Towards the actual deployment of robust, adaptable, and maintainable AI models for sustainable agriculture

In the past two decades, computer vision and artficial intelligence (AI) have made significant strides in delivering practical solutions to aid farmers directly in the fields, thereby contributing to the integration of advanced technology in pre- cision agriculture. However, extending these methods to diverse crops and broader applications, including low-resource situations, raises several concerns. Indeed, the adaptability of AI methods to new cases and domains is not always straightforward. Moreover, the dynamic global panorama requires a continuous adaptation and refinement of artificial intelligence models. In this position paper, we examine the current opportunities and challenges, and propose a new approach to address these issues, currently in the implementation phase at CNR-ISTI.