Digital Farming – Digitization of Agriculture Farming
What is digital farming?
Digital farming can be defined as consistent application of the methods of precision farming and smart farming, internal and external networking of the farm and use of web-based data platforms together with data analyses. In other words digital farming is about analysis of data gathered on dashboard through technology and deploying the information for more precision in agriculture input and output cycle.
It starts with describing the asset in the digital domain. Data of each plot are analysed to provide information on soil, weather and crop growth patterns and to give actionable geographically relevant timely insights to prevent losses and optimize productivity of each plot on farm. Farmers get their queries solved and manage supply chain directly through applications on their phone. Through pre-harvest and post-harvest management of farms, digital farming aims to takeover all the aspects of farming from farm to fork.
How Digital farming is done?
Digital Farming can be done through installation of network connected ‘smart’ devices as part of Internet of Things (IoT) or
they can be Software as a Service (SaaS) based agriculture technology. Software as a Service is a software distribution model in which a third-party provider hosts applications and makes them available to customers over the Internet.
Internet of things in agriculture comprises use of sensors, drones, robots and cameras. Sensors, cameras and robots are installed on
the farms and they record the data. Drones can be used as pay per services or can be bought and stationed in farms. The IoT equipment needs to be connected to an analytical dashboard for analysis of data. IoT is used for field related data only. They can’t help manage the overall farm activities and show the data in terms of financial gains or losses. Since IoT utilizes hardware, it requires solid technical knowledge for operating the equipment along with high maintenance and setup cost. The high capital input cost is keeping IoT out of reach of most farmers. For low input cost Software as a Service (SaaS) is the more economical and scalable way to upgrade farmer to digital farming. Many leading agritech companies utilize SaaS machine learning and satellite monitoring for performing predictive analysis and deliver customized reports and actionable insights directly to farmers’ screens. SaaS is gaining popularity pretty quickly among businesses as they can access and manage their overall farm operations with one software and this is a low risk investment with monthly and yearly subscription.
For example in Bihar and MP there is Software as a Service (SaaS ) companies like CropIn that is offering one-stop solution for driving
Farm Management, Traceability, Sales Management and Risk Management in the Agri sector.
Another example is stellapps technologies which does mobility and data analytics to improve agri supply chain parameters including
milk production, milk procurement, cold chain, animal insurance and farmer payment which gathers data via sensors that are embedded in milking systems, animal wearable, milk chilling equipment & milk procurement peripherals and provide insights for management of cattle, organization of dairy farms and logistics management.
How digitization of agriculture will help Financial Institution?
Suppose a farmer comes and asks for a loan, the bank can use agritech SaaS to look into the farmer’s records relative to the yield and profits in last ten years or five years. Based on the historical cropping patterns yield is estimated.
This helps banks and insurance in several ways.
Firstly, it helps the farmers to increase their credibility. If the records and predictions show that the farmer has been performing well and yield will be good, the loan or insurance amount will be higher. Currently the same insurance amount or loan is given to all farmers irrespective of their individual performance.
Secondly, the banks and insurance companies will be benefited as the data obtained is highly accurate; hence can help stop bad lending. Third, bank can see through SaaS, how much is the crop sown area and accordingly how much money can be disbursed. Apart from that bank can see allied agriculture activities and monitor the assets like dairy – cattle, milk procurement status, milking etc.
Conclusion
Now the face of agriculture is changing. Tractors and harvester combines are highly automated and crammed with a vast array of
sensors. Unmanned Aerial Vehicles (UAVs) and even orbital satellites are becoming very useful for agriculture. The delivery of highly detailed field observations straight to a farmer’s tablet computer will soon become a standard in agriculture and this will surely reduce the input cost, improve scalability / output and contribute significantly to the government vision of doubling of farm income by 2022.
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