![]() ![]() Tillers are important initial components related to yield as they have the potential to develop grain-bearing spikes. However, to achieve this requires complex growth models that link early or middle plant development to final yield. If, however, yield can be estimated at an early stage using early indicators alone, then the length of experiments can be reduced which would potentially accelerate breeding efforts it would certainly reduce the cost per trial. One aim of the phenotyping process is to understand plant development over time and its relevance to final yield. Other factors such as spike size, grain number per spike and grain weight feature at later stages. Of the critical factors contributing to crop yield, tiller number is established at the early stage while spike number features in the mid-life of plant development. The life span of cereal plants can be divided into four stages based on the Feekes scale: tillering, stem elongation, heading and ripening. 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. It thus represents an important tool for identifying high-yielding novel varieties. In this effort, crop phenotyping by quantitative assessment of crop canopy features plays an important role as a quantifier of crop performance. This translates into increased pressure on plant breeders to rapidly and accurately identify suitable wheat plant varieties that could be used for commercial production. However, with population growth, increasing demand and climate change threatening supply, greater effort is needed to ensure sustainable wheat crop production. Wheat is one of the three most important crop species worldwide with 700 million tonnes of grain produced annually. Our 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. The correlation between the final average grain yield and spike area is also discussed in this paper. 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 evaluation results showed an accuracy of over 80% in identification of spikes. The spike detection step was further improved by removing noise using an area and height threshold. We 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 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. The spike of a cereal plant is the grain-bearing organ whose physical characteristics are proxy measures of grain yield. ![]()
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