DATA FARMING - THE FUTURE OF AGRICULTURE
Agriculture is one of the major industries of Indian economy. The estimated contribution of the Indian agriculture industry to GDP is almost 15% which is approximately $ 400 billion and has over 60% of the Indian workforce associated with it and allied activities. India is one of the largest food producers globally, having more than 150 million hectares of arable land which is the second largest in the world.
Farming in India and also across the globe is witnessing major challenges like climate change, increase in consumption, labour shortage and lack of data supported farming activities. These challenges have propelled the stakeholders to reconsider how farming is done and to take initiatives to bring about transformation in the sector. This is one of the key driving factors in introducing modern Information and Communication Technology (ICT) in assisting farmers with data-driven insights to help them improve their farming process. Data Farming is a small step in this direction.
Technology for Agriculture
Agriculture industry has undergone mechanisation over the years from the introduction of tractors for ploughing and use of sprinklers and drip systems for irrigation. There has also been significant improvement in the quality of fertilizers, pesticides and seed technology to improve the farm produce. However, even today, the use of data-driven farming practices is limited in the field of agriculture, which not only makes farmers rely on traditional intuition for deciding their farming activities but also significantly reduces the productivity of the agricultural undertaking. The modern farmer can be equipped to make better decisions and improve their productivity with the help of modern ICT-supported, data-driven solutions tailored to their needs.
The challenge and the opportunity before us today as well as in the future demand inventing solutions which can help the agricultural sector enter a new era. There are different fields of modern ICT technologies which have potential to be the change drivers such Internet of Things, Computer Vision, Data Analytics, Artificial Intelligence and Smart Robotics. As a starting point for application of research from these fields of science it is imperative to have a relevant and adequate data base. Data Farming, as its name suggests, would be utilised for this end.
Modern digital technology and data-based decision making is a practice not familiar to many farmers. This limited awareness and adoption of technology for farming poses a significant challenge for modernisation of agriculture and also sourcing critical data. The current farming practices and farm technologies therefore need to be re-engineered to enable cultivation of data along with crops. Considering the limitations of humans to scout and source such data on a large scale, intelligent connected devices offer some of the best present day solutions. These devices would effectively help source data from farms which are characterised by large scale and diversified data patterns.
AI for Farming
According to John McCarthy, AI is “The science and engineering of making intelligent machines, especially intelligent computer programs”. AI works at its best by combining large amounts of data sets with fast, iterative processing and intelligent algorithms. This allows the AI model to learn automatically from patterns or features in vast data sets. Farming is a function of extensive interdependable factors. To measure and analyse such huge complex data sets, different models of AI offer better solutions. In order to make the AI models effective, it is important to have connected intelligent devices which would enable them to undertake the data sourcing function effectively. The more the quantity and quality of the data, the better the performance of the AI model and its recommendations to the farmer.
Primary Data Sourcing Segments
Visual and sensory data: Visual and sensory data relating to health of the crops, soil and weather patterns form primary data sets. Computer vision and deep learning algorithms can be leveraged for enabling smart devices to source the visual data from the farms and processing the data to study the crop health, pest identification and pest infestation patterns on the crop. Sensory data like soil quality, wind speed, humidity and air composition help in supporting the imagery data to provide a holistic insight into the farm’s health status.
Robotics: Primary application case for robotics in the context of data sourcing is improved access to the crop and better data taking ability. It enables quality data acquisition, precise measurements and in certain cases, it is better than humans in conducting these tasks thanks to advanced sensors and improved computational hardware and software. Robotics technologies include engineered products like drones and autonomous vehicles which would be involved in data acquisition and surveying the farms.
Intelligent management: The term intelligent management in the context of data farming means having a digital app-based platform that enables the management of the smart devices installed in the farm and their operations. The different functions performed by the management system would consist of monitoring and controlling the operations of the smart devices, managing connectivity, display data-driven insights of the farming activity and the power status of the smart devices. This can be achieved with the help of data processing in cloud servers which will form the backbone of such an intelligent management platform.
Data Farming project at Kanan Park
The data farming project at Kanan Park is focused at deploying intelligent devices which can help in building a data set of critical farm related data and use this data to help the AI model present deep insights of the farming activity to the farmer. This is the first of many steps in the mission of making farming modern, data-driven and more productive. As a part of the first phase, our focus is on building a system that generates a reliable database of the different aspects like crop health, pest identification, infestation, soil health and weather patterns, all specific to a farm. This would be followed by processing this data using AI models for giving farm-specific insights to the farmer.
The ultimate goal is to focus on different crops and the specific factors on which these crops are dependent and to develop a matrix of these factors and the corresponding crops to feed the data to machine learning and deep learning models and generate AI driven insights for better produce of these crops.
The project is designed for developing smart devices that would help in sourcing and building databases specific to a given farm. This would involve engineering a combination of devices which utilise technologies like computer vision, robotics, IoT based connected devices and cloud services etc. The goal is to achieve better than human level ability to source data from farms.
The data sourced would be subjected to different AI models which would provide efficient diagnostic and predictive analysis to the farmers.
The Data Farming project is a part of a series of projects which converge into a bigger mission of developing intelligent farming platform. The driving philosophy of the mission is to be a change agent for inventing and introducing modern ICT technologies to the agriculture sector.
This surely promises to be an exciting journey!
If you are interested in being part of this get in touch with us.
Careers at Kanan Park
We are looking for passionate team members willing to challenge themselves and work on ambitious projects. If you have the passion to be part of a dynamic team focused on innovation and collaboration we welcome you to explore this opportunity and join our energetic team!