Abstract
Assessing crop production in the field often requires breeders to wait until the end of the season to collect yield-related measurements, limiting the pace of the breeding cycle. Early prediction of crop performance can reduce this constraint by allowing breeders more time to focus on the highest-performing varieties. Here, we present a multimodal deep learning model for predicting the performance of maize (Zea mays) at an early developmental stage, offering the potential to accelerate crop breeding. We employed multispectral images and eight vegetation indices, collected by an uncrewed aerial vehicle approximately 60 days after sowing, over three consecutive growing cycles (2017, 2018 and 2019). The multimodal deep learning approach was used to integrate field management and genotype information with the multispectral data, providing context to the conditions that the plants experienced during the trial. Model performance was assessed using holdout data, in which the model accurately predicted the yield (RMSE 1.07 t/ha, a relative RMSE of 7.60% of 16 t/ha, and R2 score 0.73) and identified the majority of high-yielding varieties, outperforming previously published models for early yield prediction. The inclusion of vegetation indices was important for model performance, with a normalized difference vegetation index and green with normalized difference vegetation index contributing the most to model performance. The model provides a decision support tool, identifying promising lines early in the field trial.
Highlights
Breeding crop varieties for improved performance in the field is costly and can encompass several years, as the individuals must be selected from large populations grown across multiple environments and years [1]
The objective of this study is to assess the effectiveness of using multimodal deep learning models for early yield prediction of maize hybrids grown under field trial conditions for three consecutive years, using genotype, field management and spectral data collected by UAV
The main goals of this study are: (1) to assess if a deep learning model based on tabular features can outperform established machine learning methods for yield prediction; (2) evaluate the impact of using multimodal deep learning for early yield prediction in comparison to unimodal models, based on single-data type; (3) measure the potential contribution of vegetation indices to model performance
Summary
Breeding crop varieties for improved performance in the field is costly and can encompass several years, as the individuals must be selected from large populations grown across multiple environments and years [1]. The prediction of end-of-season traits in the field can bypass the major time limitation imposed by the plant’s long life cycle, by allowing preliminary selection of the most promising individuals based on the plant phenotype at early developmental stages [2,3]. This would enable researchers to select a subset of the varieties to focus on, allowing the resources to be employed on collecting additional phenotyping measurements, sequencing the genome of selected individuals, or defining crossing plans for seed production [4].
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