Tree / Crop Counting and Classification with Python and Scikit-Image - Tutorial
Lately there has been a wide source of spatial photogrametry available for agriculture. Available submetric images can be found in Google Earth, and drone images can increase the ortophoto resolution to the order of centimeters. Most of this data gives us a new perspective of the spatial distribution and stage of the crops, however a real spatial analysis of crop metrics have not been widely performed due to limitations on the images and software involved. Right now we live in a world of emerging technologies where new machine learning and artificial intelligence software is being launched every year at not cost. Even for profesional in the field of spatial analysis it is difficult to track the latest developments in software. Every time you search in Google, Twitter or other sources you will find something new and more important, something useful. We tried to make this tutorial with the popular library OpenCV, however the installation process was tedious in Windows. Then we shifted to Scikit-Image, a image processing library in Python that comes already installed on the Anaconda software. The process for image analysis required intermediate Python and GIS skills, but most important a strong criteria of the algorithm inputs, options and results. Input data Download the required data for the tutorial on this link: https://www.hatarilabs.com/ih-en/tree-counting-and-classification-with-python-and-scikit-image-tutorial
Lately there has been a wide source of spatial photogrametry available for agriculture. Available submetric images can be found in Google Earth, and drone images can increase the ortophoto resolution to the order of centimeters. Most of this data gives us a new perspective of the spatial distribution and stage of the crops, however a real spatial analysis of crop metrics have not been widely performed due to limitations on the images and software involved. Right now we live in a world of emerging technologies where new machine learning and artificial intelligence software is being launched every year at not cost. Even for profesional in the field of spatial analysis it is difficult to track the latest developments in software. Every time you search in Google, Twitter or other sources you will find something new and more important, something useful. We tried to make this tutorial with the popular library OpenCV, however the installation process was tedious in Windows. Then we shifted to Scikit-Image, a image processing library in Python that comes already installed on the Anaconda software. The process for image analysis required intermediate Python and GIS skills, but most important a strong criteria of the algorithm inputs, options and results. Input data Download the required data for the tutorial on this link: https://www.hatarilabs.com/ih-en/tree-counting-and-classification-with-python-and-scikit-image-tutorial