{'fraction_non_alphanumeric': 0.014574898785425101, 'fraction_numerical': 0.0, 'mean_word_length': 5.5396825396825395, '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}}
Applications of neuro-fuzzy classification, evaluation and forecasting techniques in agriculture

Aim of the present article is to show the results obtained from the application of neuro-fuzzy methodology in the solution of agriculture problems like the Bactrocera Oleae (olive fly) infestation in the Liguria region olive grows. The research is focused to create an informatic decisional instrument to support experts in the applications of Integrated Pest Management strategies against the Bactrocera Oleae infestation. The system will suggest types of treatments for each monitored farm in order to optimize the quality of the olive oil and improve the economic and environmental impact of these treatments. Statistical and forecast analyses on data sets referred to agronomic case studies, like the growth of olive fly, are actually made using standard and model approaches like analytical; these dates instead present characteristics (big variability and non-linearity) which make them complex to be treat mathematically. Agronomic research needs to introduce new analysis techniques of taken dates and information, for example neuro-fuzzy methodologies that allow a large use of infestation dates with a good flexib ility degree.