Collaboration with AI in Horticultural Science

Abstract

Artificial Intelligence, or AI, is becoming increasingly prevalent in a wide variety of scientific fields. The recent progress in deep neural networks, or simply “deep learning”, in particular, has been remarkable, which is leading to the development of valuable technologies for various biological applications. Nevertheless, the application of these AI technologies in the field of horticultural science has not progressed. In the horticultural field, there is often a tendency to compare/compete with the accuracy (or ability) of AI and experts with long experience or existing systems, which may prevent the widespread adoption of AI technology in horticulture. The current evolving AI technologies go beyond mere prediction and diagnosis; through the application of “explainable AI” techniques, which can allow novel interpretations from a scientific perspective. It extends not only to conventional image analysis, but also to various data formats, including genetic sequences or any other numerical array data. Here, we introduce recent developments and evolution of AI technologies, mainly deep learning, in plant biology and horticultural science. Recent applications of convolutional neural networks (CNN) in image analyses allowed prediction/diagnosis of various invisible traits. Further combined application of explainable AI techniques and physiological assessments may spot features that potentially reveal the mechanisms of objective traits from a novel viewpoint. We also examined prospects for new applications of deep learning in horticultural science, such as for genetic factors or with new algorithms represented by Transformer.