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

Ignesti G., Moroni D., Martinelli M.

Trustworthy  Citizen science  Artificial Intelligence  Crowd-sensing  Sustainable Agriculture  AI  Agriculture  Industry 4.0  Deep Learning 

In the past two decades, computer vision and arti- ficial 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.

Source: ACSIS PUBLICATIONS, vol. 40, pp. 33-39. Belgrade, Serbia, 9-11/09/2024

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