E-Farming Extended by AI
It is the continuation of our project E- Farming. If you have missed that, click on the previous sentence word “E-Farming” and read it. Our team Codezillas extended our project using Artificial Intelligence. This is my 7th article in Medium. Keep an eye on your plant 24/7!
If you ask greenhouse growers how they grow their best crop, you normally receive some combination of “we used last year’s crops as a standard”. It has different levels of success. But if we use controlled environments (greenhouses, indoor farms, etc.,) we can use data to help us standardize our cultivation, and therefore repeat and improve upon them.
This one reason Codezillas has created an AI powered Grow insight, organizing recorded environmental data into a useful digital crop cycle data. This comes as one part of our larger software platform, Codezillas “MAGIC”.
Doing it again and again Correctly
If you grew a very successful crop — i.e you enjoyed high yields, better tasting vegetables, better smell, etc., — how do you ensure that this happens the same way next time? Normally a grower is responsible for this. But if that grower changes his job, the operator might be in big trouble to find the right person to get the best practices for their vegetable, greens, or cannabis plants.
Keeping a record of crops allows greenhouse operators to have a detailed understanding of exactly what the environmental conditions were during their crop cycle and this is not the case for others.
Part of our solution, included in the MAGIC software platform, was the Grow Insight. Humidity, CO2, temperature, light spectrum levels, time-lapse video, as well as user-inputted notes and pictures all can be recorded, and divided into neat, organized “Reports,” or crop protocols, per crop cycle. This standardize the way we grow crops, and experience gives much easier and more effective interface.
Using Machine Learning and Artificial Intelligence
Accumulated data are analysed. Then, using artificial intelligence, we are able to incorporate services like yield predictions and crop modelling into MAGIC.
By increasing the amount of data , and using various machine learning algorithms for each greenhouse environment, we can improve the growth of crops in greenhouses.
Our Codezillas members are
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