Bluster or Lustre: Can AI Improve Crops and Plant Health?

Abstract

In a changing climate where future food security is a growing concern, researchers are exploring new methods and technologies in the effort to meet ambitious crop yield targets. The application of Artificial Intelligence (AI) including Machine Learning (ML) methods in this area has been proposed as a potential mechanism to support this. This review explores current research in the area to convey the state-of-the-art as to how AI/ML have been used to advance research, gain insights, and generally enable progress in this area. We address the question-Can AI improve crops and plant health? We further discriminate the bluster from the lustre by identifying the key challenges that AI has been shown to address, balanced with the potential issues with its usage, and the key requisites for its success. Overall, we hope to raise awareness and, as a result, promote usage, of AI related approaches where they can have appropriate impact to improve practices in agricultural and plant sciences.

Keywords: AI; crops; disruptive technologies; machine learning; omics; plant HEALTH.

Figure 1

Summarizing different types of Machine Learning (ML) analyses. Exemplary analyses are broken down into supervised and unsupervised learning examples and the decision making involved in choosing these approaches.

Figure 2

Flow chart of the processing pipeline from raw omic sequencing data to ML-analysis. Pipeline uses genomic DNA sequencing data as an exemplar to show a typical bioinformatic processing pipeline components, Quality Control (QC) steps to ensure high quality features are generated, a selection of potential output features (dependent on the analysis aim), feature selection techniques to reduce the dimensionality of the resultant feature sets and finally a range of possible AI/ML methods.