Agriculture has always been the foundation of many economies, especially in countries like India, where a large part of the population depends on farming. However, traditional farming methods are becoming less effective due to problems like population growth, climate change, and poor resource management.
A 2021 research paper titled “Smart Agriculture System With E-Garbage Using IoT” offers a modern solution to these challenges. This system uses Internet of Things (IoT) technology—a network of interconnected devices that communicate and share data over the internet—to automate farming tasks, reduce human effort, and manage waste more efficiently.
The Challenges in Traditional Farming
Traditional farming faces many problems today. For example, labor shortages are a major issue. In India, over 70% of farmers struggle to find enough workers because many people are moving to cities for better jobs. Another problem is water wastage. Old-fashioned irrigation methods waste 30–40% of water because they do not distribute water evenly.
Precision agriculture, a modern farming approach that uses data and technology to optimize resources, can solve this. However, most farmers still rely on manual methods. Pollution is also a big concern. In developing countries, nearly 60% of farmland is damaged due to improper waste disposal.
Lastly, manual monitoring of crops and waste bins increases costs by 25%. These challenges make farming harder and less profitable for many people.
To solve these problems, the research paper introduces a smart farming system powered by IoT. IoT refers to a network of devices, like sensors and cameras, that can collect and share data over the internet. These devices work together to automate tasks and provide real-time updates.
For example, soil moisture sensors can detect dry soil and trigger water pumps automatically. This technology has already shown success in pilot projects. For instance, farms using IoT for precision agriculture saw crop yields increase by 20%. This proves that technology can make farming more efficient and sustainable.
How the Smart Agriculture System Works
The smart agriculture system has two main parts: one for monitoring crops and another for managing waste. Let’s explore both parts step by step.
1. Crop Monitoring and Automation
The first part of the system focuses on improving crop growth. It uses sensors placed in the soil and air to collect real-time data. For example, a soil moisture sensor—a device that measures water content in the soil—checks how much water is available. If the moisture level drops below 30%, the system automatically turns on water pumps to irrigate the fields. This ensures crops get the right amount of water without waste.
Another important sensor is the temperature sensor (DHT11). This small electronic device monitors air and soil temperature. Crops like wheat grow best in temperatures between 15°C and 20°C. If the temperature goes too high or too low, the system sends an alert to the farmer’s phone. Similarly, a light sensor (LDR) measures sunlight levels.
Crops like tomatoes need 8–12 hours of sunlight daily. If the weather is cloudy for too long, the system notifies the farmer to take action, such as using artificial lights.
Air quality is also monitored. A gas sensor (MQ-135) detects harmful pollutants like ammonia and carbon dioxide. These gases are released from vehicles, factories, or rotting waste and can harm crops. If pollution levels rise above 200 parts per million (ppm), the farmer receives an SMS alert via a GSM module—a hardware component that enables mobile communication. This helps prevent crop damage and keeps the environment safe.
All this data is sent to a cloud server, like Amazon Web Services (AWS), using a GSM module. A cloud server is a remote computer that stores and processes data over the internet. Farmers can view this information on a mobile app. The app shows details like soil moisture, temperature, and pollution levels in simple charts. This way, farmers can make quick decisions without visiting the field constantly.
2. Smart Waste Management (E-Garbage System)
The second part of the system tackles waste management. In many cities, garbage bins overflow because collection trucks follow fixed schedules, even if bins are empty. The smart system solves this by using ultrasonic sensors—devices that use sound waves to measure distance—attached to trash bins. These sensors measure how full a bin is. When the trash reaches 90% capacity, the system sends a signal to the city’s waste management team.
This signal includes the bin’s location, which is tracked using GPS (Global Positioning System)—a satellite-based navigation system. Waste collection trucks then use this data to plan the shortest route. In tests, this method reduced travel distance by 30%, saving fuel and time. For example, in Chennai, this system helped save 120 liters of diesel per truck every month.
