Pertanian Terpadu Berbasis IoT dan Machine Learning untuk Pengoptimalan Hasil Pertanian

Summary

This project addresses Indonesia's growing population and the increasing demand for domestic food supply, focusing on enhancing crop productivity. It introduces an automated system for monitoring vital plant growth factors like air humidity, temperature, soil moisture, and light, which are essential for optimal plant health. By integrating sensors with a microcontroller and transmitting data via a gateway, the system enables real-time monitoring and water requirement predictions using machine learning. The system features both automatic and manual control modes, allowing for efficient plant management, with real-time data displayed on a dashboard to ensure proper growth conditions, especially for chili pepper plants critical to Indonesia's food supply.

Takeaways

• 😀 The population growth rate in Indonesia is 1.25% per year, which increases the demand for domestic food supply.
• 😀 The conversion of agricultural land to non-agricultural use is rising, challenging the ability to meet food demands.
• 😀 Increasing crop productivity is necessary to address the growing domestic food demand in Indonesia.
• 😀 Crop productivity is closely linked to plant growth aspects such as air humidity, temperature, soil moisture, and light requirements.
• 😀 Currently, monitoring plant growth is done manually, which is inefficient for optimal plant development.
• 😀 A system is needed to continuously monitor environmental factors to maintain optimal plant growth conditions.
• 😀 This project focuses on monitoring the growth aspects of chili peppers (cabai rawit), which play a significant role in meeting food demands and combating inflation in food prices.
• 😀 The project will use sensors connected to a microcontroller to collect data, which is then sent via a data protocol to a gateway and displayed on the ThingSpeak platform for analysis.
• 😀 The data collected will be processed alongside weather data, and machine learning will be used to predict soil water requirements.
• 😀 A real-time dashboard will be available to control the system, with two modes: automatic and manual. In automatic mode, actuators operate without user intervention, while in manual mode, the user can send control commands via the dashboard.

Q & A

What is the main challenge that Indonesia faces regarding food production?

Indonesia faces a growing demand for food due to a population growth rate of 1.25% annually. This increase in population, combined with the rising conversion of agricultural land to non-agricultural uses, creates challenges in ensuring a sufficient food supply.

Why is the project focusing on chili peppers (cabai rawit)?

The project focuses on chili peppers (cabai rawit) because they play a significant role in meeting the food needs of the Indonesian population and are a major contributor to food inflation. Ensuring their optimal growth is essential for food security.

What factors affect the growth of plants like chili peppers in this project?

The growth of plants is influenced by various environmental factors including air humidity, temperature, soil moisture, and light. Monitoring these factors is crucial for ensuring optimal growth conditions.

How is data collected in this project?

Data is collected using sensors that are connected to a microcontroller. These sensors monitor environmental conditions like humidity, temperature, and soil moisture, and send the data via a communication protocol (LoRaWAN) to a central gateway.

What role does machine learning play in this project?

Machine learning is used to analyze the collected data and predict the irrigation needs of the plants, particularly soil moisture levels. This helps optimize water usage and supports efficient plant growth.

How is the data made available for monitoring?

The collected data is processed and made available in real-time on a ThingSpeak dashboard, which can be accessed by users for monitoring and controlling the system.

What are the two modes of operation in the system?

The system operates in two modes: automatic and manual. In automatic mode, the actuators control the system based on sensor data without user intervention. In manual mode, users can send commands to control the system through the dashboard.

What is the significance of the LoRaWAN protocol in this system?

The LoRaWAN protocol is used for transmitting data from the sensors to the gateway. This protocol enables long-range, low-power communication, making it suitable for agricultural environments where reliable data transmission is necessary.

How does the system contribute to food security in Indonesia?

By optimizing agricultural practices through real-time monitoring and predictive analysis, the system helps improve the productivity of key crops like chili peppers, contributing to food security in Indonesia by ensuring efficient use of resources.

What is the overall goal of the project?

The main goal of the project is to develop a system that can monitor and optimize the growth of crops, particularly chili peppers, by leveraging sensors, machine learning, and real-time data analysis to ensure sustainable food production and address the challenges of increasing food demand.