The goal of this paper is establishing if we can satisfactorily perform a Thematic Analysis (TA) of semi-structured interviews using a Large Language Model (more precisely GPT3.5-Turbo). Building on previous work by the author, which established an embryonal process for conducting a TA with the model, this paper will perform a further analysis and then cover the last phase of a TA (phase 6), which entails the writing up of the result. This phase was not covered by the previous work. In particular, the focus will be on using the results of a TA done with the LLM on a dataset of user interviews, for writing user personas, with the model building on the TA to produce the personas narratives. User personas are models of real users, usually built from a data analysis like interviews with a sample of users. User personas are tools often used in User Centered Design processes. The paper shows that the model can build basic user personas with an acceptable quality deriving them from themes, and that the model can serve for the generation of ideas for user personas.

Agricultural drones, self-driving tractors and seed-planting robots are among the innovations that could be key to future food supplies, as autonomous farming promises to produce more crops with less effort and less impact on the environment. Global farming shortages are affecting food chains globally. Last year the National Farmers' Union (NFU) in the UK wrote to Prime Minister Boris Johnson asking for the implementation of a'Covid Recovery Visa' to alleviate labour shortages across the supply chain. Seasonal worker visa scheme has been extended until end of 2024. The extension of the scheme was a key lobbying ask by the NFU There will be 30,000 visas available this year with potential to increase by 10,000 if necessary Find out more https://t.co/gsBU8Nca6W

Today, advances in agronomy combined with smart agriculture technology have improved crop yields and sustainability. Enhancements in animal husbandry technology, improved breeding, nutrition and disease management help ensure optimal growth and performance of livestock. In spite of these innovations, the agricultural industry still faces significant challenges in producing enough food and getting it safely to market. These include changing weather patterns, water shortages, urbanization, population growth, complex environmental regulations, and dwindling available agricultural land, among others. In addition, food waste is a significant drain on the global food supply.

Ever since the 1950s Turing test, machines have been in constant competition to outsmart their human counterparts. Alan Turing, way ahead of his time, wondered if machines could think like humans -- a proposition that seemed unimaginable at the time. Now a part of our daily lives, artificial intelligence (AI) has transformed technology and the role of people in the workforce. AI's newest breakthrough is computer vision. This human-imitating technology actually appeared back in the 1970s in its earliest form, but is now transforming industries with its newfound capabilities.

From cultivation to improving harvesting quality, AI is known as one of the main elements for a surplus yield but that too for the ones who are capable enough to make use of it. Agriculture is seeing rapid adoption of Artificial Intelligence and Machine Learning, both in terms of agricultural products and in field farming techniques. Apart from that, most of the countries are looking forward to involving such techniques. In 2016, the estimated value added by the agricultural industry was estimated at just under 1% of the US GDP. The US Environmental Protection Agency, estimates that agriculture contributes roughly $330 billion in annual revenue to the economy, thus such techniques would definitely speed things up.

Agriculture is a very visual industry. At every stage of the food value chain, industry workers with varying education levels, experience, and age are performing thousands of tasks and making decisions- primarily based on visual inspection. The quality and accuracy of these actions and decisions can vary greatly and have real economic consequences. Industry innovators have responded with a "more is better" approach: add more sensors, add more automation, add more inputs like drones or satellite imagery. The result is the availability of more agricultural data than ever before, for growers who are less equipped to make sense of it all.

Adam Neilson, Chief Technology Officer at Wefarm discusses the ways in which machine learning can transform the African agricultural industry. Ever since Fritz Lang's Metropolis was first shown in the cinemas of 1927, the film industry has been forecasting how technology of the future would transform humanity. Fast forward to current day and we may not have flying cars or replica people mining in off planet worlds, but we do have something that I believe in the long run will be far more important to the future survival of our species. Over the last few years, machine learning (ML) has steadily rolled across the "hype cycle" from the "peak of inflated expectations" to officially entering the mainstream, and is now beginning to quietly revolutionise every aspect of our lives. For us consumers, it's now so deeply embedded within so many of the everyday products and services that we interact with it's almost invisible.

Agriculture is seeing accelerated adoption of Artificial Intelligence (AI) and Machine Learning (ML) the two with regard to agricultural products and in-field farming tactics. Cognitive computing, particularly, is set to turn into the most disruptive technology in agriculture services as it can certainly understand, learn, and answer various conditions (predicated on studying) to improve efficiency. Technology may likewise be used to recognize optimal sowing period, historical weather information, real time Moisture Adequacy info (MAI) from everyday rainfall and soil contamination to make predictability and supply inputs to farmers at ideal sowing time. To determine likely pest attacks, Microsoft in cooperation with United Phosphorus Limited is currently building a Pest danger Prediction API that ignites AI and machine understanding how to signify in progress, and the risk of pest attack. Predicated around harvest growth period and the weather illness, pest attacks are called to Moderate, High or lower.

For many, considering the effects artificial intelligence (AI) may soon have on society is a source of both anxiety and wonder. Agriculture, as much as any industry, is in line for big changes. Farm equipment may soon have a mind of its own. The term AI, as it relates to agriculture, is often lumped in with other emergent technologies like autonomous equipment and field sensors. But, AI-based equipment is distinct in that rather than being programed to perform a function, it's being designed to interpret data pulled from the field, act on it and teach itself best practices in the process.

The world's population is expected to hit 9 billion by 2050. Feeding everyone will be an issue (it already is). This is why it's crucial that farmers and the agricultural industry have access to the latest technology and reliable, affordable, high-speed Internet access. These sensors connect farmers to the crops in a more data driven way. Farmers can monitor soil conditions and control fertilizing and watering via smart devices.