The bins also have pollution control features. A gas sensor inside the bin detects methane, a harmful gas produced by rotting waste. If methane levels exceed 500 ppm, a small fan turns on to ventilate the bin. Some bins even have solar-powered compactors—machines that use solar energy to compress trash. These compactors reduce waste volume by 40%, meaning bins can hold more waste before needing to be emptied.
Another clever feature connects the trash bins to streetlights. When a bin is full, nearby streetlights flash red. This alerts municipal workers during nighttime collections. In trials, this reduced delays in waste pickup by 50%.
Real-World Results from Tamil Nadu
The researchers tested this system on 50 farms and 200 trash bins in Tamil Nadu, India. After six months, the results were impressive. For farming, the system led to a 22% increase in crop yields. This happened because the automated irrigation system—a setup where sensors and pumps work without human intervention—provided the right amount of water at the right time.
Water usage dropped by 30%, which is crucial in areas with water shortages. The pollution alerts also helped reduce crop diseases by 15%, as farmers could act quickly to protect their plants.
In waste management, garbage collection became 35% faster due to optimized routes. Fuel costs for trucks fell by 25%, and complaints about overflowing bins dropped by 60%. These results show that the system works well in real-life conditions.
Technical Details Made Simple
The system uses affordable and easy-to-find hardware. The main controller is an Arduino Uno—a small, programmable microcontroller board that costs around $20. It acts as the brain of the system, processing data from sensors and triggering actions like turning on water pumps. The Arduino Uno runs on 5 volts and has a clock speed of 16 MHz, making it powerful enough to handle multiple sensors. The sensors include:
- An ultrasonic sensor (HC-SR04) to measure trash levels. It works by sending sound waves and measuring how long they take to bounce back, much like how bats navigate. It can detect objects up to 4 meters away.
- A temperature sensor (DHT11) with an accuracy of ±1°C. This means it can detect even small changes in temperature.
- A gas sensor (MQ-135) that detects pollution levels between 10 and 300 ppm. This range is ideal for monitoring farm environments.
For watering crops, the system uses 12V DC water pumps. These pumps are strong enough to irrigate small farms but consume little power. The trash compactors in the bins use servo motors—small motors that provide precise control, making them quiet and energy-efficient.
On the software side, the system uses the Blynk IoT platform—a user-friendly app-building tool for IoT projects. Farmers can check soil moisture or pollution levels with just a few taps. The app also sends alerts when something needs attention. For route planning, the system uses Google Maps API—a tool that helps calculate the shortest paths for garbage trucks. Data from all sensors is stored on ThingSpeak, a free cloud service designed for IoT projects.
Why This System Is Better Than Older Methods
Earlier systems for smart farming had limitations. For example, some could only measure soil moisture but not control water pumps automatically. Others required farmers to manually check data on a computer. This new system solves those problems by combining automation with real-time alerts.
Another advantage is cost. Setting up this system costs less than $200 per acre. This is affordable for small farmers, especially when compared to the long-term savings from reduced water and labor costs. The system is also modular, meaning farmers can start with basic sensors and add more features later, like drones for monitoring large fields.
Challenges and The Future of Smart Farming
While the system has many benefits, there are some challenges. For example, sensors need a steady power supply. In rural areas with frequent power cuts, this can be a problem. To fix this, the researchers suggest using solar panels—devices that convert sunlight into electricity—to charge batteries.
Another issue is training. Many farmers are not familiar with smartphones or apps. The solution is to provide simple training sessions and create guides in local languages. Maintenance is also important. Sensors can get dirty or damaged by weather. The system needs yearly maintenance, costing about $50 per farm. While this is an extra expense, it is much lower than the cost of crop failures or wasted water.
The researchers believe this system can be improved even further. For example, adding cameras powered by artificial intelligence (AI)—computer systems that mimic human intelligence—could help detect pests or diseases early. Drones could fly over fields to take detailed photos, showing which areas need more water or fertilizer.
Another idea is to use blockchain technology—a secure digital ledger system—to track produce from farm to market. This would help farmers prove their crops are organic or chemical-free, allowing them to get better prices. Solar power could also play a bigger role. Replacing grid electricity with solar panels would make the system more eco-friendly and reduce costs.
