Internet of things in agriculture: what is IoT and how is it implemented in agriculture?

Team Cropin
August 23, 2024
17 min read

What is IoT? - the simple explanation

We are witnessing unprecedented levels of adoption of technology in Agriculture. There are many emerging technologies in agriculture that seem very promising for the future of farming. IoT was once an emerging agri technology and now it has taken a mainstream position by wider adoption. IoT in agriculture by the simplest of definitions is just that – Internet controlling things.

Every time you look at your smart watch to count calories or ask Alexa or Siri to calculate the value of pie, you are using what is IoT tech. IoT by simplest of definitions is just that – Internet controlling things. IoT devices are ‘smart’ devices that can transfer data over a network. One of the first network connected devices was a 1982 Coke vending machine at Carnegie Mellon University, which was able to report if the drinks were cold or if there were drinks at all.

What does IoT mean?

The term Internet of Things was coined in 1999 by Kevin Ashton, co-founder and executive Director of the Massachusetts Institute of Technology (MIT) Auto-ID Laboratory, while he was giving a presentation at Procter and Gamble (P&G) as their Brand Manager. The presentation that Ashton made for P&G was meant to introduce radio-frequency identification (RFID) tags to manage the supply chain so that the location and stock at hand of each item coming out of it can be more easily monitored.

Riding the RFID wave, LG Electronics then put out a refrigerator known as the Internet Digital DIOS back in the year 2000 which was connected to the Internet. It kept track of the kind of food items that were stored in it as well as their respective quantities by scanning their RFID tags. Though the Internet Digital DIOS refrigerator didn’t sell well because most people thought it was too expensive for their needs, it would eventually pave the way for more house appliances to be managed via internet.

According to Cisco Internet Business Solutions Group (IBSG), though the term was coined in 1999, the Internet of Things was born in between 2008 and 2009 at simply the point in time when more “things or objects” were connected to the Internet than people. Citing the growth of smartphones, tablet PCs, and other smart gadgets, the number of devices connected to the Internet was brought to 12.5 billion in 2010 while the world’s human population increased to 6.8 billion, making the number of connected devices per person more than 1 (1.84 to be exact) for the first time in history.

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IoT technologies in agriculture

IoT smart agriculture products are designed to help monitor crop fields using sensors and by automating irrigation systems. As a result, farmers and associated brands can easily monitor the field conditions from anywhere without any hassle.

Take a look at the different uses of IoT in agriculture by means of various IoT solutions:

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1. Robotics in agriculture

Since the industrial revolution in the 1800s, automation got more advanced to efficiently handle sophisticated tasks and increase production. With increasing demands and shortage of labor across the globe, agriculture robots or commonly known as Agribots are starting to gain attention among farmers. Crop production decreased by an estimated 213 crores approx ($3.1 billion) a year due to labor shortages in the USA alone. Recent advancements in sensors and AI technology that lets machines train on their surroundings have made agrobots more notable. We are still in the early stages of an ag-robotics revolution, harnessing the full potential of the Internet of Things in agriculture, with most of the products still in early trial phases and R&D mode.

Weeding robots

These smart Agri robots use digital image processing to look through the images of weeds in their database to detect similarities with crops and weed out or spray them directly with their robotic arms. With an increasing number of plants becoming resistant to pesticides they are a boon to the environment and also to farmers who used to spread the pesticides throughout the farm. An estimated 13,000 kilograms (3 billion pounds) of herbicides are applied at a cost of 1,725 crores ($25B) each year, thus reducing their overall cost.

Machine Navigation

As remote-controlled toy cars are enabled with a controller, tractors and heavy plowing equipment can be run automatically from the comfort of home through GPS. These integrated automatic machines are highly accurate and self-adjust when they detect differences in terrains, simplifying labor-intensive tasks. Their movements as well as work progress can be easily checked on smartphones. With advancements in IoT in Agricultural and machine learning, these tech-driven motors are enabling Advanced farming using IoT independently with features such as automatic obstacle detection.

Harvesting Robotics

Utilizing agribots to pick crops is solving the problem of labor shortages. Working the delicate process of picking fruits and vegetables these innovative machines can operate 24/7. A combination of image processing and robotic arms is used by these machines to determine the fruits to pick hence controlling the quality. Due to high operational costs, crops that have an early focus on agribot harvesting are orchard fruits like apples. Greenhouse harvesting also finds applications with these bots for high-value crops like tomatoes and strawberries. These bots can work in greenhouses to aptly determine the stage of crops and harvest them at the right time.

Material Handling

Robots can perform dreaded manual labor tasks working alongside the labors. They can lift heavy materials and perform tasks like plant spacing with high accuracy, therefore optimizing the space and plant quality and reducing production costs.

