Smart farming is an emerging concept that talks about moving away from traditional farming methods by aptly using technology. It includes the use of AI, robotics, IoT and drones merged with GeoSpatial AI techniques to improve the quantity and quality of the harvest. At the same time, smart farming also focuses on optimizing human labor.
The concept will redefine the agricultural industry completely. How? Let us figure that out as we explore the varied aspects of smart farming and how you can benefit from it.
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Smart farming is a concept that aims at making the activity reliable, sustainable, and predictable. Though it remains one of the top objectives for the industry, achieving that is often not possible with traditional farming methods. But that becomes a thing of the past with technologies like IoT, sensors and actuators, robotics, and drones.
Smart Farming is also called precision farming and is one of the vital use cases of spatial data science. It includes analyzing the LandSat data and satellite imagery data of fields to make predictions about crop health, yield performance, and weather forecasting.
Estimates have shown that almost 80% of framers in the US and 24% in Europe are already using smart farming technology. When we talk of population and hunger, a UN study shows that the global population will increase by 2 billion by 2050. It will require a 60% increase in food productivity to meet the supply-demand.
AI and ML are the technologies for the future, and they can help close this gap. The AgTech industry is making use of, satellite imagery data, Landsat data, IoT data, and insights and driving through spatial analysis techniques for enabling farm management and enhancing productivity. It will ultimately improve crop production and agricultural efficiencies, and reduce food production costs.
There are several AgTech start-ups today that aim at giving benefits to investors in the form of farmland in their investment portfolios. They are also working towards structuring it in the best way possible.
As smart farming continues to evolve, the AgTech industry will continue to witness newer investment opportunities. There is a scope for several equity-based arrangements that can redefine the way investors pump in finance in this sector.
Smart farming is much different from traditional farming methods. Let us explore the traditional farming vs. smart farming debate.
Traditional methods involve uniform practices for crop production across the region. Smart farming methods go a step ahead and analyze suitable crops and their water requirements to ensure resource optimization.
In traditional farming, the field data gets maintained manually, which could lead to errors. There is no scope for the detection of soil problems in advance. Smart methods of farming help overcome this problem and prevent financial losses.
Smart methods of farming enable farmers to use fertilizers and pesticides wherever necessary. There is no such scope in traditional farming. Fertilizer distribution systems give crop recommendations based on NPK (nitrogen, phosphorus, potassium) values and yield prediction based on soil samples and farm area.
Smart methods of farming help in detecting affected areas for taking corrective steps. Zone detection, geo-tagging, and related techniques are not possible in traditional farming. When we talk of the affected areas, satellites and related imagery data can monitor soil conditions like moisture level and ground heat for identifying ideal scenarios to grow crops. Furthermore, satellite imagery can also help in monitoring the natural environment of a farm for better targeting of fertilizers and pesticides.
Traditional methods lack technology, so there is no way farmers can predict the weather. Technology in smart methods helps in the analysis and prediction of weather to prevent crop damage from unseasonal rain or drought. Similarly, pest attack prediction models also enable planning in advance for the anticipated attack.
Tools like satellite imagery data and Landsat data are redefining the farming and food industry landscape. They augur well for sustainable means of agriculture, which can ensure food security. Definite food production will also be possible without affecting the environment. However, it is not without a fair set of challenges. Let’s take a look at them.
It is possible to enjoy the true benefits of IoT only when computational power increases and energy consumption in sensors decreases. Only then will the devices become energy autonomous. There is further a need for devices to have smart functions related to self-configuration and self-management.
Keeping in mind the financial aspect, there is also a need for these sensors to be cost-effective. Sensors with RFID and NFC tags may not always be feasible to incorporate if the cost of the food product is on the lower side. With lower profit margins, there is also a need to focus on device characteristics.
Devices with proprietary architectures and fixed standards pose a challenge for widespread adoption. It is because of issues like compatibility factors with other systems. There is a challenge to find systems with standards that do not require any modifications.