Conclusion
The Smart Agriculture System with E-Garbage Using IoT is a powerful tool for modern farming. By automating tasks like irrigation and waste collection, it saves time, money, and resources. Real-world tests in Tamil Nadu show that it boosts crop yields, cuts water usage, and makes waste management faster and cheaper.
For this system to succeed widely, governments and organizations need to support farmers. This could include funding for equipment, training programs, and subsidies for maintenance. With its affordable design and proven results, this system offers hope for farmers struggling with traditional methods. By embracing IoT and smart technology, agriculture can become more sustainable, efficient, and profitable for everyone.
Power Terms
Internet of Things (IoT): A network of physical devices connected through the internet. These devices, like sensors or cameras, can collect and exchange data automatically without human help. For farmers, IoT allows remote monitoring of fields and automatic control of equipment like irrigation systems. A practical example is soil sensors sending moisture data to a farmer’s smartphone. (Related concept: Smart devices)
Precision Agriculture: A modern farming approach that uses technology to optimize crop production. It employs tools like GPS, sensors and data analytics to precisely manage water, fertilizers and other resources. This method is crucial because it reduces waste while increasing yields – for instance, applying just the right amount of water to each section of a field based on soil moisture readings. (Alternative term: Smart farming)
Soil Moisture Sensor: An electronic device that measures water content in soil. These sensors help prevent both overwatering and underwatering of crops by providing accurate moisture readings. When soil becomes too dry, the sensor can trigger automatic irrigation systems. They work by measuring electrical resistance – dry soil conducts electricity poorly compared to moist soil. (Measurement unit: Percentage of water content)
Temperature Sensor (DHT11): A small electronic component that measures both air temperature and humidity. The DHT11 is particularly important for monitoring crop environments because many plants thrive only within specific temperature ranges. For example, lettuce grows best between 7-24°C – if temperatures exceed this range, the sensor alerts the farmer. (Accuracy: ±1°C for temperature)
Light Sensor (LDR): A component that detects light intensity using a photoresistor. In agriculture, LDRs help ensure crops receive adequate sunlight by measuring duration and intensity of light exposure. When light levels drop too low, the system might recommend using grow lights. The sensor works because its electrical resistance decreases when exposed to more light. (Technical name: Photoresistor)
Gas Sensor (MQ-135): A device that detects harmful gases like ammonia or carbon dioxide. These sensors protect both crops and farmers by monitoring air quality in fields and greenhouses. When gas concentrations reach dangerous levels (e.g., above 200ppm for ammonia), the sensor triggers ventilation systems or alerts. (Measurement unit: Parts per million)
Ultrasonic Sensor (HC-SR04): A distance-measuring device that uses sound waves. In smart agriculture, these sensors monitor trash bin fill levels by sending ultrasonic pulses and measuring their return time. For example, if the sensor detects trash within 10cm of the bin’s top, it signals that collection is needed. (Calculation method: Time-of-flight measurement)
GSM Module: A hardware component that enables cellular network communication. These modules allow agricultural sensors to send SMS alerts – like notifying a farmer when soil moisture drops critically low. They’re particularly useful in remote areas where internet connectivity may be unreliable. (Network type: 2G cellular)
Cloud Server: A remote computer system that stores and processes data over the internet. In smart farming, cloud servers collect sensor data from multiple fields, allowing farmers to monitor conditions from anywhere. Services like AWS or ThingSpeak can analyze trends in soil health over time. (Key feature: Remote accessibility)
Automated Irrigation System: A setup that waters crops without human intervention. These systems combine soil moisture sensors with water pumps and controllers to deliver precise amounts of water exactly when needed. For example, when sensors detect soil moisture below 30%, the system automatically activates irrigation. (Energy source: Typically electric or solar)
Solar-Powered Compactors: Waste compression devices that use solar energy. These compactors in smart trash bins reduce garbage volume by up to 40%, allowing bins to hold more waste between collections. The solar panels convert sunlight into electricity to power the compaction mechanism. (Environmental benefit: Reduces collection frequency)
