Machine learning in agriculture: from silos to marketplaces

Abstract

The increasing global population combined with climate change present a major challenge for agriculture. Most crops have been bred to perform in specific environments, and with the time required to produce new varieties, it is unlikely that breeders will be able to adapt varieties to the changing climate (Abberton et al., 2016). There is an urgent need to develop new approaches to accelerate the production of high performing resilient crop varieties. Crop breeding has seen many changes in recent decades, from the application of molecular markers, through to genetically modified, and more recently, genome-edited crops (Scheben et al., 2017). However, these approaches are often limited by our lack of understanding of the genomic basis for complex traits, even with the deluge of data being generated by new genome sequencing and phenomics technologies. New approaches are required to translate this explosion of data into improved crop varieties. Deep learning represents a set of machine learning approaches that are not new, but have seen major advances in the last few years, with the adoption of deep learning in robotics, smart cars, smart homes, and agriculture. The breakthroughs in deep learning have not been driven by major advances in deep learning methods but rather by the increasing availability of large labelled training data as well as advances in computational hardware, especially graphics processors (GPUs). With the continued increase in agricultural phenotype and genotype data, there are opportunities to apply deep learning to accelerate crop breeding and agricultural productivity (Figure 1). Deep learning approaches are most advanced in the field of image recognition (Kamilaris and Prenafeta-Boldú, 2018); for example, Convolutional Neural Networks (CNNs, a class of deep neural network) have been developed to count wheat spikes and spikelets with 95.91% and 99.66% accuracy (Pound et al., 2017). For training, a novel dataset with 520 images of wheat plants was generated. The data were annotated by an expert who manually counted the 4100 ears and 48 000 spikelets, and whether the image contains an awned phenotype. In the field, images taken by aerial drones of lettuce fields were used to accurately count lettuce plants and predict yields at a fraction of the cost of manual counting (Bauer et al., 2019). This was a larger dataset than the wheat spikelet counting data: 60 subsections of lettuce-growing fields had their pictures taken from light aircraft. Each subsection contained between 300 and 1000 lettuce heads. For training purposes, each lettuce head was manually labelled with a red dot. This work therefore required the labelling of up to 60 000 lettuce heads. The complete training set, including labelled regions containing no lettuce heads, with 100 000 manually generated and curated data points. Another example is SeedGerm, a breeding platform that combines cost-effective custom hardware implementation with a graphical user interface that uses three different machine learning approaches to automatically phenotype commercial seeds in the growth chamber (Colmer et al., 2020). The performance of SeedGerm matches that of experts when asked to score radicle emergence. Outside of image recognition, there are few examples of the application of deep learning in crops. The prediction of phenotypes from genotypes is increasingly being applied in crop breeding but remains challenging due to the complexity of interactions in the data; however, this may be addressed using deep learning approaches. DeepGS is a CNN trained with data from 2403 Iranian bread wheat landraces from CIMMYT’s wheat gene bank (Ma et al., 2018). The data consisted of 33 709 DArT markers per individual, and each individual was assessed for 8 phenotypes of agronomic importance such as grain hardness and plant height. Trained using the DArT markers to predict these eight phenotypes, DeepGS led to improvements in prediction accuracy between 1% and 65% greater th