Abstract
BackgroundThe spike of a cereal plant is the grain-bearing organ whose physical characteristics are proxy measures of grain yield. The ability to detect and characterise spikes from 2D images of cereal plants, such as wheat, therefore provides vital information on tiller number and yield potential.ResultsWe have developed a novel spike detection method for wheat plants involving, firstly, an improved colour index method for plant segmentation and, secondly, a neural network-based method using Laws texture energy for spike detection. The spike detection step was further improved by removing noise using an area and height threshold. The evaluation results showed an accuracy of over 80% in identification of spikes. In the proposed method we also measure the area of individual spikes as well as all spikes of individual plants under different experimental conditions. The correlation between the final average grain yield and spike area is also discussed in this paper.ConclusionsOur highly accurate yield trait phenotyping method for spike number counting and spike area estimation, is useful and reliable not only for grain yield estimation but also for detecting and quantifying subtle phenotypic variations arising from genetic or environmental differences.
Highlights
The spike of a cereal plant is the grain-bearing organ whose physical characteristics are proxy measures of grain yield
Spike identification on living plants We have argued that spike number is one key parameter contributing to the determination of yield of a cereal plant
To validate the approach we have taken, analyses were conducted using images of 194 single wheat plants grown per pot
Summary
The spike of a cereal plant is the grain-bearing organ whose physical characteristics are proxy measures of grain yield. With population growth, increasing demand and climate change threatening supply, greater effort is needed to ensure sustainable wheat crop production [2]. This translates into increased pressure on plant breeders to rapidly and accurately identify suitable wheat plant varieties that could be used for commercial production. In this effort, crop phenotyping by quantitative assessment of crop canopy features plays an important role as a quantifier of crop performance. One of the aims of digital crop phenotyping is to predict, non-destructively, the yield of a crop and preferably at an early stage in plant development
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