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AI transforms Canadian potato farming: A leap towards smart agriculture

In a groundbreaking development for the agricultural sector, Canadian potato growers are now turning to artificial intelligence (AI) to monitor and predict the nutritional needs of their crops in real time. This innovative approach is set to transform the way potatoes are cultivated in Canada, according to an article written by Reem Abukmeil and Ahmad Al-Mallahi which was published on The Conversation here.

“As the Canadian potato landscape continues to evolve, the delicate equilibrium between production ambitions and environmental protection remains at the forefront of industry considerations,” the researchers say.

The Challenge of Nutrient Management

Traditionally, potato growers have faced challenges in nutrient management, a crucial aspect of farming that directly impacts crop yields. The conventional methods of soil treatments and foliar feeding, while effective to some extent, have limitations, especially for nutrients required at later stages of potato growth.

“Prevailing industry practices often involve the concentrated application of fertilizers during the planting or hilling phases, particularly in Atlantic Canada. While this approach may be suitable for certain nutrients, it presents challenges for nutrients required at later stages of potato growth,” according to Abukmeil and Al-Mallahi.

The need for more precise and efficient nutrient application has become increasingly apparent, particularly in light of rising fuel and fertilizer costs.

AI: A Game Changer in Potato Farming

Enter the world of AI and machine learning. Researchers from Dalhousie University, including Ph.D. candidate Reem Abukmeil and Associate Professor Ahmad Al-Mallahi, are at the forefront of this agricultural revolution. Their research involves the use of a portable spectrophotometer, an optical sensor, to rapidly determine petiole nutrient values in potato fields.

Technological advances in optical sensors and their wavelength ranges has led to wide-ranging applications of spectroscopy to evaluate the nutritional composition of plants using machine learning techniques.

This technology, combined with machine learning algorithms trained on historical data, allows for near real-time assessment of the plant’s nutritional needs.

The Benefits of Real-Time Nutrient Monitoring

This AI-driven approach offers numerous advantages. It enables farmers to apply fertilizers more efficiently and timely, ensuring that the plants receive the right nutrients at the right time. This not only optimizes crop quality and yields but also helps in balancing production goals with environmental protection.

Looking Ahead: The Future of Potato Farming

As the Canadian potato landscape continues to evolve, this integration of AI technology marks a significant step forward. It promises not only to enhance the efficiency and sustainability of potato farming but also to set a precedent for other crops.

This new approach promises to be a valuable tool for farmers, enabling them to efficiently apply the necessary fertilizers in a timely manner, which will eventually balance between production ambitions and environmental protection.

Source: This news story is based on an article by Reem Abukmeil and Ahmad Al-Mallahi, published on The Conversation, titled “Potato growers can use AI to monitor and predict potato nutrition in real time”. Read the full, original article here.
Photo: Credit Catkin from Pixabay

Editor & Publisher: Lukie Pieterse

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