5 tips to save money and the world on pesticide application using Python code
Everybody is talking about technology and farming these days. But bringing agriculture and computers together is not only developing new apps and interfaces. First of all, it is building a new approach to old problems using computer science. Throughout 2017 I’ve been writing some posts (based on Jupyter Notebooks) on how Python could help us on this effort.
1. Calculate what your orchard really needs
It’s funny that I’ve started trying to learn OOP and I’ve ended thinking about pesticide spraying in a quite different way. The question is simple: if you don’t know two identical orchards, why do you spray them the same way? In this story we use Python code to build some classes that perform calculations, assuring that you deliver the right amount of water and pesticide on your trees.
2. Make some changes if you have a vineyard
In the first story of Codefarm, I’ve started to calculate what is the correct spray volume for a given orchard. In the second one, I made some calculations on vineyards. It was easier since I could reuse some classes, but I needed (as expected) some modifications. I’ve also addressed the problem of pesticide doses.
3. Keep attention to difference in trees
When you spray a fruit tree, you should do it from bottom to top. But should you do it the same way? I’ve addressed that question on this infographic. I didn’t forget that I have to use the same pressure, although there could be some small variations along the hose.
4. Statistics helps you to understand what spray is made of
Until now we have used Python to observe pesticide spraying from outside. In this story, I shared some ideas on what spray is made of: droplets. Most of the times we use hydraulic spraying, produced by different kinds of nozzles. After the liquid (a mixture of water and pesticide) is pressurized by a pump, there is a small place where it finds atmospheric pressure (the nozzle) and, as a result, it is pulverized in millions of small droplets. Python abilities to data analysis and visualization helped us to understand better this phenomena.
5. Use image analysis and a great tool to check if everything is ok
Water sensitive paper (WSP) was always recommended as a good tool to address pesticide spray coverage. Although is not as precise as magnesium oxide samples or fluorescence pigment quantification, it is so much easier to use, that it became a de facto standard in the industry. In this story, I’ve used Python image analysis and segmentation to count how many droplets did we get from the sprayer.
