March 3, 2026Open Access

Artificial intelligence in plant science: from image-based phenotyping to yield and trait prediction

Key Points

Yield prediction improves using artificial intelligence, enabling more accurate agricultural practices.
High-throughput image-based phenotyping automates trait measurement across different farming environments.
Analysis combines satellite observations and UAV imaging to enhance trait monitoring precision.
This approach highlights the potential for sustainable agriculture, meriting further exploration in diverse agricultural contexts.

Abstract

With the development of artificial intelligence (AI) in complicated imaging and remote sensing technologies, plant research is transitioning from manual measurements to automated data collecting. High-throughput image-based phenotyping enables the precise and automated acquisition of traits across various spatial and temporal scales, ranging from controlled laboratory settings to intricate field. Furthermore, AI facilitates the combination of satellite observations, unmanned aerial vehicle (UAV) imaging, soil and climate data, and spatiotemporal information to enhance the precision of trait monitoring and yield prediction. These advances enhance the ability to evaluate and predict crop performance under variable environmental conditions. This paper offers a cross-disciplinary paradigm for accurate and sustainable modern agriculture by merging AI methodologies with plant phenotyping and yield forecasting.

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