{'fraction_non_alphanumeric': 0.019771071800208116, 'fraction_numerical': 0.0, 'mean_word_length': 5.370860927152318, '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': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Multifactorial Evolutionary Prediction of Phenology and Pests: Can Machine Learning Help?

Agriculture is a key primary sector of economy. Developing and applying techniques that support a sustainable development of the fields and maximize their productivity, while guaranteeing the maximum levels of health and quality of the crops, is necessary. Precision agriculture refers to the use of technology to help in the decision-making process and can lead to the achievement of these goals. In this position paper, we argue that machine learning (ML) techniques can provide significant benefits to precision agriculture, but that there exist obstacles that are preventing their widespread adoption and effective application. Particularly, we focus on the prediction of phenology changes and pests, due to their important to ensure the quality of the crops. We analyze the state of the art, present the existing challenges, and outline our specific research goals.