781–790 of 1001 posts
Not your regular "idea" but still interested in how it plays out.
https://onlineornot.com for the curious.
I am working on the sunflower plant density estimation problem. The goal is to be able to estimate the germination rate as early as possible. Farmers benefit from such information, because: - there are lots of expenses still to be made (fertilizer, pesticide, salaries), which may not be worth it if germination is under certain threshold - if detected early, there is still time to plant another grain or to fill up the…
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.…
I am working on the sunflower plant density estimation problem. The goal is to be able to estimate the germination rate as early as possible. Farmers benefit from such information, because: - there are lots of expenses still to be made (fertilizer, pesticide, salaries), which may not be worth it if germination is under certain threshold - if detected early, there is still time to plant another grain or to fill up the…
Feel like this basically enabling the use of ANOVA? (Compares yields across different treatments (e.g., irrigation methods, seed types).
I am working on the sunflower plant density estimation problem. The goal is to be able to estimate the germination rate as early as possible. Farmers benefit from such information, because: - there are lots of expenses still to be made (fertilizer, pesticide, salaries), which may not be worth it if germination is under certain threshold - if detected early, there is still time to plant another grain or to fill up the…
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…
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.
Built a backend and web version but now focusing on an Expo/React Native app (my first ever).
I am working on the sunflower plant density estimation problem. The goal is to be able to estimate the germination rate as early as possible. Farmers benefit from such information, because: - there are lots of expenses still to be made (fertilizer, pesticide, salaries), which may not be worth it if germination is under certain threshold - if detected early, there is still time to plant another grain or to fill up the…
Very cool, what type of parameters are within your control if detected early?
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).