The agricultural domain is another crucial domain in the internet of things. The Internet of Things is responsible for modernizing agriculture by utilizing advanced methods and instruments to manage crops, soil, and animals. As a result, waste generation has decreased and productivity has increased dramatically. This is IoT-enabled smart agriculture.
The following are some examples of IoT applications in agriculture:
Sensors for monitoring and tracking crop and insect status
Drones for livestock monitoring, such as hens
Water pumping systems that are automated to water crops at convenient times
Machines that perform route operations and ensure the proper operation of systems
Table of Contents
What exactly is smart agriculture?
Smart agriculture is a relatively new concept. The vast majority of farmers and agricultural experts are unfamiliar with this concept. Smart agriculture is the use of smart technologies to control and operate agricultural lands and animals, such as automated machines, sensors, actuators, drones, and security cameras. The goal is to increase the quality and quantity of agricultural goods while keeping costs and energy consumption in mind.
What exactly is a smart farm?
A smart farm is managed using smart technologies and modern forms of communication.
Farmers use the following technologies in smart farms:
Sensors for controlling soil, water, moisture, and humidity
Software for diagnosing plant and animal disease, as well as learning and treating the disease.
LoRaWAN or cellular networks can be used to connect.
Using GPS and satellites to manage and track locations
The smart farms are completely run by automated tools and robotics, so the farmer never has to set foot on the field. Smart farming reduces the cost of manual labor. IoT integrates and connects the entire farm to improve crop quality and quantity.
Traditional farming and smart farming
Traditional farming methods employed antiquated and outmoded farming techniques. To monitor the soil and manage the livestock, manual methods are used. These methods are both time-consuming and costly. Early detection of outbreaks is extremely difficult.
Smart farming manipulates and manages agricultural yields using modernized methods and IoT-based technology. It entails using geolocation, GPS tracking, sensors, and drones to monitor fields, crops, and cattle.
Smart farming cycle based on IoT
Any IoT-based technology relies on data. Smart farms must form a continuous and constant cycle that collects and analyzes data to perform the next set of actions to ensure optimization. An example of a smart farming cycle is as follows:
Observation: Sensors are used to sense the environment and collect data such as soil, temperature, humidity, and so on.
Diagnostics: data collected from sensors is sent to IoT-based cloud platforms for analysis.
Decisions: Farmers make relevant decisions based on their analysis to generate better outputs.
Actions: When the tasks are completed, the cycle begins again from the beginning.
Internet of Things (IoT) solutions for agricultural issues
Many agricultural problems can be solved using IoT. Precision farming and automated farming are two of the most important IoT-based solutions.
Farming with pinpoint accuracy (Percision farming)
Precision farming is a modern approach that employs precision values to increase yield and produce. It collects precise data and amounts about agricultural lands. As a result, plants and animals receive the precise amount of input they require because decisions are made for each plant or animal rather than the entire crop or herd.
Livestock farming with pinpoint accuracy (Percision livestock farming)
The primary goal of PLF is to monitor, control, and manage individual animals through the use of precision techniques that generate accurate data about individual cattle. Precision technology measures and collects data on livestock 24 hours a day, seven days a week. If something goes wrong with any farm animal, the farms are notified. This enables farmers to respond quickly to prevent disease outbreaks or other setbacks.
Automation in intelligent greenhouses
Temperature sensors and actuators are used to control smart greenhouses. These devices collect and transmit data 24 hours a day, seven days a week. This information is fed into clouds, which are then stored in servers for future use. Following the completion of the performance analysis, lighting control and spraying activities are managed accordingly.
Drones for agriculture
Farmers use drones to gather insight and real-time data about crops and animals in barns. Drones fly over the entire field and send alerts to farmers if there is any suspicious activity. These drones are also used to provide security.
The Third Green Revolution and the Future of Smart Farming
Smart agriculture is rapidly taking over farms of all sizes. It is becoming a part of the so-called third green revolution. It entails managing agricultural yields through the use of modern IoT devices such as security cameras, sensors, drones, and actuators. Smart agriculture has increased profitability by assisting farmers as well as consumers.
Agriculture and IoT
The world’s population is increasing at an alarming rate. Agriculture must be reformed to feed the world in large quantities. The internet of things enables the integration of technology with agriculture to provide better food quality, management, monitoring, and control.
