The integration of remote sensing and machine learning in agriculture is revolutionizing the industry by providing valuable insights and predictions through data analysis. This combination of technologies leads to improved yield prediction and water management, resulting in increased efficiency, better yields, and more sustainable agricultural practices. To achieve the United Nations' Sustainable Development Goals, particularly "zero hunger," it is crucial to investigate crop yield and precipitation gaps. This can be accomplished through the utilization of artificial intelligence (AI), machine learning (ML), remote sensing (RS), and the internet of things (IoT). By integrating these technologies, a robust agricultural mobile or web application can be developed, providing farmers and decision-makers with essential information and tools for enhancing crop management and increasing efficiency. Numerous studies have explored the potential of these new technologies for various tasks such as crop monitoring, yield prediction, irrigation management, etc. This paper critically reviews relevant articles that have utilized RS, ML, cloud computing, and IoT in crop yield prediction. It evaluates different machine-learning approaches proposed in the literature for crop yield prediction and water management to provide insights into how these methods can improve decision-making in agricultural production systems. The aim of this work is to serve as a compendium for those interested in yield prediction by providing an overview of primary literature and highlighting approaches that can be used for real-time and robust prediction. With the world population projected to reach 9.7 billion by 2050, there is an urgent need to increase food production by 70%. The agricultural sector faces challenges such as climate change, labor force decline, arable land scarcity, soil degradation, and achieving food security. In response to these challenges digital technologies like AI ML RS ,and IoT are driving innovation in agriculture towards Climate Smart Agriculture (CSA) practices. These solutions help bridge the gap between supply and demand by improving yields ,profitability ,and sustainability .To meet these challenges effectively ,Agriculture 4 .0 which involves adopting internet connectivity solutions in agricultural practices has become crucial .Internet connectivity enables automation reduces manual labor .Additionally advancements in RS ,ML ,and crop model simulation have emerged to predict yield more accurately by analyzing massive amounts of data using improved scalability computing power .The recommended approach for crop yield prediction is to combine historical weather data with Sentinel 2 satellite data vegetation indices (VI) .This method offers advantages temporal spatial resolution allowing better observation water resources crop growth vegetation indices estimation crop yields .
- Integration of remote sensing and machine learning in agriculture is revolutionizing the industry
- Improved yield prediction and water management
- Increased efficiency, better yields, and more sustainable practices
- Utilization of AI, ML, RS, and IoT to achieve Sustainable Development Goals
- Development of agricultural mobile or web application for crop management
- Potential of these technologies for tasks such as crop monitoring, yield prediction, irrigation management
- Critical review of articles on RS, ML, cloud computing, and IoT in crop yield prediction
- Aim to provide overview of primary literature and highlight approaches for real-time prediction
- Urgent need to increase food production by 70% by 2050
- Challenges faced by agricultural sector: climate change, labor force decline, arable land scarcity, soil degradation
- Digital technologies driving innovation towards Climate Smart Agriculture practices
- Internet connectivity crucial in Agriculture 4.0 for automation and reducing manual labor
- Advancements in RS, ML, and crop model simulation for accurate yield prediction using data analysis
- Recommended approach: combine historical weather data with Sentinel 2 satellite data vegetation indices (VI)
1. Remote sensing and machine learning are being used in agriculture to make it better.
2. Farmers can predict how much they will grow and use water more efficiently.
3. Using these technologies helps farmers be more efficient, get better crops, and take care of the environment.
4. Scientists are using AI, ML, RS, and IoT to achieve goals for sustainable development.
5. People are making apps to help farmers manage their crops.
Definitions- Remote sensing: Using special tools to collect information about things from far away.
- Machine learning: Teaching computers how to learn and make decisions on their own.
- Agriculture: Growing plants and raising animals for food or other products.
- Sustainable Development Goals: Plans to make the world a better place by protecting the environment and helping people have good lives.
- IoT (Internet of Things): Connecting different devices together through the internet so they can work together.
Revolutionizing Agriculture with Remote Sensing and Machine Learning
The agricultural industry is being revolutionized by the integration of remote sensing (RS) and machine learning (ML). This combination of technologies leads to improved yield prediction and water management, resulting in increased efficiency, better yields, and more sustainable agricultural practices. To achieve the United Nations' Sustainable Development Goals, particularly "zero hunger," it is crucial to investigate crop yield and precipitation gaps. This can be accomplished through the utilization of artificial intelligence (AI), ML, RS, and the internet of things (IoT). By integrating these technologies, a robust agricultural mobile or web application can be developed that provides farmers and decision-makers with essential information for enhancing crop management.
Climate Smart Agriculture Practices
With the world population projected to reach 9.7 billion by 2050, there is an urgent need to increase food production by 70%. The agricultural sector faces challenges such as climate change, labor force decline, arable land scarcity, soil degradation, and achieving food security. In response to these challenges digital technologies like AI ML RS ,and IoT are driving innovation in agriculture towards Climate Smart Agriculture (CSA) practices. These solutions help bridge the gap between supply and demand by improving yields ,profitability ,and sustainability .To meet these challenges effectively ,Agriculture 4 .0 which involves adopting internet connectivity solutions in agricultural practices has become crucial .Internet connectivity enables automation reduces manual labor .Additionally advancements in RS ,ML ,and crop model simulation have emerged to predict yield more accurately by analyzing massive amounts of data using improved scalability computing power .
Integrating Technologies for Improved Yield Prediction
Numerous studies have explored the potential of these new technologies for various tasks such as crop monitoring, yield prediction irrigation management etc..This paper critically reviews relevant articles that have utilized RS ML cloud computing IoT in crop yield prediction It evaluates different machine-learning approaches proposed in literature for crop yield prediction water management provide insights into how methods can improve decision-making in agricultural production systems The aim this work serve compendium those interested yield prediction providing overview primary literature highlighting approaches used real-time robust prediction
Recommended Approach for Crop Yield Prediction
The recommended approach for crop yield prediction is to combine historical weather data with Sentinel 2 satellite data vegetation indices (VI). This method offers advantages temporal spatial resolution allowing better observation water resources crop growth vegetation indices estimation crop yields With this approach farmers able make informed decisions regarding their crops ensure maximum productivity while minimizing environmental impact
Conclusion
In conclusion combining remote sensing machine learning enable accurate predictions regarding crops’ performance provide valuable insights into farming operations allow farmers take advantage latest technology maximize their profits minimize environmental impact Ultimately this could lead greater food security world over meeting UN Sustainable Development Goals zero hunger
