How AI and Computer Vision Can Transform the International Grain Trading Market

The international grain trading market is a vital sector that connects the producers and consumers of grains, such as wheat, corn, rice, barley, and soybeans. According to the International Grains Council, the global trade volume of grains reached 427 million tonnes in 2020/21, accounting for about 19% of the world’s total grain production. The main exporters of grains are: 

  • United States, Russia, Brazil, Argentina, Ukraine, and Australia 

The main importers are: 

  • China, the European Union, Japan, Mexico, and Egypt. 

The grain trading market involves a complex process of sourcing, grading, pricing, storing, transporting, and delivering grains from the farm gate to the end-user. One of the key challenges in this process is to ensure the quality and quantity of the grains at every stage. This requires accurate and reliable methods of measuring and assessing the physical & chemical characteristics of the grains: 

  • moisture content,  
  • protein content  
  • weight testing 
  • presence of foreign material 
  • damage & defects.  

These factors affect the nutritional value, processing performance, and marketability of the grains. 

Traditionally grain quality assessment is done by manual sampling and laboratory testing which are time-consuming, labour-intensive, costly, and prone to human errors. Moreover these methods are not scalable and consistent across different locations and markets. As a result there is a lack of transparency and trust between the buyers and sellers of grains, leading to disputes, rejections, price discounts, and losses. 

To overcome these limitations there is a growing interest in applying artificial intelligence (AI) and computer vision (CV) to automate and improve the grain quality assessment process.  

CV is a subfield of AI that enables machines to perceive and interpret the visual world like humans. By combining AI and CV techniques with cameras for image acquisition, non-contact and scalable sensing solutions are made possible in grain trading. 

Some of the applications of AI and CV in grain trading include: 

  • Grain grading: AI and CV can be used to classify commercial grain samples based on dimensionless morphometric parameters (such as length, width, area, perimeter) and colour parameters (such as hue, saturation, value) extracted using CV algorithms from digital images. These parameters can be used to determine the grade of the grains according to the official standards or the specific requirements of the buyers.  
  • Grain quality prediction: AI and CV can also be used to predict the quality attributes of grains based on their appearance features extracted from images. A further study showed a CNN-based model can estimate the moisture content and protein content of wheat kernels from RGB images with an accuracy of 95.8% and 94.2% respectively. Another study developed a CNN-based model that can detect fungal infection in wheat kernels from hyperspectral images with an accuracy of 99.4%. These models can help reduce the need for laboratory testing and provide real-time feedback to the buyers and sellers. 
  • Grain yield estimation: AI and CV can also be used to monitor crop growth and estimate grain yield based on satellite or drone imagery. For example, additional research applied a CNN-based model to estimate wheat yield at field level from Sentinel-2 satellite images with an accuracy of 86%. Another study by  used a CNN-based model to estimate corn yield at county level from Landsat-8 satellite images with an accuracy of 91%.  

These models can help improve crop management practices and provide early warning signals for food security. Additionally the benefits of using AI and CV in grain trading are manifold. They can: 

  • Enhance efficiency: automate and speed up the grain quality assessment process by reducing manual labour and human intervention. 
  • Reduce costs: lower the operational costs by eliminating or minimizing the need for laboratory testing and equipment. 
  • Increase accuracy: improve the accuracy and reliability of grain quality assessment by using objective and consistent methods that can learn from data. 
  • Increase transparency: increase transparency and trust between buyers and sellers by providing verifiable and traceable information about grain quality. 
  • Reduce waste: reduce waste by minimizing rejections or discounts due to poor quality or misclassification. 
  • Improve sustainability: improve sustainability by optimising resource use and reducing environmental impacts. 

The outlook for the international grain trading market using AI and CV is promising. As the demand for grains continues to grow – driven by population growth, urbanisation, income growth, and dietary changes – the need for efficient and reliable grain quality assessment methods also increases. AI and CV can provide innovative solutions that can meet this need and transform the grain trading market. By leveraging the power of  these disruptive  technologies, the grain trading market can become more competitive, profitable, sustainable and ensure food security for all.