Abstract
Spikelet number per panicle (SNPP) is one of the most important yield components used to estimate rice yields. The use of high-throughput quantitative image analysis methods for understanding the diversity of the panicle has increased rapidly. 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. 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. 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. Second, the TLPB and area (the primary branch region) traits were rapidly extracted by developing image analysis algorithm. Finally, a 5-point calibration method was adopted to improve the universality of the model. The number of panicle samples that the error of the SNPP estimates was less than 10% was greater than 90% by the proposed method. The estimation accuracy was consistent with the accuracy determined using manual measurements. The proposed method uses available concepts and techniques for automated estimations of rice yield information.
Highlights
Spikelet number per panicle (SNPP) is one of the most important yield components used to estimate rice yields
The number of panicle samples that the error of the spikelet number per panicle (SNPP) estimates was less than 10% was greater than 90% in the use of area models
The proposed method of integrating the image-analysis and 5-point calibration model was effective in rapidly estimating the SNPP
Summary
Spikelet number per panicle (SNPP) is one of the most important yield components used to estimate rice yields. 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, for measuring the panicle traits and estimating the yields of small plots. With the rapid development of optical imaging techniques and computer technology, image analysis has become an effective method for the automated measurement of rice panicle traits, including analysis using machine-vision-based facilities[3,4,5,6,7] and 2-D image-based panicle phenotyping software[8,9,10] Among these image analysis methods, machine-vision-based facilities can measure traits efficiently but are very expensive and large, and these facilities are not available for field measurements in real time. Special 2-D image-based panicle phenotyping software (e.g., PASTAR/PASTA Viewer[8], P-TRAP9 and PANorama10) is cost-effective, this methodology is inefficient because each spikelet/grain on a panicle must be spread out manually before the panicle image is captured
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