Artificial Intelligence (AI) in Agribusiness is growing in emerging market economies.

It is the science and engineering of making machines intelligent, especially intelligent computer programs. AI techniques have seen rapid progress over the past decade.

AI can help improve the resilience of farming methods, reduce the cost of quality inputs and services to underserved farmers, and improve market access for smallholder farmers.

The food and agriculture-related Sustainable Development Goals (SDGs) are challenging, with 820 million people hungry today.

Investing in new technologies will be critical to meeting these challenges, with UNCTAD estimating that the food and agriculture SDG investment gap is almost $200 billion per year, through 2030. 

Agtech can help farmers use scarce resources more efficiently, access advanced inputs, and trace the origin and quality of their produce.

Artificial intelligence technologies have been incorporated into agtech business models to reduce the cost of serving smallholder farmers and improve the efficient and sustainable use of resources. 

These technologies have become commercially feasible for agtech in recent years through advances in big-data analytics, increased computing power, cloud-based storage, and cost reductions in satellite imagery, remote sensors, and other hardware.

Despite its advantages, AI technology has many barriers to adoption, particularly in  emerging market economies.

The distribution of venture capital into AI technologies closely tracks total venture capital flows, and the latter can be used as a proxy for interest in AI. Agtech firms using AI have received $6.7 billion in venture capital flows over the past five years, including $1.9 billion in 2018 alone.

Artificial Intelligence's profound impact on agribusiness cannot be overstated. It holds the key to unlocking unparalleled efficiency, productivity, and sustainability, driving us towards a future where we can feed the world's growing population while preserving our precious natural resources.

AI technologies can improve food system performance.

The U.N.'s Food and Agriculture Organization estimates that annual food losses that occur from farm to fork are as much as one-third of annual global food production, and emerging market agriculture sectors suffer from low total factor productivity growth.

AI applications in financial services, knowledge and capital, livestock solutions, irrigation & water tech, robotics and equipment, and precision agriculture can help improve the efficiency of agribusinesses, reduce fertilizer and pesticide use, enhance the accuracy of pest and disease detection, and facilitate the automated grading of crops.

Technological advancements have made it possible to serve farmers in rural and remote areas and extend financial products to underserved farmers. Machine learning platforms are being used to generate credit scores and price new products to help farmers upgrade their inputs to production.

Financial services providers can use new and existing data points to price new insurance contracts and trigger payouts for a predefined event such as below-average rainfall. Precision agriculture, alongside machine learning, has the potential to reduce agricultural insurance premiums by defining risks and improving risk assessment tools.

Machine learning algorithms help farmers manage production risk, increase the effective use of inputs, and optimize planting and harvest times to increase yields and farm-gate prices.

AI can help smallholder farmers integrate into regional and global supply chains by improving traceability, reducing trade friction, and facilitating the adoption of new technologies.

AI can also help improve the food and agricultural supply chain by reducing food waste, increasing profits for retailers, and improving customer-level quality of produce. AI systems can be implemented across delivery modes, including mobile applications, fixed cameras on the processing line, and cloud or edge computing.

Businesses are using AI to improve agricultural sustainability in several ways, including improving on-farm management of pests and diseases, allowing farmers to access more advanced machinery, and developing credit scores for farmers using machine learning and blockchain technology.

It is possible to use weather data and remote sensing data from satellites, and IoT data from tractors to help smallholder farmers improve their farming practices. High-resolution aerial images, temperature readings, humidity measurements, rainfall, soil samples, terrain type, equipment utilized, planting rates, applications, and other data points help build a complete and precise situational representation of every monitored field for the entire growing season.

Precision agriculture is dominant in higher-income economies like the United States, but there are new applications by agtech businesses in emerging markets with large-scale production systems. Precision farming is now being extended to on-farm robotics equipment that acts autonomously. 

This can increase farm income by reducing the labor required to pull weeds, minimizing chemical residue levels in the soil, and reducing health risks to operators.

There are technical difficulties in building reliable AI systems that can be applied across different terrains, and the seasonality of the agricultural sector increases the time needed for machine learning algorithms to learn and prove their value.

Agtech companies need to train smallholder farmers in the use of digital technology so they can use AI applications as they become more cost-effective.

Barriers to adoption exist beyond the technology itself, with challenges in data supply, data use, and a lack of enabling infrastructure. Additionally, farmers must be empowered to provide the data that the technology depends on.

Agtech is now at an inflection point with venture capital flows increasing to emerging markets in recent years. Agribusinesses are leveraging AI to create new cost-effective business models and to provide information to small farmers in emerging market economies. Agtech firms are using AI to demonstrate their viability and are looking to scale their innovations.

TO SUM UP:

Artificial Intelligence (AI) in Agribusiness is growing in emerging market economies, and can help improve the resilience of farming methods, reduce the cost of quality inputs and services to underserved farmers, and improve market access for smallholder farmers.

Agtech can help smallholder farmers use scarce resources more efficiently, access advanced inputs, and trace the origin and quality of their produce. 

Technological advancements have made it possible to serve farmers in rural and remote areas, extend financial products to underserved farmers, and reduce agricultural insurance premiums by defining risks and improving risk assessment tools.

AI can help smallholder farmers integrate into regional and global supply chains, reduce food waste, increase profits for retailers, and improve customer-level quality of produce using mobile applications, fixed cameras on the processing line, and cloud or edge computing.

Weather data, remote sensing data, IoT data, soil samples, terrain type, equipment utilized, planting rates, and applications can be used to improve farming practices.

Precision agriculture is being extended to on-farm robotics equipment that acts autonomously.

We cannot stress enough the importance for Agtech companies to train smallholder farmers to use digital technology and provide the data that AI applications depend on, as well as leverage artificial intelligence to create new cost-effective business models and provide information to small farmers in emerging market economies to improve their lives in the long term and a sustainable manner.