GPS (Global Positioning System): A satellite-based navigation system that provides location data. In waste management, GPS helps optimize collection routes by identifying the most efficient path between full trash bins. This technology can reduce fuel consumption by up to 30% compared to traditional collection methods. (Satellite count: 24 operational satellites)
Arduino Uno: A popular microcontroller board used in IoT projects. In smart agriculture, the Arduino processes data from various sensors and controls connected devices like water pumps. For instance, it might read temperature data and decide whether to activate cooling systems. (Processing speed: 16 MHz)
12V DC Water Pump: An electric pump that operates on 12 volts of direct current. These pumps are commonly used in automated irrigation systems because they’re energy-efficient and suitable for small to medium-sized farms. When activated by the control system, they deliver precise amounts of water to crops. (Power calculation: Voltage × Current)
Servo Motor: A precise, controllable motor used in various automation tasks. In smart trash bins, servo motors power the compactors that reduce waste volume. These motors are ideal because they can be precisely controlled to apply just the right amount of compaction force. (Control method: Pulse-width modulation)
Blynk IoT Platform: A user-friendly app development platform for IoT projects. Farmers can use Blynk to create custom dashboards that display sensor data and control farm equipment remotely. For example, a simple interface might show soil moisture levels with color-coded alerts. (Key feature: Drag-and-drop interface)
Google Maps API: A programming interface that allows applications to use Google Maps data. Waste management systems use this API to calculate optimal collection routes by analyzing traffic patterns and bin locations. This helps reduce fuel costs and improve collection efficiency. (Data used: Real-time traffic information)
ThingSpeak: An IoT analytics platform that collects and visualizes sensor data. Farmers can use ThingSpeak to track long-term trends in field conditions, like gradual changes in soil moisture patterns across seasons. The platform can generate charts and send alerts based on the data. (Parent company: MathWorks)
Methane (CH4): A flammable greenhouse gas produced by decomposing organic waste. Monitoring methane levels in trash bins is important for both safety and environmental reasons. High concentrations (above 500ppm) can be dangerous and may require ventilation. (Environmental impact: 25× more potent than CO2 as greenhouse gas)
Parts Per Million (ppm): A unit of measurement for small concentrations. In agriculture, ppm is used to quantify things like gas concentrations or nutrient levels in soil. For example, carbon dioxide levels above 1000ppm might trigger greenhouse ventilation systems. (Equivalent to: 1 milligram per liter)
Modular Design: A system architecture where components can be added or removed easily. Smart farming systems benefit from modularity because farmers can start with basic sensors and later add more advanced features like drone monitoring. This makes the technology more accessible and scalable. (Advantage: Future-proofing)
Artificial Intelligence (AI): Computer systems that can perform tasks typically requiring human intelligence. In agriculture, AI might analyze images from field cameras to identify pest infestations or nutrient deficiencies in crops. These systems learn from data to improve their accuracy over time. (Subfield: Machine learning)
Blockchain Technology: A secure, decentralized digital ledger system. For farming, blockchain could create tamper-proof records of crop production methods, helping farmers prove their products are organic or sustainably grown. Each transaction or record is cryptographically secured in the chain. (Key feature: Immutability)
Solar Panels: Devices that convert sunlight into electricity. In remote farming applications, solar panels provide reliable power for sensors and communication devices where grid electricity is unavailable. A typical small solar setup might include a 20-watt panel and battery storage. (Efficiency: Typically 15-20%)
Clock Speed (16 MHz): A measure of how many operations a processor can perform each second. The Arduino Uno’s 16 MHz clock speed means it can execute 16 million cycles per second, which is sufficient for most agricultural monitoring applications. This determines how quickly the system can process sensor data and respond. (Comparison: Human brain ~1000 Hz)
Reference:
Senthil Kumar, A., Suresh, G., Lekashri, S., Babu Loganathan, G., & Manikandan, R. (2021). Smart agriculture system with E–carbage using IoT. International Journal of Modern Agriculture, 10(1), 928-931.
