News highlights:
The government of India has taken various initiatives using the Internet of Things (IoT) and Artificial Intelligence (AI) in agriculture.
Key takeaway:
Applications include Precision Farming, Agricultural Drones and Hopping systems, Livestock Monitoring, Monitor Climate Conditions, Smart Greenhouses, AI and IoT-based Computer imaging etc.
Artificial Intelligence in Agriculture:
- Detecting agriculture Defects:
- AI technology helps detect diseases in plants, pests, and poor plant nutrition on farms.
- AI sensors can detect and target weeds and then decide which herbicides to apply within the right buffer zone.
- This helps to prevent over-application of herbicides and excessive toxins that find their way in our food.
- It would increase productivity by introducing precision agriculture.
- Shortage of labour Challenge:
- With fewer people entering the farming profession, most farms are facing the challenge of a workforce shortage.
- One solution to help with this shortage of workers is AI agriculture bots. These bots augment the human labour workforce and are used in various forms. For example:
- These bots can harvest crops at a higher volume and faster pace than human labourers, more accurately identify and eliminate weeds, and reduce farm costs by having a clock labour force.
- Additionally, farmers are beginning to turn to chatbots for assistance. Chatbots help answers various questions and provide advice and recommendations on specific farm problems.
- Real-Time Farm Data:
- Farms produce hundreds of thousands of data points on the ground daily. With the help of AI, farmers can now analyze a variety of things in real-time such as weather conditions, temperature, water usage or soil conditions collected from their farm to inform their decisions better.
- Farmers are also using AI to create seasonal forecasting models to improve agricultural accuracy and increase productivity.
Need for IoT and AI in the Agriculture Sector:
- Lack of technology adoption:
- Even as agriculture remains a priority sector accounting for the livelihoods of around 58 % of the country’s population, the adoption of technology in the sector is at a transitory juncture. It faces several challenges across the value chain.
- These challenges require disruptive interferences, which can be provided by technological solutions such as the IoT and AI etc.
- Higher Production:
- Adopting AI technologies can pave the way for higher production with the optimum utilization of available resources and facilitate predictive analysis, crop health management, enhanced quality and traceability, among others.
- Major Trend:
- Adopting innovative and transformative intelligent farming practices in the country is gradually becoming a significant trend.
- Globally technological advancements in recent years are re-engineering both the upstream and downstream segments of the Agri value chain, which makes it essential to adapt innovation in Agriculture.
- Overcome the Challenges:
- Cutting-edge technologies in AI such as IoT, ML (Machine Learning), cloud computing, statistical computing, deep learning, Virtual Reality (VR) and Augmented Reality (AR) can enable the Agriculture Sector to overcome the challenges of productivity, quality, traceability and carbon emission with enhanced profitability.
Challenges in its adoption and implementation:
- Policy upgrade:
- Yet-to-mature data governance and data rights regime. Lack of enforcement of data regulations, privacy and transparency.
- Trust deficit:
- Risk-aversion and resistance to change, lack of trust in technology and insufficient support of universities and academics in data digitization and digital agriculture.
- Language Barrier:
- Language barriers including high illiteracy rates and the digital divide. Lack of formal, non-formal and informal education in data engineering, data analysis and data science and insufficient proficiency.
- Tech connectivity:
- Lack of supporting ICT and data infrastructure includes data collection, transmission, and insufficient digitization. Deficient telecommunication networks, poor internet connectivity, low bandwidth, irregular and erratic electricity supply, and lack of data standards.
- Finance and investing:
- Insufficient capital investment in ICT devices and data infrastructures and lack of public investment to bridge gaps in data engineering, data analysis and data science education.
- Lack of awareness:
- Lack of awareness and clarity regarding return on investment in Al systems and no financial assistance schemes for small farms to adopt and deploy ICT devices and embedded systems.
Conclusion:
- With the recent reforms in the Agriculture sector, there is a likelihood of increased investments in contract farming and infusion of technology for better yields and productivity.
- This will further push the adoption of AI in agriculture. Further, To help these AI solutions scale, increased investments from the public and private sectors are needed.
Pic Courtesy: Freepik
Content Source: PIB