Kaggle – Visualizing biodiversity data

Identifying arthropods in real-time is a hard problem. First, arthropods have an extremely large number of species, even narrowing the search to the biodiversity within specific biomes. Furthermore, they have a huge variety of forms, so classifiying at a higher level, such as at order level, is still a problem. Look at the incredible variety of the order Coleoptera for example.

Yet there are applications, e.g. in agriculture, biodiversity calculations, collection scanning, in general any application that benefits from large scale automation, that require just that: real-time arthropod taxonomy.

So here is a first try, comparing several machine learning methods, and evaluating the results in terms of accuracy and speed.

There are important potential applications for a machine learning system than can classify plants. For example, current research on crop protection is using machine learning for precision weed and plant disease detection. The importance of moving away from traditional pesticide-based crop protection methods cannot be overstated, as demonstrated by the alarming rate at which flowering plants are evolving away from insect pollination.

I obtained a dataset of plant leaf images (Hussain, 2023) and compared three machine learning algorithms for classification: multilayer perceptron, random forest and support vector machine. The classifiers’ accuracy vary between 74% and 84% .

The Jupyter notebook with the full Python code can be accessed on Kaggle.

Category: Kaggle

Real-time arthropod taxonomy

Plant leaf classification