What role do automation and robotics play?
As the world’s population grows, so will the demand for food. To meet the demands, we need automated methods that reduce the cost of manual labor. We also need methods to shorten the time it takes to cultivate produce. All of this must be done while keeping the health of those who will consume the product in mind.
Semi-automated robots could be deployed in fields to detect insects and pesticides and halt their growth at an early stage. These robots are also capable of ignoring large vehicles. These operations can be easily managed by farmers from the comfort of their own homes.
Smart agriculture processes
- Data collection: Farmers install sensors in their fields to collect information about soil moisture levels, for example.
- Diagnostics: the data is then sent to the cloud to be processed. The data is interpreted in order to produce weighted outputs.
- Decision-making: The next step is taken with extreme caution and care based on the processed data.
- Action: Once a decision has been made, it is time to take the necessary steps to achieve the goals.
Intelligent irrigation system
Smart irrigation systems monitor the water levels in the water lanes created by farmers. They make use of raspberry pies and Arduino boards. The raspberry pi serves as the main processing unit, with an Arduino board for each of the water lanes.
The Arduino boards can be connected to various sensors found in the water lanes. These sensors collect vital information such as the soil’s moisture and hydration levels.
If a water lane does not meet the farmer’s specifications, the Arduino boards send signals to the Raspberry Pis, which then alert the farmers to take the necessary actions. Farmers interact with the application to activate relays that send water to the lanes based on the criteria specified. Smart irrigation systems can be thought of as gate-controlled systems that keep the gates open only when the moisture content is low.
If the moisture levels exceed the preset levels, it sends another signal to the raspberry pi to stop the pumping. This system promotes resource efficiency and water conservation. Smart systems like these boost agricultural productivity.
The Modern Agricultural City’s Difficulties
Some of the challenges farmers face as the farming industry modernizes are listed below:
a scarcity of labor and human resources
Greenhouse gases and global warming
On a larger platform, manual intervention is required.
There are no proper monitoring systems.
Difficulties in processing a large amount of non-uniform data
Agriculture IoT analytics
The data collected by sensors is routed for further processing and analysis. Smart farming combines machine learning and predictive analysis to assist farmers in dealing with natural disasters such as droughts and landslides.
Smart agriculture implementation
- Drone-based applications
Smart farming also includes the use of drones to monitor areas from a distance. Drones can go places that humans cannot. They are also capable of gathering data and information. They can send users data about soil, livestock, and water levels, as well as aid in the prevention of burglaries, crow attacks, and other crimes.
- Crop monitoring in real time
Temperature sensors, light sensors, motion sensors, and a variety of other smart sensors allow for real-time crop updates. Farmers can remotely monitor the status of their crops to ensure that everything is running smoothly.
- Animal husbandry
Sensors and smart cameras can count the number of animals on the farm, such as hens and pigs. Sensors can also gather information about the health of the farm’s animals. This aids in the early detection of flu outbreaks and enables farmers to take appropriate action more quickly.
- Monitoring of tank levels
IoT enables remote monitoring of water levels in large tanks. It notifies authorities when the water level in the tanks falls below the average level.
- Intelligent greenhouse solutions
Greenhouses ensure that the plants receive the necessary amount of oxygen. This procedure, however, necessitates ongoing monitoring and outside intervention. A smart greenhouse, on the other hand, employs IoT monitors and sensors that sense the atmosphere to send vital data for further analysis. This is a dependable and efficient procedure that significantly reduces the level of harmful pollutants in the environment.
6. data analytics
Cloud and IoT platforms are critical components of smart agriculture systems. Sensors are the primary means of data collection. However, if there is no proper mechanism in place to analyze the data, the information is useless. The data is predictively analyzed, and the cloud generates the necessary action based on the data.
- Infrastructure necessities
Smart farming requires some basic infrastructure, such as a stable internet connection, hardware maintenance costs, a large investment in drones and sensors, a well-trained staff, and a stable power connection.
Conclusion
We looked in-depth at the topic of smart agriculture in this article. We discovered how it differs from traditional farming and its applications, which range from real-time crop monitoring to smart greenhouse solutions. We hope you found the article to be easy to understand.
