Rapid yield prediction in paddy fields based on 2D image modelling of rice panicles

•The image processing method is effective for obtaining the area of rice grain.•The rice yield can be reliably predicted from panicle grain area.•“Five-point calibration model” could achieve rapid yield predictions in the field. Rice yield measurement in paddy fields is of great importance for regulating the balance of national grain supply and demand, but it is laborious and time consuming. In this paper, a fast yield measurement method based on rice panicle attributes and modelling is presented. To develop the method, first, the panicle attributes that are the main determinants of grain weight were identified through regression analysis. Second, the correlations between grain area and weight parameters were established and verified for different cultivars. Finally, a “five-point calibration model” was developed to rapidly estimate the weight parameters, and the estimated values and actual yields were compared. The results showed that the image processing method was capable of extracting the area of grain per panicle more accurately than other methods and that it was suitable for different cultivars: the lowest accurate extraction rate was 90.50%. All the coefficient of determination (R2) values of the weight prediction models of rice panicle grain area and the weight parameters were greater than 0.80, and the highest R2 value was 0.96. The R2 values of the “five-point calibration model” of grain area and grain weight were all greater than 0.99. When the predicted yields calculated by the “five-point calibration model” were compared with the actual yields, the relative errors ranged from 1.36% to 8.64%; i.e., they were all less than 10%. These results indicated that rice panicle 2D image modelling could enable rapid yield predictions in the field.