity and sustainability in American agriculture.
What AI technologies are mentioned in the study?
The study mentions machine learning, IoT (Internet of Things) monitoring, and satellite data as key AI technologies used in precision farming.
What specific areas of agriculture does the study focus on?
The study focuses on precision farming, crop yield prediction, resource optimization (water and pesticides), and pest control in the context of climate resilience.
What are some of the potential benefits of AI in agriculture, according to the study?
AI has the potential to increase crop yields, decrease water usage, reduce pesticide use, and enhance productivity and sustainability in American agriculture.
What are some of the challenges to AI adoption in agriculture?
The study acknowledges barriers such as high implementation costs, poor rural infrastructure, and the need for technical knowledge.
What are some of the keywords associated with this study?
The keywords include: Artificial Intelligence, Climate Resilience, Precision Agriculture, Resource Optimization, Sustainable Farming Practices, Machine Learning, IoT, Satellite Data, Crop Yield Prediction, Pest Control, and Water Management.
What does the Abstract state about the impact of AI?
The Abstract states that AI significantly increases crop yields (28.6%), decreases water use (40%), and reduces pesticide use (30%).
- Citar trabajo
- Ruth Nyads (Autor), 2024, Harnessing Artificial Intelligence for Climate Resilience in American Agriculture, Múnich, GRIN Verlag, https://www.grin.com/document/1555533
