If you are an agri-input manufacturer, you are the spine of a sector that feeds billions of humans, pumps economies, and fuels growth for humanity. Without seeds, fertilizers, agrochemicals, and farm machinery, farmers, agri-players, government organizations, essential services, and food corporations cannot increase productivity, reduce wastage, and battle the volatility of weather and markets.
You are a significant cog in the wheel that runs the world, in a sense. And for this cog to run smoothly and efficiently, its supply chain management must be intact. There has to be a holistic, real-time, and precision-backed approach to demand visibility here. Otherwise, the domino effects of errors and misinformation can go long and deep into the entire agriculture chain – affecting not just farmers, agri-companies, and governments but billions of people. In fact, without self-sufficiency and self-reliance in food production, many ecosystems would crumble due to the delicate forward and backward linkages between this sector and others.
To get this precision and accuracy in demand sensing and supply chain management, one would need help from artificial intelligence (AI) and machine learning. Countries like India have been chasing modernization and advanced research for many years, but this sector would need support from intelligent machines to leap-frog here. Intelligence and data-driven forecasting are crucial pillars enabling India’s trajectory with speed and direction here. That’s not just a choice anymore but an imperative of sorts, especially with the current state of low yield, poor forecasting and agricultural productivity.
Patches and rocks – agriculture without AI now
With 11.9 percent of the Gross Value Added (GVA) in global agriculture ($3,320.4 billion), India is a crucial area here. More so when, despite a 50 percent increase in crop production in the last ten years, the food production growth rate will not suffice to feed the increasing global population. It is a population projected to reach approximately ten billion by 2050 if we go by some estimates from the World Bank. To add to that pressure, the Indian agriculture landscape suffers from many endemic issues like,
- Land fragmentation
- Poor sustenance approaches
- Access issues to credit, technology, and data
- Too many intermediaries
- High-input, poor-output approaches that lead to soil depletion and low productivity
- Lack of data sets at many levels
- Loopholes in market linkages
- Challenges in price discovery and price visibility
- High wastage due to ecosystem gaps in farm infrastructure
Studies have pointed out that adopting appropriate mechanization of farm operations can heighten production and farm productivity by 10-15 percent and improve cropping intensity by 5- 20 percent. As a PwC report on ‘Redefining Agriculture through AI’ reminds us, the present digitalization market of agriculture is valued at $204 million. It is expected to grow exponentially owing to the increasing adoption of AI and remote sensing technologies. The domain of AI/ML has made remarkable strides globally and can account for the largest share in the usage of technology in agriculture. The emergence and growth of more than 1,400 AgriTech start-ups is a good sign.
It is high time we embrace AI and machine learning for intelligent farming practices, predictive analysis, crop health management, enhanced quality, traceability, etc.
We can see here experts pointing out how machine learning (ML) and AI technologies are needed to build data-centric models that can predict any weather event, pest attack, harvest, and crop yield more accurately. It adds that many AI models also provide immediate, low-cost, affordable, portable, and accurate solutions for measuring soil moisture, nutrition, crop health, quality assessment, etc. Therefore, these technologies are required to build data-centric models that accurately predict any weather event, pest attack, harvest, and crop yield.
A promising harvest ahead – with AI
AI and machine learning (ML) are showing massive potential, with investment and expenditure trends expected to triple by 2025 to $15.3 billion. According to some forecasts, the global market size for AI in agriculture is projected to reach $8,379.5 million by 2030.
There’s a lot that can be transformed, disrupted, and elevated with the use of AI and machine learning:
- Use for remote sensing and weather station data
- Use for predictive analysis on sowing, scheduling irrigation, crop health management
- AI-powered pest control
- Creation of market linkage platforms to enhance quality and traceability
- Better connects in the value chain and easier access to the global market
- Minimization of supply chain losses
- Improvement of quality, traceability, logistics, and distribution
- Smart/responsible sourcing technologies
- Predictive market demand forecasting and prescriptive intelligence for route-optimization
- Data-driven simulation modeling of food systems
- Use of robotics, sensors, and soil sampling for proactive analysis
- Use of autonomous vehicles, wearables, button cameras, robotics, and control systems for better crop health
- Field analysis and geo-fencing
- Analysis of data like rainfall, topography, soil elevation, slope aspect, wind direction, flooding, and erosion
- AI for irrigated landscape mapping, crop health assessment, and irrigation amendment analysis
- ML for land degradation assessment and erosion remediation
- Real-time AI for yield and quality assessment
- Predictive analytics for crop sustainability
- Elimination of weeds by recognizing species of plants/crop
- Timely detection of crop infections and diseases
- AI for intelligent harvesting and pricing decisions
- AI-based crop monitoring
- RFID for end-to-end traceability in supply chains
- Regenerative agriculture that improves soil carbon sequestration
- Enhancement of the marketability of produce in terms of quality plus access
- Livestock facial recognition
- Machine vision to study crop and weather patterns and water monitoring
- Soil health scans
- Smart irrigation and yield management
- Enhancement of input-output profitability ratio
- Robotics for remote agri-operations
- Water budgeting
- Data-based insurance coverage
- Impact on post-production areas like harvesting, storage, and traceability
- Digital connectedness through AI platforms and better market access
- Facilitation of input market linkages
- Better integrated farm-to-market supply management chains
- Controlling costs of fertilizer distribution
- Mapping of soil micronutrients, temperature, and pests for better information on pesticides and other inputs
AI and ML can be beneficial for agri-input players in areas like:
- Sales visibility
- Geo-tagging
- Farm records and data integration
- Crop monitoring
- Soil health management
- Scheduling and maintenance of irrigation
- Pest and disease control
- Advisory on weather volatility
- Procurement management
- Quality analysis and assurance
- Precision farming-enablement
- Yield forecasting
- Data-based supply chain interventions
- Digital marketplaces and subscription models
- AI for real-time demand visibility
Watch out for weeds
While ML and AI are expected to report the most significant shares in the AI in the agriculture market, a lot remains to be addressed. The PwC report also underlines that this dynamic ecosystem represents only a fraction of Indian agriculture and has reached less than 20 percent of Indian farmers. With a total addressable agri-tech market in India of about $24.1 billion but less than one percent penetration in view of India’s potential- a lot remains to be done.Everything about AI and ML hinges on data. And only a good partner with solid technological and domain expertise can help you iron out some fundamental issues here, like data availability, standards, accessibility, security, sovereignty, quality, authenticity, and disambiguation. Especially as everything in agriculture depends on quick, actionable knowledge. You would need to tap a solution that is both scalable and resilient. It should be able to handle large volumes as well as heterogeneous segments of data – making everything interoperable, seamless, and integrated.