2. Drones in agriculture

Drones in agriculture are used to enhance and optimize various farming activities such as crop monitoring, crop spraying, soil analysis, and mapping. In fact, agriculture is one of the major sectors to incorporate drones. Drones equipped with sensors and cameras are used for imaging, mapping, and surveying farms. There are ground-based drones and aerial drones. Ground drones are bots that survey the fields on wheels. Aerial drones, formally known as unmanned aerial vehicles (UAVs) or unmanned aircraft systems (UAS), are flying robots. Drones can be controlled remotely or they can fly automatically through software-controlled flight plans in their embedded systems, working in coordination with sensors and GPS. From the drone data, insights can be drawn regarding crop health, irrigation, spraying, planting, soil and field, plant counting, yield prediction, and much more. Drones can either be scheduled for farm surveys (drone as a service) or can be bought and stored near farms where they can be recharged and maintained. After the surveys, the drones need to be taken to nearby labs to analyze the data that has been collected, thereby helping leverage IoT in agriculture better.

3. Remote sensing in agriculture

Remote sensing in agriculture is revolutionizing the way data is acquired from different nodes in a farm’ IoT-based remote sensing utilizes sensors placed along with the farms like weather stations for gathering data, which is transmitted to analytical tools for analysis. Sensors are devices sensitive to anomalies. Farmers can monitor the crops from the analytical dashboard and take action based on insights.

Crop monitoring

Sensors placed along the farms monitor the crops for changes in light, humidity, temperature, shape, and size. Any anomaly detected by the sensors is analyzed and the farmer is notified. Thus remote sensing can help prevent the spread of diseases and keep an eye on the growth of crops.

Weather conditions

The data collected by sensors in terms of humidity, temperature, moisture precipitation, and dew detection helps in determining the weather pattern in farms so that cultivation is done for suitable crops.

Soil quality

Soil health analysis helps in determining the nutrient value and drier areas of farms, soil drainage capacity, or acidity, which allows for adjustment of the amount of water needed for irrigation and opting for the most beneficial type of cultivation. The soil health data can also help leverage regenerative agriculture by providing insights into how and when to increase organic matter and therefore achieve a better soil structure and eventually pave a path for climate-smart agriculture.

4. Computer imaging in agriculture

Computer imaging involves the use of sensor cameras installed at different corners of the farm or drones equipped with cameras to produce images that undergo digital image processing. Digital image processing is the basic concept of processing an input image using computer algorithms. Image processing views the images in different spectral intensities such as infrared, compares the images obtained over a period of time, and detects anomalies, thus analyzing limiting factors and helping a better management of farms.

Smart agriculture system using IoT: in simple words

IoT device includes every object that can be controlled through the Internet. IoT devices have become commonplace in consumer markets with wearable IoWT (Internet of Wearable Things), such as smartwatches, and home management products, like Google home. It is estimated over 30 billion devices could be connected to the Internet of Things by 2020.

The applications of the Internet of Things in agriculture target conventional farming operations to meet the increasing demands and decrease production losses. IoT in agriculture uses robots, drones, remote sensors, and computer imaging combined with continuously progressing machine learning and analytical tools for monitoring crops, surveying, and mapping the fields, and providing data to farmers for rational farm management plans to save both time and money.

Data collection

One of the biggest applications of cloud-based software in agriculture is for data collection and retrieval. It stores high volumes of data relating to weather, crop patterns, soil quality, harvesting, and satellite imagery to provide insights with accuracy and speed. All the data related to the farm is stored in the cloud and hence readily accessible. So, if crops are affected by the same symptoms as 10 years ago, the data can be used to find a remedy more quickly than before, preventing extensive losses.

Data processing analysis

Database management in cloud software ties up all the loose ends of every type of data available with respect to farms to enable higher levels of decision-making. Meteorological data, market data, farm data, GIS, and water availability. All forms of historical and current data are analyzed thoroughly before giving valuable insights into optimum seed, water, and pesticide requirements for a farm. The systems also have an alert system whenever discrepancies in crop growth are detected. Hence these systems work efficiently in case of pest attack informing farmers with actionable data.

DatasStorage and dissemination

Data storage is the backbone of predictive analysis. Earlier the data storage was hardware-based, hence the infrastructure needed to be carefully maintained and updated. Any damage to the hardware meant the data was gone forever. Nowadays, agritech systems are cloud-based, which means that one need not invest in purchasing and maintaining hardware. All the data is available all the time and can even be accessed through PCs and mobile gadgets. This approach to data storage also makes it accessible for other devices and solutions to consume it for further analysis. The more the data available relating to farms, the more accurate the insights pertaining to crop production and management, pest and disease detection, crop yield estimation, and others.

