Benefits of the Integrated Sensing System

Oct 13, 2025 by magicalceylontours.com 57 views

# Integrated Sensing System for Monitoring Planting Land Conditions and Scheduling Plant Scheduling Using the Long Short-Term Memory Algorithm

In the current era of technological advances, farmers can take advantage of technology as a tool in various aspects of agriculture. One promising innovation is the use of agricultural robots designed to help farmers in caring for plants. This agricultural robot can monitor the condition of planting land in real-time and perform various automatic tasks, including watering plants. However, so that this system is more efficient, optimization is needed, especially in scheduling and restrictions on robot work time for routine activities such as watering.

The Importance of Integrated Sensing Systems in Agriculture

To achieve this efficiency, the authors designed an integrated sensing system that serves to monitor the condition of the planting land while automating the watering schedule. This system utilizes Machine Learning technology using the Long Short-Term Memory (LSTM) algorithm to predict the level of soil humidity. This prediction model also uses the estimated environmental conditions, taken from the weather data provider and the Application Programming Interface (API) Geoloting as input for predictions.

The Role of Machine Learning in Agricultural Technology

The data used in this model consists of soil sensing results, which include the value of humidity and soil temperature, air temperature, and time, which is collected for three months. The data is then trained using the LSTM algorithm with the hyperparameter settings, which is 416 layers, learning rates 0.001, and the ratio of training and testing data sharing of 70:30. The prediction results, in the form of soil humidity, will be a reference in determining the watering schedule.

The Accuracy of the Prediction Model

The average deviation of the prediction of soil humidity ranges from 5.13% to 5.60%, indicating a fairly good level of accuracy. The final result of this study is the realization of agricultural robots that are equipped with integrated sensing systems and automatic watering scheduling systems that can be monitored through a web-based dashboard application.

Benefits of the Integrated Sensing System

The application of this integrated sensing system brings many benefits to the agricultural sector. By using the LSTM algorithm, the system can predict soil humidity more accurately based on historical data and current environmental conditions. This allows farmers to optimize water use, which is one of the important resources in agriculture. In addition, reducing watering frequency based on the actual data of soil humidity not only saves costs but also supports sustainable agricultural practices.

The Importance of Technological Integration in Agriculture

On the other hand, the importance of technological integration in agriculture cannot be ignored. Agricultural robots that are equipped with integrated sensing capabilities not only increase work efficiency but also reduce the physical workload of farmers. Thus, farmers can focus more on other strategic aspects of their agricultural business, such as the selection of plant varieties and product marketing.

Web-Based Dashboard Applications for Farmers

Web-based dashboard applications that are developed provide convenience for farmers to monitor land conditions in real-time. The data displayed can help in making decisions faster and more precisely. With advances in developing technological, system integration is the key to increasing productivity and sustainability in the agricultural sector.

Conclusion and Future Directions

Overall, this research not only shows the potential use of LSTM algorithms in agriculture but also opens opportunities for the development of more innovative and efficient agricultural technology in the future. The integrated sensing system designed in this study can be a starting point for the development of more advanced agricultural robots that can monitor and manage planting land conditions more effectively.

Future Research Directions

Future research can focus on improving the accuracy of the prediction model by using more advanced machine learning algorithms and incorporating more environmental factors into the model. Additionally, the development of more advanced web-based dashboard applications can provide farmers with more detailed and real-time information about their planting land conditions.

The Potential of Agricultural Technology

The potential of agricultural technology is vast, and the development of more innovative and efficient agricultural systems can have a significant impact on the agricultural sector. By leveraging the power of machine learning and technological integration, farmers can optimize their agricultural practices and improve their productivity and sustainability.

The Role of Students in Agricultural Research

This research was conducted by undergraduate students, demonstrating the potential of students to contribute to agricultural research and development. The involvement of students in agricultural research can provide valuable insights and perspectives, and can help to drive innovation and progress in the agricultural sector.

Conclusion

In conclusion, the integrated sensing system designed in this study has the potential to revolutionize the way farmers monitor and manage planting land conditions. By leveraging the power of machine learning and technological integration, farmers can optimize their agricultural practices and improve their productivity and sustainability. This research demonstrates the potential of students to contribute to agricultural research and development, and highlights the importance of technological integration in agriculture.
Q&A: Integrated Sensing System for Monitoring Planting Land Conditions and Scheduling Plant Scheduling Using the Long Short-Term Memory Algorithm

In our previous article, we discussed the integrated sensing system designed to monitor planting land conditions and schedule plant scheduling using the Long Short-Term Memory (LSTM) algorithm. In this article, we will answer some of the most frequently asked questions about this system.

Q: What is the main purpose of the integrated sensing system?

A: The main purpose of the integrated sensing system is to monitor planting land conditions and schedule plant scheduling using the LSTM algorithm. This system aims to provide farmers with accurate and real-time information about their planting land conditions, allowing them to make informed decisions about watering and other agricultural practices.

Q: How does the LSTM algorithm work in this system?

A: The LSTM algorithm is a type of machine learning algorithm that is used to predict the level of soil humidity based on historical data and current environmental conditions. The algorithm takes into account various factors such as soil temperature, air temperature, and time to make predictions about soil humidity.

Q: What are the benefits of using the integrated sensing system?

A: The benefits of using the integrated sensing system include:

Accurate and real-time information about planting land conditions
Improved water use efficiency
Reduced costs associated with watering
Increased productivity and sustainability in agriculture
Reduced physical workload for farmers

Q: How does the web-based dashboard application work?

A: The web-based dashboard application is a user-friendly interface that provides farmers with real-time information about their planting land conditions. The application displays data such as soil humidity, soil temperature, and air temperature, allowing farmers to make informed decisions about watering and other agricultural practices.

Q: Can the integrated sensing system be used in other agricultural applications?

A: Yes, the integrated sensing system can be used in other agricultural applications such as monitoring crop health, predicting crop yields, and optimizing fertilizer use.

Q: What are the future directions for this research?

A: Future research directions for this project include:

Improving the accuracy of the prediction model using more advanced machine learning algorithms
Incorporating more environmental factors into the model
Developing more advanced web-based dashboard applications
Exploring the use of the integrated sensing system in other agricultural applications

Q: How can students contribute to agricultural research and development?

A: Students can contribute to agricultural research and development by:

Participating in research projects such as this one
Developing new technologies and innovations in agriculture
Collaborating with farmers and other stakeholders to identify research needs and priorities
Sharing their knowledge and expertise with others in the agricultural community

Q: What are the potential impacts of this research on the agricultural sector?

A: The potential impacts of this research on the agricultural sector include:

Improved productivity and sustainability in agriculture
Increased efficiency in water use
Reduced costs associated with watering
Improved crop yields and quality
Increased adoption of precision agriculture practices

Q: How can farmers benefit from this research?

A: Farmers can benefit from this research by:

Gaining access to accurate and real-time information about their planting land conditions
Improving their water use efficiency and reducing costs associated with watering
Increasing their productivity and sustainability in agriculture
Improving their crop yields and quality
Reducing their physical workload and improving their overall well-being.