An Introduction to Smart Farming
Smart Farming is focused on the use of data acquired through various sources (historical, geographical, and instrumental) in the management of farm activities. Technologically advanced doesn’t essentially mean that it is a smart system. Smart systems differentiate themselves through their ability to record the data and make sense of it. Smart farming employs hardware (IoT) and software (Software as a Service or SaaS) to capture the data and give actionable insights to manage all the operations on the farm, both pre- and post-harvest. The data is organized, accessible all the time, and full of data on every aspect of finance and field operations that can be monitored from anywhere in the world.
DIFFERENCES BETWEEN TRADITIONAL AND SMART FARMING
Implementing robotics in agriculture and other smart farming techniques brings in several benefits over conventional farming strategies. Some of these include exercising increased control over production processes, which in turn enhances cost management and reduces waste generation. In addition to this, smart farming, through the implementation of new technology in agriculture, makes it easier to trace anomalies in crop growth as well as livestock health.
Take a look at some of the key differences between traditional and smart farming practices.
UNDERSTANDING THE IMPACT OF NEW TECHNOLOGY IN AGRICULTURE: HOW DO AUTOMATION AND ROBOTICS HELP?
IoT in agriculture involves sensors, drones, and robots connected through the internet which function automatically and semi0automatically performing operations and gathering data aimed at increasing efficiency and predictability. With increasing demands and shortage of labor across the globe, agriculture automation and 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 agri-bots more notable.
The world is in the early stages of an ag-robotics revolution with most of the products still in R&D and trial phases.
A Closer Look at Some Modern Agriculture Tech
Semi-automatic robots with arms can detect weeds and spray pesticides on the affected plants, preventing extensive damage as well as reducing the overall pesticide costs. These robots can also be used in harvesting and lifting. Heavy farming vehicles can also be navigated from the comfort of homes through phone screens to perform tasks and GPS can track their positions at any time.
Drones equipped with sensors and cameras are used for imaging, mapping, and surveying the farms. They can be remotely controlled or they can fly automatically through agriculture 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 and yield prediction, and much more.
IoT-based remote sensing utilizes sensors like weather stations placed on farms to gather data, which is then transmitted to analytical tools for analysis. They monitor the crops for changes in light, humidity, temperature, shape, and size. 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. The analysis of the soil quality helps in determining the nutrient value and drier areas of farms, soil drainage capacity, or acidity, which allows for adjusting the amount of water needed for irrigation and the opt most beneficial type of cultivation.
Computer imaging involves the use of sensor cameras installed at different points on the farm or drones equipped with cameras. The images they capture undergo digital processing to derive meaningful insights from them. They are used for quality control, disease detection, irrigation monitoring, and sorting and grading the produce after harvest. Image processing using machine learning incorporates comparing images from a database with images of standing crops to determine the size, shape, color, and growth, therefore controlling the quality.
ROLE OF SAAS-BASED CLOUD SOFTWARE IN SMART FARMING
Cloud-based software is used for the management of financial and field activities of farms. Prior to computers, farmers maintained data manually by keeping lengthy records on papers. This method was prone to human calculation errors. After the computer boom in the 1980s, it was not long before finance software such as Money Counts came to market. They intended to replace the spreadsheets to maintain the financial data.
The biggest challenge that farmers faced was the inability to manage field data. To address this, around the mid-2000s, satellite image use with tools like Raven Receiver for field zone tracking became widely used. Farmers had to implement and coordinate different tools to manage complete farm operations.
With constant improvements through the years, agritech SaaS has become an all-in-one tool for the management of all these activities and more in one place using a single tool. A good example would be Cropin. As a global agritech provider, we have worked with 200+ public and private sector organizations worldwide, utilizing deep learning, satellite monitoring, cloud computing, and other technological advancements to collect, analyze data, and manage all the activities from farm to fork with streamlined farm solutions.
Farming companies - Output predictability
Agri-input companies - Production forecast and quality maximization
Financial lending institutions - Risk management
Crop insurance providers - Risk coverage
Food production companies - Quality control and compliance
Government advisories - Grass-roots level benefits for farmers
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.
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.
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.
What are the benefits of smart farming using SaaS solutions?
- Readily available and accessible management through smartphones, tablets, and PCs
- Facilitates logging and management of alerts (pest infestations, crop diseases, etc.)
- Incorporates end-to-end solutions for farm-to-fork traceability
- Robust and flexible system for farm management
- Geo-tagging for accountability and accurate predictability
- Configurable package of best practices for each crop variety
- Agronomic advisories based on satellite imagery and weather data
- Higher yield output as input usage is optimized and constantly monitored
- Improved quality due to compliance with food standards and nutrition tracking
- Reduced wastage owing to the precise application of resources and thus reduced production costs
- Traceability and output predictability
- Comprehensive reports and insights
What are the differences between IoT and SaaS solutions to consider before investing in one?
- It provides a one-stop farm solution for managing all the operations pre- and post-harvest.
- Highly-skilled labor is not required.
- No equipment other than mobile devices is required on farms.
- Labor logs and chemical usage data are available.
- No devices are required to be placed on farms.
- No hardware maintenance costs.
- Insights are accessible on a laptop or PC.
- It allows holistic supply chain management.
- Data is stored in a secure cloud for easy accessibility.
- It uses publicly available, high-resolution satellite images for farm monitoring and weather data.
- Only a single application is required to manage multiple farms around the globe.
- It is scalable for multi-country implementation.
- Can be integrated with existing devices, IoT devices, and legacy solutions.
- Yearly or monthly subscription plans are available at a low cost.
- It enables crop production almost anywhere, including urban rooftops.
- It helps minimize the usage of scarce resources like water, energy, and land.
- It enables the automation of processes like sowing seeds, watering, crop monitoring, and harvesting.
- It requires highly-skilled field staff to implement and manage the devices.
- The devices are expensive and fragile.
- It demands heavy initial investments.
- Sensors, robots, drones, and cameras need to be placed on farms to monitor and operate.
- It involves recurring maintenance costs for the hardware.
- Computer imaging is done via sensor cameras and drones with manual operators.
- Each piece of equipment has a defined set of operations and can be used only for specific tasks.
- The use of IoT devices is not scalable to larger regions; each farm’s data has to be managed separately.
Both SaaS and IoT have their own advantages and disadvantages. To know how each can address your concerns and what could be the best fit for your business, read more here.