Agricultural conversion is a major reason for deforestation that affects the United States and is responsible for the loss of species, soil depletion and global warming. This work aims to analyze the use of AI for combating deforestation in the agricultural sector in the United States through improved surveillance, risk assessments, and policy modeling. This proposed framework combines satellite imagery data, agricultural records, and selected socio-economic factors and uses CNNs, GBMs, and ABMs to tackle deforestation systematically. CNNs also showed an accuracy of 94% in the identification of the area of deforestation, while the GBMs showed an accuracy of 0.92 AUC-ROC in identifying hotspot areas. Through ABMs that assumed policy changes such as reforestation incentives and fines for violators, the study showed that deforestation rates could be cut by up to 25%. Regression and correlation analyses and hypothesis testing proved significant predictors such as crop yield, rainfall variability and the superiority of the models to conventional techniques. The outcomes reveal that AI can offer an effective solution to increase food production and maintain forests at the same time. This framework allows for the formulation of specific recommendations for policy initiatives because it incorporates empirical evidence. Further research should improve the modularity, the real-time monitoring system and the access to the algorithm to further increase the impact of AI on sustainable land management and the chopping down of forests.
Inhaltsverzeichnis (Table of Contents)
- I. INTRODUCTION
- II. RELATED WORKS
- III. PROPOSED METHODOLOGY
- IV. EVALUATION
- V. STATISTICAL ANALYSIS
- VI. CONCLUSION
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This work aims to analyze the use of Artificial Intelligence (AI) in combating deforestation in the U.S. agricultural sector. It explores improved surveillance, risk assessments, and policy modeling using a framework combining satellite imagery, agricultural records, and socio-economic factors. The study investigates the effectiveness of AI techniques like Convolutional Neural Networks (CNNs), Gradient Boosting Machines (GBMs), and Agent-Based Models (ABMs) in addressing deforestation.
- Combating deforestation in U.S. agriculture through AI-driven solutions.
- Utilizing AI for improved surveillance and risk assessment of deforestation.
- Developing policy models based on AI analysis to mitigate deforestation.
- Evaluating the accuracy and effectiveness of AI models in identifying deforestation hotspots and predicting deforestation rates.
- Assessing the potential of AI to increase food production while preserving forests.
Zusammenfassung der Kapitel (Chapter Summaries)
I. INTRODUCTION: This chapter introduces the problem of deforestation in U.S. agriculture, highlighting its impact on biodiversity, water security, and carbon emissions. It emphasizes the conflict between agricultural production and environmental protection and sets the stage for exploring AI-based solutions to mitigate deforestation while maintaining agricultural yields. The chapter details the extent of deforestation caused by large-scale monoculture farming, citing statistics and research on the loss of tree cover and its consequences, including soil degradation and increased greenhouse gas emissions. It also discusses the limitations of traditional methods in combating deforestation, such as time lags in data acquisition, scalability issues with field surveys, and underutilization of satellite imagery due to a lack of advanced analytical tools. The chapter concludes by introducing AI as a transformative tool capable of addressing these limitations and revolutionizing deforestation control efforts.
II. RELATED WORKS: (This section's content needs to be added based on the missing "Related Works" chapter from the provided text.)
III. PROPOSED METHODOLOGY: (This section's content needs to be added based on the missing "Proposed Methodology" chapter from the provided text.)
IV. EVALUATION: (This section's content needs to be added based on the missing "Evaluation" chapter from the provided text.)
V. STATISTICAL ANALYSIS: (This section's content needs to be added based on the missing "Statistical Analysis" chapter from the provided text.)
Schlüsselwörter (Keywords)
Artificial Intelligence (AI), Deforestation, U.S. Agriculture, Satellite Imagery, Convolutional Neural Networks (CNNs), Gradient Boosting Machines (GBMs), Agent-Based Models (ABMs), Sustainable Land Management, Policy Modeling, Risk Assessment, Food Security, Environmental Protection.
Frequently asked questions
What is the main topic of this academic text?
This academic text focuses on the use of Artificial Intelligence (AI) in combating deforestation within the U.S. agricultural sector.
What are the key themes explored in the text?
The key themes include using AI for improved surveillance, risk assessment, and policy modeling related to deforestation. It also investigates the effectiveness of AI techniques like Convolutional Neural Networks (CNNs), Gradient Boosting Machines (GBMs), and Agent-Based Models (ABMs).
What AI techniques are mentioned as potential solutions for deforestation?
The text mentions Convolutional Neural Networks (CNNs), Gradient Boosting Machines (GBMs), and Agent-Based Models (ABMs) as AI techniques to be explored.
What data sources are used in the proposed methodology?
The proposed methodology uses a framework combining satellite imagery, agricultural records, and socio-economic factors.
What is the conflict that this study addresses?
The study addresses the conflict between agricultural production and environmental protection, specifically deforestation.
What are some consequences of deforestation mentioned in the text?
The text mentions consequences such as biodiversity loss, water insecurity, increased carbon emissions, and soil degradation.
What limitations of traditional methods in combating deforestation are highlighted?
The text highlights limitations such as time lags in data acquisition, scalability issues with field surveys, and underutilization of satellite imagery due to a lack of advanced analytical tools.
What are some of the keywords associated with this text?
Keywords include Artificial Intelligence (AI), Deforestation, U.S. Agriculture, Satellite Imagery, Convolutional Neural Networks (CNNs), Gradient Boosting Machines (GBMs), Agent-Based Models (ABMs), Sustainable Land Management, Policy Modeling, Risk Assessment, Food Security, and Environmental Protection.
What objectives does the study aim to achieve?
The study aims to:
- Combat deforestation in U.S. agriculture through AI-driven solutions.
- Utilize AI for improved surveillance and risk assessment of deforestation.
- Develop policy models based on AI analysis to mitigate deforestation.
- Evaluate the accuracy and effectiveness of AI models in identifying deforestation hotspots and predicting deforestation rates.
- Assess the potential of AI to increase food production while preserving forests.
What topics were intended to be discussed in the 'Related Works,' 'Proposed Methodology,' 'Evaluation' and 'Statistical Analysis' chapters?
The 'Related Works' chapter was intended to provide context by reviewing existing literature on deforestation and AI applications in related fields. The 'Proposed Methodology' chapter was intended to describe the specific AI techniques and data analysis methods used in the study. The 'Evaluation' chapter would have detailed the process and metrics used to assess the performance of the AI models. The 'Statistical Analysis' chapter was planned to present the quantitative results and statistical significance of the findings.
- Quote paper
- Ruth Nyads (Author), 2024, Harnessing Artificial Intelligence (AI) to Combat Deforestation in U.S. Agriculture, Munich, GRIN Verlag, https://www.grin.com/document/1555530
