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.
Highlights
Recent estimates predict that an increase of more than 60% in food will be needed by 2050 in order to feed the increasing global population [1]
The found that the DL models gave 0 to 5% higher prediction accuracy than regression best linear unbiased predictor (rrBLUP) model for all five traits with, multi-layer perceptron (MLP) producing a 5% higher prediction accuracy than convolutional neural networks (CNN) for grain yield and grain protein content
In this review we have explored the usage and potential advantages of Artificial Intelligence (AI) across a variety of applications relevant to the agricultural and plant sciences
Summary
Recent estimates predict that an increase of more than 60% in food will be needed by 2050 in order to feed the increasing global population [1]. More than ever, it is critical to provide plant/crop breeders with new tools that will allow them to develop and sustain the generation of crop varieties These tools can come from a range of technologies, including but not limited to; methods to identify alleles linked to favourable traits, genetic marker combinations via high-throughput genotyping or multi-omic sequencing, gene editing, speed breeding strategies, yield prediction, monitoring and management of pests/disease, fertilization schemes and real-time crop surveillance e.g., through imaging and remote sensing. These technologies are producing data at an unprecedented rate and turning plant science into a data intensive discipline.
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