The Bleeding Edge: Bots and Rots

programs funds through Cornell University. There are 13 different university researchers participating in activities such as grape genomics, AI services, field trials, breeding, extension and, of course, economics and communications. For this development with the robots, Yu Jiang of Cornell University provided expertise in AI and robotics.

How do robots work in the vineyard and how do you design a trial to see if they work? What did you measure?

Cadle-Davidson: Whether it’s a human driving a quad or a robot, there are two things required: you need them to navigate up and down rows and a camera to capture the images. For our trial designs, the breeding is different than a commercial trial. We look at everything from no disease to high disease. We designed two types of vineyards:

1. Breeding which combines diversity for disease susceptibility and resistance.

2. Fungicide trials which compare highly effective chemistries and biologicals versus an untreated vinifera (the control)

In both types of trials we have a severity range from 0 to 100 percent and this data helps us build “calibrations” for our AI.

What is a phytopatholobot and why are they used in the vineyard?

Cadle-Davidson: Hah! Maybe you can help us with naming that in the future. The phytopatholobot (PPB) is essentially a camera on wheels that goes up and down vineyard rows and takes pictures of grapevines. We use data analysis and AI to look at the pictures and quantify how much disease there is. This robot takes more pictures than a human would. We find that PPBs can spot disease as well as a human and can do so with a much higher coverage than a human expert. It’s the robot technology affixed with a specialized camera that makes it fully automated. There are a lot of high-tech components to this project.

Did you encounter any complications or difficulties?

Cadle-Davidson: The greatest challenge is that powdery mildew often grows under the leaf surface, where it is difficult for a camera to see. This is especially the case in places like California, where the UV light is intense.

Did you or your colleagues have any predictions about the conclusions?

Jiang: There have been many advancements in robotics since the early days of its deployment at General Motors decades ago. Initially, we focused on the imaging side. We needed to really see what was happening in the environment for vineyard management. Some of the things that we had to figure out during this process were:

1. Lighting. Just like good lighting is important to taking good pictures on your cell phone, it is especially important when collecting and processing imaging data. We were surprised at how large of a factor lighting is on imaging. I can make AI to do a lot of things but without a good, reproducible image you can’t do much. We had to figure out how to adjust to different light conditions. We added a strobe light to the camera to provide a constant source of illumination.

2. Differences in terrain. Researchers have maybe 30 years of experience using GPS, and with using robots in the field but we failed to navigate the robot in the field. The added dimension of the bumpy soil surface plus the loss of traction threw off some of our route calculations in the vineyard.

3. Hard obstacles. How does the robot know when a hard object is just a bump that requires a little more power to get over or a vine?

What conclusions can commercial growers and vintners use in their vineyards?

Cadle-Davidson: The primary goal of this research is to provide the grower with vines that are more resilient to downy and powdery mildew. These are some of the most troublesome diseases for grapes and many other crops. The primary use of this research is to provide disease resistant varieties to nurseries to propagate and sell to growers.

Yu Jiang: A secondary focus could perhaps lead to robots scouting for various diseases in commercial vineyards but a lot more work needs to be done to make it commercially available. The user experience would need to be simplified. Modifications such as simplified maintenance parts for easy servicing, etc. Before we went there.

What are the next steps with this research? How is a technology like this scaled?

Cadle-Davidson As mentioned, the greatest impact that we can have on the industry is through first focusing on the needs of grape breeders. A successful seedling can be rapidly scaled into production once it is sent out to the nurseries. Once achieved, these advances can profoundly impact a grower’s quantity and quality of yields. Powdery mildew is only one strain but there are many other pests and pathogens that we can help quantify down the road once we develop a successful platform.

A typical grape breeder will generate anywhere between five and 10,000 new seedlings every year. They are limited by the number of seeds that they can screen, and this is where AI really shines. We need to make decisions about whether to keep them or throw them away. Probably more than 99.9 percent of these seeds will be thrown away. AI helps speed up the assessment rate between the good seeds and the bad. WBM