Agriculture & Food

Use AI for increased quality and more efficient harvesting processes.

Agriculture & Food Solutions

The use of artificial intelligence holds enormous potential in today's agricultural and food industry. Conventional solutions in image processing often reach their limits due to the variable nature of crops, food or, livestock. At the same time, new developments in agriculture, such as autonomous harvesting machines, automated food processing or automated livestock farming, create the need for innovation and new technical solutions.

AI-based image processing makes it possible for our customers to reliably classify and count different crops, livestock and food by means of deep learning and to detect different error patterns. Data Spree helps agricultural and food companies to take the necessary steps into the future using AI-based image processing and innovative vision technology.

Sorting and Checking Harvested Goods and Food Items

The versatile nature and variability of harvested crops and foods poses a major challenge in the automation of agricultural and food processing operations. Image processing approaches, which are used for sorting or quality assurance of industrial goods or standard parts, often cannot meet the high requirements of today's agricultural and food industry. With AI-based image processing, however, even the most demanding tasks can be reliably solved with high variability. Food or harvested products can be classified and sorted according to different sizes, patterns or harvest classes. By detecting damaged areas or production errors during harvesting or food processing by AI, quality can be reliably and sustainably assured, even for large quantities.

Key Applications
  • Detect and classify crops under different weather and lighting conditions
  • Controlling food processing with a wide variety of variants
  • Quality assurance and defect classification
  • Fast and flexible production changeover

Autonomous Agricultural Machinery and Harvesting Processes

Autonomous vehicles are already a reality in the field of agricultural and harvesting machinery. AI-based image processing systems help autonomous systems to orientate themselves correctly, interpret events correctly and plan courses of action by machine learning. The challenge for AI is to detect possible unexpected obstacles at an early stage and to classify them correctly, to secure and, if necessary, to correct routes or to control the harvesting process. These tasks are highly complex and require a high degree of safety. Simple sensor technology or conventional image processing has no neuronal intelligence and no interpretation horizon and is therefore not able to realize innovative autonomous systems. AI-based image processing is therefore an elementary component for the agriculture of the future.

Key Applications
  • Intelligent route guidance
  • Flexible reactions to obstacles
  • Real-time detection and classification of crops or other objects
  • Security detection of employees
  • Counting of crops

Recognize and Count Livestock

Modern livestock farming requires efficient housing, fattening, and transport processes. With AI-based image processing, farm animals of different species can be reliably identified, classified according to characteristics and counted. This means that animals can be monitored in their housing and fattening and, for example, can always be classified and sorted according to type, size or weight. The intelligent detection or counting of animals also enables complete transparency of the stock in the holding and of the quantities of animals loaded or transported. Intelligent image processing thus opens up completely new possibilities for optimising procedures and processes in the keeping, breeding, and transport of farm animals. Artificial intelligence thus helps to secure your competitiveness and improve animal husbandry.

Key Applications
  • Intelligent mast control
  • Detection and classification of animals
  • Recording and counting of animals
  • Transparent loading and transport of animals
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