{'fraction_non_alphanumeric': 0.018931710615280595, 'fraction_numerical': 0.0, 'mean_word_length': 5.607142857142857, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Analysis of Eucalyptus Regnans Form Characteristics

The huge surge in population over recent years has led to several budding challenges like unemployment, rapid urbanisation and ecological imbalance. The aforementioned issues can be addressed simply by promoting the growth of vegetation and commercial tree plantations in desolated lands leading to overall sustainable development and maintenance of ecological balance. Eucalyptus is one such plant which is easy to grow, can survive in alkaline or saline soils, can support rapid afforestation and has high commercial value. The present study focuses on understanding the relationship between various tree form parameters or characteristics of Eucalyptus Regnans using Machine Learning (ML) techniques such as Support Vector (SV), K-Nearest Neighbours (KNN) and Decision Tree (DT). In comparison to several methods that can be used for a typical data analysis problem, Machine Learning approaches are famed to produce reasonable conclusion by analysing a data pattern. In our current work the above-mentioned algorithms were employed to understand the relationship between various form factors of a tree such as branch form, crown form, diameter at breast height (DBH), and the height of the tree, stem form and vigour and provide suitable insights. The algorithms provide a mathematical approach to understand the dependencies between different tree forms enabling effective monitoring and sustainable growth in plantations.