Artificial Intelligence for Digitalization in Agriculture: Considerations for the Development of a Fruit Detection System

The adoption of digital solutions is gradually diffusing also in the realm of agriculture, due to the valuable contributions that innovative technologies can bring to a distressed sector. Among these, the application of Artificial Intelligence based fruit detection systems is receiving increasing interest, given the reliance that many technological agricultural applications have on detection tasks to execute their functions, as well as the usefulness such solutions can have in improving several activities: once they track down fruits on a tree, they are able to provide for a quality analysis of the fruits, thus rendering information over maturity level or presence of diseases, for yield estimates ahead of time or for the implementation of intelligent robots able to automatically collect fruits or perform agrochemicals spraying. Nonetheless, the development of an AI based fruit detection system is a non-trivial process since it requires many accurate and pondered considerations over intricate technological aspects relating to data requirements, feature extraction, existing models, necessary hardware configurations, as well as over the socio-economic context. Through an analysis of these elements based on relevant literature, the present elaborate aims to provide therefore a comprehensive understanding of the broader implications that arise during the conception, design, and integration phases of AI technologies for fruit detection tasks, encouraging the necessity of an holistic perspective for informed decision-making processes that could actually result beneficial for agricultural practices.