Interesting, I'm also involved in a project to do yield prediction, but with a ground-vehicle with camera's on top to drive between strawberry and blueberry plants.

Yield prediction is huge indeed, because overshooting your prediction means seller stuff for a lower price. Undershooting means paying for someone's product to make up for the difference. Probably there's quite a bit of matchmaking in between those under and overshooters and someone making a good buck out of that too.

> Undershooting means paying for someone's product to make up for the difference

Indeed. Making up the difference can easily eat most of the farmer's profits. I guess it is even more pronounced for berries when compared to grains, because they cannot be stored for so long.

Hey, this is interesting. I used to work on a somewhat similar problem. Our problem was more general, but one usecase is to predict the number of interactions between flowers and pollinators, given some initial counts. As these initial counts are obtained manually (by going to the fields, taking pictures and count, like number of bees within a frame), those count numbers are likely to be lower the the actual numbers. We addressed this under-counted issue using low-rankness and Poisson mixture model. Take a look if you're interested: https://ieeexplore.ieee.org/document/10888717

Interesting. Thanks!

Feel like this basically enabling the use of ANOVA? (Compares yields across different treatments (e.g., irrigation methods, seed types).

It is possible. However, getting accurate yield data requires a "smart" harvester that can produce yield maps. Many modern harvesters are equipped with GPS and various sensors, so it is possible. However, farmers are really slow to replace old equipment if it works fine. I guess there are some retrofit solutions for yield mapping, but I haven't investigated their affordability and penetration into the (EU) farming landscape yet. Additionally, there are other interesting parameters apart from the harvested quantity that can be captured (e.g. the quality of the grain itself, such as size, composition, humidity etc).

Fisher invented ANOVA specifically for analyzing crop yields so it's a natural fit.

However for precision agriculture kavalg might want to consider other methods.

Very cool, what type of parameters are within your control if detected early?

I am not sure that I understand your question correctly, but given more precise sunflower density estimation, the farmer has three options:

1. Plow the field and seed again (same or different variety or grain). This is a very crude measure, but it is sometimes the right thing to do, because as I said most of the expenses have not been realized yet (fertilizer, pesticide, fuel, payroll, paying rent for the land). It is also a time critical decision, because the window of opportunity for plowing and reseeding is not very wide.

2. Accept the lower yield if it is within a reasonable margin (e.g. comparable to the expenses to plow and reseed).

3. Do partial reseeding over the existing plants (without plowing). This is an emerging strategy with the proliferation of smart seeders, but it requires a precise seeding map to be created beforehand (i.e. based on the density estimate). As an advantage, you spare the expenses for seeds and plowing, however there is some disadvantage as well, due to the different rate of development of the newly seeded plants. Farmers usually need plants to be ready for harvest at the same time, otherwise the quality of the grains suffers and hence the selling price is lower.

In addition to these points, having precise density information after germination helps with the identification of problems, such as seeder malfunction (e.g. nozzles getting clogged), seed quality and meteo data (e.g. too much rain, low temperatures etc).

Do sunflower farmers not use fertilizers, pesticides, or irrigation?

In the EU they use fertilizers and pesticides, but rarely use irrigation. However, pesticides are usually applied over the lifecycle of the plants. For fertilizer, there is some value to apply it on time, because it tends to migrate with water and applying now vs a month ago is not equivalent.