Abstract
Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in the last few decades as the direct and indirect methods available are laborious and time-consuming. The recent emergence of high-throughput plant phenotyping platforms has increased the need to develop new phenotyping tools for better decision-making by breeders. In this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue (RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The model mixes numerical data collected in a wheat breeding field and visual features extracted from the images to make rapid and accurate LAI estimations. Model-based LAI estimations were validated against LAI measurements determined non-destructively using an allometric relationship obtained in this study. The model performance was also compared with LAI estimates obtained by other classical indirect methods based on bottom-up hemispherical images and gaps fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The model performance was slightly better than that of the hemispherical image-based method, which tended to underestimate LAI. These results show the great potential of the developed model for near real-time LAI estimation, which can be further improved in the future by increasing the dataset used to train the model.
Highlights
The world’s population is expected to increase to about 9 billion people by 2050 [1]
leaf area index (LAI) values could still be increasing even when the crop canopy already covers approximately 70%–80% of the ground area. This means that LAI estimation using only data from image segmentation can be improved by adding other parameters that affects the growth and development of plants, as recently shown by Longson and Cambardella [46] who developed a statistical model to determine LAI from ground cover and plant height measurements. Taking all this into consideration, the aim of this paper was to investigate the suitability of using field-based nadir-view red green blue (RGB) images taken from a high-throughput phenotyping platform to obtain high-accuracy LAI estimates and provide reliable data for wheat breeding programs
Obtaining an allometric relationship to estimate the leaf area of individual wheat leaves in a non-destructive way is of great interest for the validation of other non-destructive methods in trials where the plant material cannot be sampled. This occurs in crop breeding trials where variation in the number of plants per subplot would alter the productivity results of the cultivars
Summary
The world’s population is expected to increase to about 9 billion people by 2050 [1] In this context, food production will need to increase by 70% despite the limited availability of arable lands, the increasing need for fresh water and the impact of climate change [2]. A large part of the work done by breeders consists of evaluating cultivars in the field by taking data manually to support decision-making [9]. This process is costly and time-consuming, since measurements are carried
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