![]() presented a system in which, the growth and leaf area was calculated using a camera mounted above the plants. As the plant size increased, the plant architecture complexity also increased making accurate measurement more difficult. The measurements from this system were inaccurate as it was not able to handle occlusion efficiently, and struggled in the presence of shadows and reflections. During the growth monitoring period, different plant traits are measured, such as plant height and width. The system presented in uses images taken from two different views (side and top view) for growth monitoring. However, as the plant architecture gets more complex, 2D images cannot monitor the growth reliably. ![]() In the initial growth stage of the plant, 2D images can monitor the growth efficiently as the plant architecture is simple. However, since this system uses a single digital camera, it takes more time for data acquisition and processing. This system constantly rotates the positions of the pots within the greenhouse environment to reduce the effect of micro-environmental conditions which is the strength of this system. 2D systems for plant measurementĪ semi-automatic phenotyping system presented in uses 2D images to monitor plant rosette growth rate and expansion of size during its vegetative stages. Nonetheless, this cannot be guaranteed in practical set-ups, providing inaccurate leaf measurements. Precise leaf measurements can be achieved if the camera inspects a view perpendicular to the leaf. However, calculation of biomass of a plant using 2D images has limited accuracy because these techniques depend on the position of the camera relative to the plant (since whole plant is not visible from a single 2D camera). Systems often consist of a single camera mounted above the plant to produce a top view, occasionally combined with one or two more cameras to produce side views to calculate the leaf area or biomass of the plant. Image-based 2D methods can also help to extract plant traits. Some existing non-destructive phenotyping systems use 2D hyperspectral imaging, or stereo imaging methods for calculating structural parameters of the plant. Some 3D imaging techniques, such as laser triangulation produce geometrically precise 3D plant models which enable accurate extraction of plant features. These measurements can be performed using 2D or 3D imaging techniques. at the level of organs, plants, and canopies. Plant traits are measured at different scales, e.g. It is implemented by a fusion of several techniques, such as spectroscopy, non-destructive imaging, and high performance computing. Plant-phenotyping is an important area of research in plant breeding. Popular plant traits for growth monitoring include stem height, stem diameter, leaf area, leaf length, leaf width, number of leaves or fruits on the plant, and biomass. Plant-phenotyping is a set of protocols and techniques used to precisely calculate plant architecture, composition, and growth at different plant growth stages. Conventional phenotyping is manual, which is tedious, prone to errors, and labour intensive. Plant phenotyping provides vital information about crops which is helpful to farmers for their decision making process. One of the goals is to improve crop production and plant breeding efficiency to successfully meet the growing food demands of more than nine billion people by 2050. ![]() United Nations have included a goal in their 17 sustainable goals to promote sustainable agriculture to provide sufficient food for everyone with the aim to end hunger. In conclusion, this study demonstrated that the methods proposed to calculate plant traits can monitor plant growth in outdoor conditions.Īgriculture is one of the important factors that humanity relies on. The results demonstrate that the proposed system has potential to non-destructively monitor plant growth in outdoor conditions with high precision, when compared to the state-of-the-art systems. The accuracy of the proposed system is measured by comparing the values derived from the 3D plant model with manual measurements. Various plant traits such as number of leaves, stem height, leaf length, and leaf width were measured from the reconstructed and segmented 3D models at different plant growth stages. ![]() A method to measure leaf length and leaf width when the leaf is curled is also proposed. A non-destructive solution is proposed for growth monitoring in 3D using a single mobile phone camera based on a structure from motion algorithm. In this study, the growth of chilli plants ( Capsicum annum L.) was monitored in outdoor conditions. There is a demand for non-destructive systems in plant phenotyping which could precisely measure plant traits for growth monitoring.
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