AI is Helping Farmers Battle Wheat Disease

The wheat disease is an issue that farmers are constantly contending with. This is an issue caused by viruses, fungi, and bacteria. However, thanks to AI-powered object recognition technology, farmers can identify signs of wheat disease to take the necessary actions. In this article, we will take a closer look at why this issue is important for farmers and how AI can help farmers address this problem.

How Big of a Problem is Wheat Disease?

Wheat is one of the primary staple foods throughout the planet. Significant yield gains in wheat production over the past 40 years have resulted in a steady balance of supply versus demand. However, predicted global population growth rates and dietary changes mean that substantial yield gains over the next several decades will be needed to meet this escalating demand. 

A key component to meeting this challenge is better management of fungal-incited diseases, which can be responsible for 15%–20% yield losses per year. Prominent diseases of wheat that currently contribute to these losses include rusts, blotches, and head blight/scabs. Other recently emerged or relatively unnoticed diseases, such as wheat blast and spot blotch, respectively, also threaten grain production. This review seeks to provide an overview of the impact, distribution, and management strategies of these diseases. In addition, the biology of the pathogens and the molecular basis of their interaction with wheat are discussed.

What are Some Signs of Wheat Disease? 

The signs of wheat disease can vary depending on the type. The most common symptoms are yellow or red discoloration on the leaves, giving them a flame-like appearance. Other symptoms include dark brown or purple lesions on the heads. Lesions are often more intense at the top of the glume, with brown streaks or blotches extending down toward the base of the spikelet. The presence of tiny fungal reproductive structures embedded in the tissue can confirm the diagnosis but will require significant magnification. The disease may spread to the head from leaf infections initiated earlier in the growing season.

As you can imagine, farmers need to monitor their crops carefully to identify signs of wheat disease to make sure it does not get into the food supply or start affecting healthy plants. Having said this, it is very time-consuming to physically go around the field and identify possible signs of wheat disease. In addition to this, there may be subtle signs that are missed and can be problematic down the road. This is why AI technology that could identify wheat disease would be so useful to farmers. 

In the next section, we will take a look at how farmers can leverage AI to identify the wheat disease and the data annotation that is required to train the system to produce accurate results. 

How Can AI Help Identify Signs of Wheat Disease? 

An AI system trained with a large image dataset can learn to recognize specific wheat diseases with a high degree of accuracy, potentially paving the way for field-based crop disease identification. Basically, drones equipped with computer vision cameras would fly over the farmland and scan all of the crops for signs of wheat disease. This would allow farmers to have robust, rapid, accurate, and operational solutions and tools to take preventive actions before the wheat disease consumes even more crops. Using the latest technology available can allow farmers to increase crop yield to feed a growing population while reducing the environmental impact of food production at the same time.

What Types of Data Annotation are Necessary to Train AI to Spot Wheat Disease?

Since the signs of wheat disease can be very subtle, very detailed types of data annotation will be necessary, like semantic segmentation. This is a deep-learning algorithm that associates a label or category with every pixel in an image. It is used to recognize a collection of pixels that form distinct categories. In this case, this could be lesions on crops, discolorations, and other signs. In addition to this, instance segmentation would be necessary which involves detecting, segmenting, and classifying every individual object in an image. A good way of thinking about instance segmentation is a combination of semantic segmentation and object detection (detecting all instances of a category in an image) with the additional feature of demarcating separate instances of any particular segment class added to the vanilla segmentation task.

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