![]() ![]() Thus, there is an urgent demand for an automated method for rapidly estimating the SNPP. Moreover, the SNPP is the most difficult to quantify manually, particularly when the SNPP is greater than 200. Manual low-throughput measurement methods for determining these traits are time consuming and the results are unreliable. The regional-scale yield estimation method comprises four yield components, which include the panicle number per plant, spikelet number per panicle (SNPP), filling rate and 1000 grains weight. Hence, the regional-scale yield estimation method is typically adopted and is widely approved for use in studies of high-yield breeding. By contrast, regional-scale rice yield estimation uses a traditional statistical sampling method, which is more flexible and has higher accuracy than the large-scale yield estimation method, particularly for measuring the panicle traits and estimating the yields of small plots. Unfortunately, the large-scale yield estimation method is not suitable for organizations other than research institutions and state departments because of the high costs. Large-scale yield estimation is a promising method because of the adoption of remote sensing and satellite technologies, which obtain timely and objective yield-related traits, such as LAI, NDVI, fPAR and NPP 1, 2 and therefore provide a prediction of the entire yield. Many methods for estimating rice yields have been proposed and are currently in use, including methods that use large-scale full coverage and regional-scale sampling surveys. Therefore, accurate yield estimates are extremely important for ensuring the safety of rice production and providing a continuous supply. Rice ( Oryza sativa) is a primary cereal crop that is consumed by more than half of the world’s population and rice is particularly important in China because of the extensive population. The proposed method uses available concepts and techniques for automated estimations of rice yield information. The estimation accuracy was consistent with the accuracy determined using manual measurements. The number of panicle samples that the error of the SNPP estimates was less than 10% was greater than 90% by the proposed method. Finally, a 5-point calibration method was adopted to improve the universality of the model. Second, the TLPB and area (the primary branch region) traits were rapidly extracted by developing image analysis algorithm. First, a linear relationship model between the total length of the primary branch (TLPB) and the SNPP was established based on the physiological characteristics of the panicle. To use a lower resolution and meet the accuracy requirement, we proposed an interdisciplinary method that integrated image analysis and a 5-point calibration model to rapidly estimate SNPP. However, it is difficult to simultaneously extract panicle branch and spikelet/grain information from images at the same resolution due to the different scales of these traits. The use of high-throughput quantitative image analysis methods for understanding the diversity of the panicle has increased rapidly. Spikelet number per panicle (SNPP) is one of the most important yield components used to estimate rice yields. ![]()
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