The World Government Summit report published in 2018 Agriculture 4.0 – Future of Farming Technology states that four major developments, demographics, depletion of natural resources, climate change, and food wastage, dictate our future needs from agriculture. Using concepts such as big data analytics, precision agriculture, and Internet of Things (IoT) to measure agriculture quantitatively Agriculture 4.0 envisions better crop yield with less exploitation of the environment, lower input use and cost. Agriculture 4.0 emphasizes the organization of agricultural inputs (fertilizers, seeds, agrofuels, and herbicides) through distributed management practices.
In a scenario where the demand for agricultural output keeps increasing along with population growth and changes in people’s lifestyles, producing adequate inputs using limited resources is tough without the help of some innovative concepts. Man-Machine Interaction Specifically, Artificial Intelligence (AI) is the concept that can potentially transform current agriculture to produce more with fewer inputs models.
At the farm level, AI impacts crop production in many ways; particularly through proper distribution of seeds, fertilizers and other agrochemicals, automated irrigation scheduling, monitoring of soil, crop and animal health, monitoring of pests and diseases, and farm machinery positions in the field.
Applicability of AI in Agriculture
• Diagnostic application service: Identification of symptoms of water stress, pest and diseases infestation, etc. in farm fields. • Prescriptive Application Service: Soil health analysis and fertilizer recommendation or prescription of any other agricultural input • Advisory Application Service: Weather advisory and irrigation scheduling • Predictive Application Service: Yield prediction, disease and pest attack forecast (early warning system)
Potential of AI in Indian Agriculture
Agriculture growth driven by IoT: AI and IoT (sensors) can be used to create intelligent systems that can be embedded in machines to make it work with high accuracy. These technologies help in spatial and temporal assessment of individual plots or plants.
Image-based insight generation: Using satellite and drone imagery real-time alerts can be generated in precision farming. These various types of artificial intelligence systems not only save time but also increase proper safety and reduce many types of potential human error while improving effectiveness properly.
Computer Vision: Computer vision technology can be used to grade agricultural produce (grading of fruits and vegetables), enhance the quality of produce, and help farmers realise higher market prices and profits. This will help reduce post-harvest losses of perishable commodities. Also, the technology can find use in the procurement, storage, and distribution of food grains.
Identifying the optimal agronomic product mix: AI helps generate farm specific recommendations analysing various parameters such as soil health, weather forecast, seed type and pest infestation in the field. Although the recommendation includes the best choice of crops and technologies for a specific farm, it can be individually personalised based on farm requirements, local conditions and past data on successful farming.
Crop health monitoring systems: Remote sensing technology, hyperspectral imaging and AI create crop health monitoring systems that can monitor crop health from both time and effort perspectives. AI facilitates identification of pests, diseases and weeds problems and automates the management of these problems. AI-enabled agricultural production systems also predict future conditions and issue advisories for sowing, pest control and commodity pricing.
Smart irrigation systems: Smart irrigation involves providing the right amount of water at the right place at the right time for the right crop to improve crop yield. Using sensor based automated irrigation systems issues associated with the low irrigation efficiency of Indian agriculture (around 38%) can be solved to a greater extent. Moreover, automated irrigation scheduling is possible using machines trained on historical weather patterns and soil quality of the terrain, as well as the kind of crops grown.
Agricultural Risk Management: Climate change issues such as increased temperatures, erratic rainfall patterns and other associated problems have increased the importance of AI systems in agriculture in recent years. AI based technologies support farmers in managing risk and uncertainty in agriculture by facilitating farmers’ preparedness to handle these risks efficiently.
Natural Language Processing (NLP) for Agricultural Advisory: India is a multilingual society and most of the farmers are illiterate. A lot of content failed to reach the desired people due to lack of human-resource to convert it into the end-user’s language. This gap can be effectively filled through Natural Language Processing.
Attract youth to agriculture: The reduction in agricultural labour force due to migration of youth to other jobs can be well addressed using AI based agriculture. AI based technologies will attract tech-savvy youth, reduce drug addiction and save natural resources and agriculture
Barriers to AI adoption in Indian agriculture
Policy: Inconsistent data governance and data rights regime, and lack of enforcement of data rules, privacy, and transparency. Trust Culture and Society: Barriers recognized and prioritized i.e., risk-aversion and resistance to change, lack of uptake of technology, and inadequate support from universities in data digitization and digital agriculture.
Education and skills: Factors identified as inhibitors to AI adoption include language barrier, high illiteracy rate, and digital divide, lack of formal, non-formal, and informal education in data engineering, data analytics, data science, and inadequate proficiency.
Information and Communication Technology (ICT) and Data Infrastructure: Constraining factors related to ICT and data infrastructure include inadequate ICT and data infrastructure (data collection, transmission, storage, processing, cleaning, and analysis tools, missing historical data and inadequate digitization and limited access and poor internet connectivity, minimal and erratic power supply, fragmentation of data and lack of data standards.
Finance and Investment: Constraints related to finance and investment are inadequate capital to invest in ICT tools and data infrastructure, lack of funds for maintenance of existing infrastructure, lack of public investment to bridge the gaps in data engineering, data analysis and data science education, low awareness and lack of clarity about the return on investment in AI systems.
Future of AI in India
The applications of AI based tools in agriculture have been worked upon by many start-ups in this sector to help farmers with better productivity and profitability from agriculture. India’s burgeoning start-up ecosystem is actively playing its role in developing the agriculture sector. Since, the opportunity in agritech exists across the value chain from improving farmers’ access to markets, inputs, data, advisory, credit, and insurance; India can tackle the issues associated with the adoption of AI-based technologies by providing a suitable ecosystem to these start-ups to access data and market. Moreover, the National Strategy for Artificial Intelligence released by NITI AAYOG in June 2018 identifies agriculture as one of the focus areas.
India should use sophisticated deep technologies in the future to maximize agricultural production from limited resources. Deep-tech innovations support farmers to grow crops even in arid areas with high resource use efficiency, using technologies such as AI and ML, robots, temperature and humidity sensors, aerial images, and GPS. An important aspect of AI is the response time and accuracy of the system. Even the behavioral changes in field crops due to changes in microclimate conditions can be analyzed in the fastest response time with accurate information. However, concerns about the durability of AI technologies may discourage farmers from its adoption as technologies are changing very fast in this digital age, and changing the devices and sensors quickly with the advancement in the adopted technologies is not going to be affordable for the small-scale farmers of India. It is certain that digital innovation can transform Indian agriculture if there are proper efforts to convince the vend users about the potential of AI based technologies in the agriculture sector; not only from the user and consumer side but also the governance and policy side.
Read Also:
- Challenges And Opportunities Of Artificial Intelligence In Agriculture
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- Role Of Artificial Intelligence In Indian Agriculture
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- Advantages And Disadvantages Of Robots In Agriculture
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- Artificial Intelligence (AI) In Agriculture: Current Status And Future Need
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