IoT systems need to have the best connectivity options for their optimal performance. As IoT systems work in farms situated in rural areas, there is a challenge regarding connectivity. It necessitates the need for devices that can function even with less power communication.
IoT devices can generate a lot of data, so extracting meaningful insights from them is crucial. However, the level of data generated is still at a nascent stage in the agriculture industry. The present data largely suits decision-support systems. However, advanced information like production planning and predictive modeling is still a distant possibility.
Whenever there is data, concern about its security always remains. Critical information related to predicting crop yield and soil fertility needs protection. Cloud-based services are ideal for processing and storing such data. When it is also about data aggregation from various farms, care needs to be taken to ensure individual farm data remains secure.
Here is a list of various devices that are generally used for analyzing data for smart farming.
Here is a list of some of the best techniques involved in smart farming with AI.
The equipment has a communications controller or computer linked to the agricultural machine. There is a connection provided to data buses and other controllers of the machine.
The connection between controllers and data bus ensures that alerts from sensors get communicated to the controller. It further generates snapshots and summary reports that go to central information servers. The information finally reaches the user on the application.
Sensor-based mapping has made remote sensing an ideal data source for applications and their study. The sensors that provide information to the application on a smartphone prove a lot beneficial for farmers. In the past, there was no such possibility to get advanced data or assistance from agricultural experts.
It is possible to analyze historical as well as current farming data to prepare for future yield. Satellite imagery helps in understanding and managing the natural environment of farms, which gives them cues for sustainable agricultural practices. Yield maps further allow for better targeting of fertilizers for improved crop production standards. Data-driven decision-making is crucial to improving the bottom line. So predictive analytics helps farmers make informed decisions for better profits.
It is possible to monitor crops remotely through remote sense data and field data. It helps check vital parameters like crop condition, yield, and productivity, cropping intensity, planting status, and drought prediction.
The production of crops gets affected by uneven weather patterns. Statistical weather forecasts make use of historical climate data to show the relationship between different periods. So, it is possible to predict winter temperatures based on the data of summer temperatures. Any fluctuation in the former will likely hold true for the latter.
Smart farming makes use of data analytics to collect information from various farming activities. It helps in creating algorithms for better and sustainable farming. Here are the various components of predictive analytics in smart methods of farming.
Cloud software in farming supports large-scale gathering and retrieving of data from multiple sources. Data can be of various types like soil conditions, crop mapping, crop environment monitoring, satellite images, yield information management, and much more. They offer insights with excellent speed and accuracy. As the data remains stored in the cloud, it is accessible at any time. Farmers can use historic data to overcome problems related to crop production.
The analysis of data helps in gathering insights that aid better decision-making. Data related to water availability, soil moisture content, and GIS are some examples. This information can help the AgTech industry understand the optimal water requirements, soil moisture levels, and much more.
If there are any discrepancies, the system will also alert the concerned person to take corrective actions. The system will also alert about the possibility of a pest attack.
It is a vital aspect of predictive analytics. In earlier times, when storage required physical infrastructure, it was tough to maintain it. Any fault with the hardware meant that data can easily get compromised. However, cloud systems eliminate this problem in a modern agriculture scenario.
There is no need to invest in costly hardware now. The data additionally remains accessible to anyone at any time on their smartphones. When quality data is available in large amounts, it gives better insights for improved decision-making.
Here are some advantages of using SaaS solutions in smart methods of farming.
The future ahead in the farming industry is about making the apt use of technology for improved yield. Technological development and innovation will continue to redefine farming practices. IoT-based solutions are ideal for improving the quantity and quality of crop production.
If you are looking to get started with smart framing data solutions, Gramener’s award-winning spatial analytics innovation can help.
Contact us for custom built low code data and AI solutions for your business challenges and check out spatial analytics solutions built for our clients, including Fortune 500 companies. Book a free demo right now.
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