Smart farming applications of cloud-based software

Cloud-based software, one of the best outputs in the range of advanced farming technology available now, finds applications with:

Smart farming with Saas-based cloud software

Climate smart agriculture (CSA) is an approach to agriculture that aims to help farmers adapt to and mitigate the effects of climate change. CSA focuses on three main pillars: increasing productivity, reducing emissions, and improving resilience. One way to increase productivity is to use climate-resistant crops or varieties. For example, heat-tolerant varieties of rice can be planted in areas that are prone to droughts. Another way to reduce emissions is to improve farm management practices, such as using more efficient irrigation systems. Finally, CSA also involves building resilience into farming systems, such as by diversifying crops and using early warning systems for extreme weather events. By adopting climate smart agriculture practices, farmers can help to keep their operations running smoothly in the face of a changing climate.

Smart farming by Cropin

Smart farming focuses on the application of captured data and combining it from various data sources to show the bigger picture to manage all the activities of the farm. Smart farming is a big leap from traditional farming as it brings certainty and predictability to the table which is the Future of Agriculture.

Robotics, drones, and sensors placed throughout the farms can collect data that can be processed to produce farm insights. Cloud-based software can be used to collect the data from farms and combine them with other sources of data to determine yield output, irrigation scheduling, disease outbreaks, pest infestations, and the like. It can also consume off-farm data, such as market information and dealer availability, to enable informed decision-making post-harvest processes.

As a global agritech, Cropin has worked with 200+ public and private sector organizations worldwide, utilizing deep learning, satellite monitoring, cloud computing, precision agriculture, and other technological advancements to collect, analyze data, and manage all the activities from farm to fork with streamlined farm solutions.

Cropin’s SaaS solution is beneficial for farming companies, lending and insurance institutions, food processing companies, insurance providers, seed production, non-profit organizations, and government agencies.

Frequently asked questions (FAQs)

1. What is IoT?

IoT is the abbreviated form of the Internet of Things. IoT is a broad terminology given to every object that can relay information when connected to the network. The term Internet of Things was coined in 1999 by Kevin Ashton, co-founder and Executive Director of the MIT Auto-ID Laboratory while he was giving a presentation at Procter and Gamble (P&G) as their Brand Manager. He wanted to introduce RFID tags to manage the supply chain so that the location and stock at the hand of each item coming out of it can be more easily monitored.

2. What is the role of IoT in agriculture?

Agriculture through precision agriculture implements IoT through the use of robots, drones, sensors, and computer imaging integrated with analytical tools for getting insights and monitoring the farms. Placement of physical equipment on farms monitors and records data, which is then used to get valuable insights.

3. What are the key differences between Saas and agriculture IoT?

Saas

No physical equipment required to be placed on farms
Highly skilled labor not required
No hardware maintenance costs
Insights are accessible on laptop or PC screens and data is stored in the cloud
Yearly or monthly subscription plans available at low cost, low risk investment
High resolution satellite images for monitoring, GPS geo-tagging and weather analyzing
Holistic supply chain management
Very scalable, single application for management of multiple farms around the globe
Labor logs and chemical usage data available
Can be integrated with existing devices and IoTs
One stop solution for managing all the operations before and post harvest

IoT

Sensors, robots, drones, and cameras required to be placed on farms to monitor and operate
Requires high skilled field staff to implement and manage bots and insights of IoT lingua
Equipment are expensive and fragile
Recurring maintenance costs for hardware
Heavy initial investments
Computer imaging is done via sensor cameras and drones with manual operators
No supply chain management
Not scalable as each farm data has to be managed separately
No log info
Difficult to integrate with already implemented devices
Each equipment has a defined set of operation and not one can show all stats

4. What are the infrastructure requirements for adopting an intelligent agriculture system using IoT?

The high initial investments in sensors, drones, bots, and their setting up
Hiring well-trained field staff for operating and management
Connectivity to power to charge and operate the drones and robots
Hardware maintenance costs
Continuous connectivity to the internet

5. How easy is it to operate agricultural IoT?

IoT in agriculture uses smart farming which integrates sensitive physical hardware with analytical software. An analytical dashboard is mostly software that processes the data recorded by equipment. Hence, a sound technical knowledge of robotics and computer-based intelligence is a prerequisite for operating, maintaining, and understanding the insights of this valuable equipment.

6. How can I integrate IoT for multiple farm management?

Since IoTs require physical devices, each farm will need to install its own set of sensors and bots which will record the data related to that farm only. So, every farm will have its own separate dashboard for viewing the insights. Integrating and scaling IoT for several farms in a single platform is not possible, unlike SaaS agritech such as Cropin, which lets you integrate and manage multinational operations through a single platform.