In the evolution from Agriculture 2.0 to Agriculture 5.0, the agricultural sector has witnessed transformative changes. Agriculture 5.0 represents a revolutionary phase, integrating advanced technologies to enhance efficiency and sustainability adapted to individual field and livestock needs. The integration of robotics, extended reality, and 6G technologies marks a significant leap, enabling real-time monitoring and automation in farming practices. Artificial intelligence and big data are pivotal in Agriculture 5.0, offering insights for decision making and predictive analytics. Natural language processing plays a crucial role in facilitating efficient communication and data processing. The transition to Agriculture 5.0 should also consider societal challenges, in terms of technology lock-ins and the need for behavior shifts among stakeholders for faster adoption and customization of the solutions. This article presents a comprehensive description of Agriculture 5.0, underscoring the potential and hurdles in reshaping the future of farming.

Text is the prevalent medium for conveying research findings and developments within and beyond the domain of agriculture. Mining information from text is important for the (research) community to keep track of the most recent developments and identify solutions to major agriculture-related challenges. The task of Named Entity Recognition (NER) can be a first step in such a context. The work presented in this paper relates to a custom NER model for the automated identification and extraction of agricultural terms from text, built on Python’s spaCy library. The model has been trained on a manually annotated text corpus taken from the AGRIS database, and its performance depending on different model configurations is presented. We note that due to the domain ambiguity, inter-annotator agreement and model performance can be improved.

Advances in language modeling have provided affordances for term extraction based on the capture of the lexical context and its semantics and encoding this in real-valued vectors (embeddings). Term importance is usually computed using quantitative measures, ignoring the semantic context. Until now, there has been limited or no research on the effect of context on term importance using machine learning methods. In this paper, we investigate whether there is a relation between the importance of agricultural terms and the context of their occurrence as represented by text embeddings. Using a dataset of almost 33.7 K AGRIS abstracts containing 50 concepts randomly extracted from AGROVOC, we computed the correlation between the concept tf-idf scores and each of three semantic distances (cosine similarity of embeddings) used as a proxy to context: (i) the semantic distances of the various occurrences of the concept; (ii) the semantic distances between the concept and the abstracts that it appears in; (iii) the semantic distances of the abstracts in which the concept occurs. Embeddings were generated using Agriculture-BERT. We present a methodology and initial results from the computation of correlations. The novelty of our work is in the systematic investigation of the relation between term importance and semantic context.